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  1. 4 points
    Here is an interesting review: http://www.50northspatial.org/uav-image-processing-software-photogrammetry/ 😉😊
  2. 3 points
    Our objective is to provide the scientific and civil communities with a state-of-the-art global digital elevation model (DEM) derived from a combination of Shuttle Radar Topography Mission (SRTM) processing improvements, elevation control, void-filling and merging with data unavailable at the time of the original SRTM production: NASA SRTM DEMs created with processing improvements at full resolution NASA's Ice, Cloud,and land Elevation Satellite (ICESat)/Geoscience Laser Altimeter (GLAS) surface elevation measurements DEM cells derived from stereo optical methods using Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) data from the Terra satellite Global DEM (GDEM) ASTER products developed for NASA and the Ministry of Economy, Trade and Industry of Japan by Sensor Information Laboratory Corp National Elevation Data for US and Mexico produced by the USGS Global Multi-resolution Terrain Elevation Data 2010 (GMTED2010) developed by the USGS and the National Geospatial-Intelligence Agency (NGA) Canadian Digital Elevation Data produced by Natural Resources Canada We propose a significant modernization of the publicly- and freely-available DEM data. Accurate surface elevation information is a critical component in scientific research and commercial and military applications. The current SRTM DEM product is the most intensely downloaded dataset in NASA history. However, the original Memorandum of Understanding (MOU) between NASA and NGA has a number of restrictions and limitations; the original full resolution, one-arcsecond data are currently only available over the US and the error, backscatter and coherence layers were not released to the public. With the recent expiration of the MOU, we propose to reprocess the original SRTM raw radar data using improved algorithms and incorporating ancillary data that were unavailable during the original SRTM processing, and to produce and publicly release a void-free global one-arcsecond (~30m) DEM and error map, with the spacing supported by the full-resolution SRTM data. We will reprocess the entire SRTM dataset from raw sensor measurements with validated improvements to the original processing algorithms. We will incorporate GLAS data to remove artifacts at the optimal step in the SRTM processing chain. We will merge the improved SRTM strip DEMs, refined ASTER and GDEM V2 DEMs, and GLAS data using the SRTM mosaic software to create a seamless, void-filled NASADEM. In addition, we will provide several new data layers not publicly available from the original SRTM processing: interferometric coherence, radar backscatter, radar incidence angle to enable radiometric correction, and a radar backscatter image mosaic to be used as a layer for global classification of land cover and land use. This work leverages an FY12 $1M investment from NASA to make several improvements to the original algorithms. We validated our results with the original SRTM products and ancillary elevation information at a few study sites. Our approach will merge the reprocessed SRTM data with the DEM void-filling strategy developed during NASA's Making Earth System Data Records for Use in Research Environments (MEaSUREs) 2006 project, "The Definitive Merged Global Digital Topographic Data Set" of Co-Investigator Kobrick. NASADEM is a significant improvement over the available three-arcsecond SRTM DEM primarily because it will provide a global DEM and associated products at one-arcsecond spacing. ASTER GDEM is available at one-arcsecond spacing but has true spatial resolution generally inferior to SRTM one-arcsecond data and has much greater noise problems that are particularly severe in tropical (cloudy) areas. At one-arcsecond, NASADEM will be superior to GDEM across almost all SRTM coverage areas, but will integrate GDEM and other data to extend the coverage. Meanwhile, DEMs from the Deutsches Zentrum für Luft- und Raumfahrt Tandem-X mission are being developed as part of a public-private partnership. However, these data must be purchased and are not redistributable. NASADEM will be the finest resolution, global, freely-available DEM products for the foreseeable future. data page: https://lpdaac.usgs.gov/products/nasadem_hgtv001/ news links: https://earthdata.nasa.gov/esds/competitive-programs/measures/nasadem
  3. 3 points
    Interesting application of WebGIS to plot Dinosaur database, and you can search how is your place in the past on the interactive globe Map. Welcome to the internet's largest dinosaur database. Check out a random dinosaur, search for one below, or look at our interactive globe of ancient Earth! Whether you are a kid, student, or teacher, you'll find a rich set of dinosaur names, pictures, and facts here. This site is built with PaleoDB, a scientific database assembled by hundreds of paleontologists over the past two decades. check this interactive webgis apps: https://dinosaurpictures.org/ancient-earth#170 official link: https://dinosaurpictures.org/
  4. 3 points
    link: https://press.anu.edu.au/publications/new-releases
  5. 3 points
    Interesting video on How Tos: WebOpenDroneMap is a friendly Graphical User Interfase (GUI) of OpenDroneMap. It enhances the capabilities of OpenDroneMap by providing a easy tool for processing drone imagery with bottoms, process status bars, and a new way to store images. WebODM allows to work by projects, so the user can create different projects and process the related images. As a whole, WebODM in Windows is a implementation of PostgresSQL, Node, Django and OpenDroneMap and Docker. The software instalation requires 6gb of disk space plus Docker. It seem huge but it is the only way to process drone imagery in Windows using just open source software. We definitely see a huge potential of WebODM for the image processing, therefore we have done this tutorial for the installation and we will post more tutorial for the application of WebODM with drone images. For this tutorial you need Docker Toolbox installed on your computer. You can follow this tutorial to get Docker on your pc: https://www.hatarilabs.com/ih-en/tutorial-installing-docker You can visit the WebODM site on GitHub: https://github.com/OpenDroneMap/WebODM Videos The tutorial was split in three short videos. Part 1 https://www.youtube.com/watch?v=AsMSoWAToxE Part 2 https://www.youtube.com/watch?v=8GKx3fz0qgE Part 3 https://www.youtube.com/watch?v=eCZFzaXyMmA
  6. 3 points
    7th International Conference on Computer Science and Information Technology (CoSIT 2020) January 25 ~ 26, 2020, Zurich, Switzerland https://cosit2020.org/ Scope & Topics 7th International Conference on Computer Science and Information Technology (CoSIT 2020) will provide an excellent international forum for sharing knowledge and results in theory, methodology and applications of Computer Science, Engineering and Information Technology. The Conference looks for significant contributions to all major fields of the Computer Science and Information Technology in theoretical and practical aspects. The aim of the conference is to provide a platform to the researchers and practitioners from both academia as well as industry to meet and share cutting-edge development in the field. Authors are solicited to contribute to the conference by submitting articles that illustrate research results, projects, surveying works and industrial experiences that describe · Geographical Information Systems/ Global Navigation Satellite Systems (GIS/GNSS) Paper Submission Authors are invited to submit papers through the conference Submission system. Here’s where you can reach us : [email protected] or [email protected]
  7. 3 points
    The first thing to do before mapping is to set up the camera parameters. Before to set up camera parameters, recommended resetting the all parameters on camera first. To set camera parameters manually need to set to manual mode. Image quality: Extra fine Shutter speed: to remove blur from photo shutter speed should be set for higher value. 1200–1600 is recommended. Higher the shutter speed reduce image quality . if there is blur in the image increase shutter speed ISO: lower the ISO higher image quality. ISO between 160–300 is recommended. if there is no blur but image quality is low, reduce ISO. Focus: Recommended to set the focus manually on the ground before a flight. Direct camera to an object which is far, and slightly increase the focus, you will see on camera screen that image sharpness changes by changing the value. Set the image sharpness at highest. (slide the slider close to infinity point on the screen you will see the how image sharpness changes by sliding) White balance: recommended to set to auto. On surveying mission Sidelap, Overlap, Buffer have to be set higher to get better quality surveying result. First set the RESOLUTION which you would like to get for your surveying project. When you change resolution it changes flight altitude and also effects the coverage in a single flight. Overlap: 70% This will increase the number of photos taken during each flight line. The camera should be capable to capture faster. Sidelap: recommended 70% Flying with higher side-lap between each line of the flight is a way to get more matches in the imagery, but it also reduces the coverage in a single flight Buffer: 12% Buffer increases the flight plane to get more images from borders. It will improve the quality of the map source: https://dronee.aero/blogs/dronee-pilot-blog/few-things-to-set-correctly-to-get-high-quality-surveying-results
  8. 3 points
    The GeoforGood Summit 2019 drew its curtains close on 19 Sep 2019 and as a first time attendee, I was amazed to see the number of new developments announced at the summit. The summit — being a first of its kind — combined the user summit and the developers summit into one to let users benefit from the knowledge of new tools and developers understand the needs of the user. Since my primary focus was on large scale geospatial modeling, I attended the workshops and breakout sessions related to Google Earth Engine only. With that, let’s look at 3 new exciting developments to hit Earth Engine Updated documentation on machine learning Documentation really? Yes! As an amateur Earth Engine user myself, my number one complaint of the tool has been its abysmal quality of documentation spread between its app developers site, Google Earth’s blog, and their stack exchange answers. So any updates to the documentation is welcome. I am glad that the documentation has been updated to help the ever-exploding user base of geospatial data scientists interested in implementing machine learning and deep learning models. The documentation comes with its own example Colab notebooks. The Example notebooks include supervised classification, unsupervised classification, dense neural network, convolutional neural network, and deeplearning on Google Cloud. I found that these notebooks were incredibly useful to me to get started as there are quite a few non-trivial data type conversions ( int to float32 and so on) in the process flow. Earth Engine and AI Platform Integration Nick Clinton and Chris Brown jointly announced the much overdue Earth Engine + Google AI Platform integration. Until now, users were essentially limited to running small jobs on Google Colab’s virtual machine (VM) and hoping that the connection with the VM doesn’t time out (which usually lasts for about 4 hours). Other limitations include lack of any task monitoring or queuing capabilities. Not anymore! The new ee.Model() package let’s users communicate with a Google Cloud server that they can spin up based on their own needs. Needless to say, this is a HUGE improvement over the previous primitive deep learning support provided on the VM. Although it was free, one could simply not train, validate, predict, and deploy any model larger than a few layers. It had to be done separately on the Google AI Platform once the .TFRecord objects were created in their Google bucket. With this cloud integration, that task has been simplified tremendously by letting users run and test their models right from the Colab environment. The ee.Model() class comes with some useful functions such as ee.Model.fromAIPlatformPredictor() to make predictions on Earth Engine data directly from your model sitting on Google Cloud. Lastly, since your model now sits in the AI Platform, you can cheat and use your own models trained offline to predict on Earth Engine data and make maps of its output. Note that your model must be saved using tf.contrib.saved_model format if you wish to do so. The popular Keras function model.save_model('model.h5') is not compatible with ee.Model(). Moving forward, it seems like the team plans to stick to the Colab Python IDE for all deep learning applications. However, it’s not a death blow for the loved javascript code editor. At the summit, I saw that participants still preferred the javascript code editor for their non-neural based machine learning work (like support vector machines, random forests etc.). Being a python lover myself, I too go to the code editor for quick visualizations and for Earth Engine Apps! I did not get to try out the new ee.Model() package at the summit but Nick Clinton demonstrated a notebook where a simple working example has been hosted to help us learn the function calls. Some kinks still remain in the development— like limiting a convolution kernel to only 144 pixels wide during prediction because of “the way earth engine communicates with cloud platform” — but he assured us that it will be fixed soon. Overall, I am excited about the integration because Earth Engine is now a real alternative for my geospatial computing work. And with the Earth Engine team promising more new functions in the ee.Model() class, I wonder if companies and labs around the world will start migrating their modeling work to Earth Engine. Cooler Visualizations! Matt Hancher and Tyler Erickson displayed some new functionality related to visualizations and I found that it made it vastly simpler to make animated visuals. With ee.ImageCollection.getVideoThumbURL() function, you can create your own animated gifs within a few seconds! I tried it on a bunch of datasets and the speed of creating the gifs was truly impressive. Say bye to exporting each iteration of a video to your drive because these gifs appear right at the console using the print() command! Shown above is an example of global temperature forecast by time from the ‘NOAA/GFS0P25’ dataset. The code for making the gif can be found here. The animation is based on the example shown in the original blog post by Michael DeWitt and I referred to this gif-making tutorial on the developers page to make it. I did not get to cover all the new features and functionality introduced at the summit. For that, be on the lookout for event highlights on Google Earth’s blog. Meanwhile, you can check out the session resources from the summit for presentations and notebooks on topics that you are interested in. Presentation and resources Published in Medium
  9. 3 points
    found this interesting tutorial : For the last couple years I have been testing out the ever-improving support for parallel query processing in PostgreSQL, particularly in conjunction with the PostGIS spatial extension. Spatial queries tend to be CPU-bound, so applying parallel processing is frequently a big win for us. Initially, the results were pretty bad. With PostgreSQL 10, it was possible to force some parallel queries by jimmying with global cost parameters, but nothing would execute in parallel out of the box. With PostgreSQL 11, we got support for parallel aggregates, and those tended to parallelize in PostGIS right out of the box. However, parallel scans still required some manual alterations to PostGIS function costs, and parallel joins were basically impossible to force no matter what knobs you turned. With PostgreSQL 12 and PostGIS 3, all that has changed. All standard query types now readily parallelize using our default costings. That means parallel execution of: Parallel sequence scans, Parallel aggregates, and Parallel joins!! TL;DR: PostgreSQL 12 and PostGIS 3 have finally cracked the parallel spatial query execution problem, and all major queries execute in parallel without extraordinary interventions. What Changed With PostgreSQL 11, most parallelization worked, but only at much higher function costs than we could apply to PostGIS functions. With higher PostGIS function costs, other parts of PostGIS stopped working, so we were stuck in a Catch-22: improve costing and break common queries, or leave things working with non-parallel behaviour. For PostgreSQL 12, the core team (in particular Tom Lane) provided us with a sophisticated new way to add spatial index functionality to our key functions. With that improvement in place, we were able to globally increase our function costs without breaking existing queries. That in turn has signalled the parallel query planning algorithms in PostgreSQL to parallelize spatial queries more aggressively. Setup In order to run these tests yourself, you will need: PostgreSQL 12 PostGIS 3.0 You’ll also need a multi-core computer to see actual performance changes. I used a 4-core desktop for my tests, so I could expect 4x improvements at best. The setup instructions show where to download the Canadian polling division data used for the testing: pd a table of ~70K polygons pts a table of ~70K points pts_10 a table of ~700K points pts_100 a table of ~7M points We will work with the default configuration parameters and just mess with the max_parallel_workers_per_gather at run-time to turn parallelism on and off for comparison purposes. When max_parallel_workers_per_gather is set to 0, parallel plans are not an option. max_parallel_workers_per_gather sets the maximum number of workers that can be started by a single Gather or Gather Merge node. Setting this value to 0 disables parallel query execution. Default 2. Before running tests, make sure you have a handle on what your parameters are set to: I frequently found I accidentally tested with max_parallel_workers set to 1, which will result in two processes working: the leader process (which does real work when it is not coordinating) and one worker. show max_worker_processes; show max_parallel_workers; show max_parallel_workers_per_gather; Aggregates Behaviour for aggregate queries is still good, as seen in PostgreSQL 11 last year. SET max_parallel_workers = 8; SET max_parallel_workers_per_gather = 4; EXPLAIN ANALYZE SELECT Sum(ST_Area(geom)) FROM pd; Boom! We get a 3-worker parallel plan and execution about 3x faster than the sequential plan. Scans The simplest spatial parallel scan adds a spatial function to the target list or filter clause. SET max_parallel_workers = 8; SET max_parallel_workers_per_gather = 4; EXPLAIN ANALYZE SELECT ST_Area(geom) FROM pd; Boom! We get a 3-worker parallel plan and execution about 3x faster than the sequential plan. This query did not work out-of-the-box with PostgreSQL 11. Gather (cost=1000.00..27361.20 rows=69534 width=8) Workers Planned: 3 -> Parallel Seq Scan on pd (cost=0.00..19407.80 rows=22430 width=8) Joins Starting with a simple join of all the polygons to the 100 points-per-polygon table, we get: SET max_parallel_workers_per_gather = 4; EXPLAIN SELECT * FROM pd JOIN pts_100 pts ON ST_Intersects(pd.geom, pts.geom); Right out of the box, we get a parallel plan! No amount of begging and pleading would get a parallel plan in PostgreSQL 11 Gather (cost=1000.28..837378459.28 rows=5322553884 width=2579) Workers Planned: 4 -> Nested Loop (cost=0.28..305122070.88 rows=1330638471 width=2579) -> Parallel Seq Scan on pts_100 pts (cost=0.00..75328.50 rows=1738350 width=40) -> Index Scan using pd_geom_idx on pd (cost=0.28..175.41 rows=7 width=2539) Index Cond: (geom && pts.geom) Filter: st_intersects(geom, pts.geom) The only quirk in this plan is that the nested loop join is being driven by the pts_100 table, which has 10 times the number of records as the pd table. The plan for a query against the pt_10 table also returns a parallel plan, but with pd as the driving table. EXPLAIN SELECT * FROM pd JOIN pts_10 pts ON ST_Intersects(pd.geom, pts.geom); Right out of the box, we still get a parallel plan! No amount of begging and pleading would get a parallel plan in PostgreSQL 11 Gather (cost=1000.28..85251180.90 rows=459202963 width=2579) Workers Planned: 3 -> Nested Loop (cost=0.29..39329884.60 rows=148129988 width=2579) -> Parallel Seq Scan on pd (cost=0.00..13800.30 rows=22430 width=2539) -> Index Scan using pts_10_gix on pts_10 pts (cost=0.29..1752.13 rows=70 width=40) Index Cond: (geom && pd.geom) Filter: st_intersects(pd.geom, geom) source: http://blog.cleverelephant.ca/2019/05/parallel-postgis-4.html
  10. 3 points
    Hello everyone ! This is a quick Python code which I wrote to batch download and preprocess Sentinel-1 images of a given time. Sentinel images have very good resolution and makes it obvious that they are huge in size. Since I didn’t want to waste all day preparing them for my research, I decided to write this code which runs all night and gives a nice image-set in following morning. import os import datetime import gc import glob import snappy from sentinelsat import SentinelAPI, geojson_to_wkt, read_geojson from snappy import ProductIO class sentinel1_download_preprocess(): def __init__(self, input_dir, date_1, date_2, query_style, footprint, lat=24.84, lon=90.43, download=False): self.input_dir = input_dir self.date_start = datetime.datetime.strptime(date_1, "%d%b%Y") self.date_end = datetime.datetime.strptime(date_2, "%d%b%Y") self.query_style = query_style self.footprint = geojson_to_wkt(read_geojson(footprint)) self.lat = lat self.lon = lon self.download = download # configurations self.api = SentinelAPI('scihub_username', 'scihub_passwd', 'https://scihub.copernicus.eu/dhus') self.producttype = 'GRD' # SLC, GRD, OCN self.orbitdirection = 'ASCENDING' # ASCENDING, DESCENDING self.sensoroperationalmode = 'IW' # SM, IW, EW, WV def sentinel1_download(self): global download_candidate if self.query_style == 'coordinate': download_candidate = self.api.query('POINT({0} {1})'.format(self.lon, self.lat), date=(self.date_start, self.date_end), producttype=self.producttype, orbitdirection=self.orbitdirection, sensoroperationalmode=self.sensoroperationalmode) elif self.query_style == 'footprint': download_candidate = self.api.query(self.footprint, date=(self.date_start, self.date_end), producttype=self.producttype, orbitdirection=self.orbitdirection, sensoroperationalmode=self.sensoroperationalmode) else: print("Define query attribute") title_found_sum = 0 for key, value in download_candidate.items(): for k, v in value.items(): if k == 'title': title_info = v title_found_sum += 1 elif k == 'size': print("title: " + title_info + " | " + v) print("Total found " + str(title_found_sum) + " title of " + str(self.api.get_products_size(download_candidate)) + " GB") os.chdir(self.input_dir) if self.download: if glob.glob(input_dir + "*.zip") not in [value for value in download_candidate.items()]: self.api.download_all(download_candidate) print("Nothing to download") else: print("Escaping download") # proceed processing after download is complete self.sentinel1_preprocess() def sentinel1_preprocess(self): # Get snappy Operators snappy.GPF.getDefaultInstance().getOperatorSpiRegistry().loadOperatorSpis() # HashMap Key-Value pairs HashMap = snappy.jpy.get_type('java.util.HashMap') for folder in glob.glob(self.input_dir + "\*"): gc.enable() if folder.endswith(".zip"): timestamp = folder.split("_")[5] sentinel_image = ProductIO.readProduct(folder) if self.date_start <= datetime.datetime.strptime(timestamp[:8], "%Y%m%d") <= self.date_end: # add orbit file self.sentinel1_preprocess_orbit_file(timestamp, sentinel_image, HashMap) # remove border noise self.sentinel1_preprocess_border_noise(timestamp, HashMap) # remove thermal noise self.sentinel1_preprocess_thermal_noise_removal(timestamp, HashMap) # calibrate image to output to Sigma and dB self.sentinel1_preprocess_calibration(timestamp, HashMap) # TOPSAR Deburst for SLC images if self.producttype == 'SLC': self.sentinel1_preprocess_topsar_deburst_SLC(timestamp, HashMap) # multilook self.sentinel1_preprocess_multilook(timestamp, HashMap) # subset using a WKT of the study area self.sentinel1_preprocess_subset(timestamp, HashMap) # finally terrain correction, can use local data but went for the default self.sentinel1_preprocess_terrain_correction(timestamp, HashMap) # break # try this if you want to check the result one by one def sentinel1_preprocess_orbit_file(self, timestamp, sentinel_image, HashMap): start_time_processing = datetime.datetime.now() orb = self.input_dir + "\\orb_" + timestamp if not os.path.isfile(orb + ".dim"): parameters = HashMap() orbit_param = snappy.GPF.createProduct("Apply-Orbit-File", parameters, sentinel_image) ProductIO.writeProduct(orbit_param, orb, 'BEAM-DIMAP') # BEAM-DIMAP, GeoTIFF-BigTiff print("orbit file added: " + orb + " | took: " + str(datetime.datetime.now() - start_time_processing).split('.', 2)[0]) else: print("file exists - " + orb) def sentinel1_preprocess_border_noise(self, timestamp, HashMap): start_time_processing = datetime.datetime.now() border = self.input_dir + "\\bordr_" + timestamp if not os.path.isfile(border + ".dim"): parameters = HashMap() border_param = snappy.GPF.createProduct("Remove-GRD-Border-Noise", parameters, ProductIO.readProduct(self.input_dir + "\\orb_" + timestamp + ".dim")) ProductIO.writeProduct(border_param, border, 'BEAM-DIMAP') print("border noise removed: " + border + " | took: " + str(datetime.datetime.now() - start_time_processing).split('.', 2)[0]) else: print("file exists - " + border) def sentinel1_preprocess_thermal_noise_removal(self, timestamp, HashMap): start_time_processing = datetime.datetime.now() thrm = self.input_dir + "\\thrm_" + timestamp if not os.path.isfile(thrm + ".dim"): parameters = HashMap() thrm_param = snappy.GPF.createProduct("ThermalNoiseRemoval", parameters, ProductIO.readProduct(self.input_dir + "\\bordr_" + timestamp + ".dim")) ProductIO.writeProduct(thrm_param, thrm, 'BEAM-DIMAP') print("thermal noise removed: " + thrm + " | took: " + str(datetime.datetime.now() - start_time_processing).split('.', 2)[0]) else: print("file exists - " + thrm) def sentinel1_preprocess_calibration(self, timestamp, HashMap): start_time_processing = datetime.datetime.now() calib = self.input_dir + "\\calib_" + timestamp if not os.path.isfile(calib + ".dim"): parameters = HashMap() parameters.put('outputSigmaBand', True) parameters.put('outputImageScaleInDb', False) calib_param = snappy.GPF.createProduct("Calibration", parameters, ProductIO.readProduct(self.input_dir + "\\thrm_" + timestamp + ".dim")) ProductIO.writeProduct(calib_param, calib, 'BEAM-DIMAP') print("calibration complete: " + calib + " | took: " + str(datetime.datetime.now() - start_time_processing).split('.', 2)[0]) else: print("file exists - " + calib) def sentinel1_preprocess_topsar_deburst_SLC(self, timestamp, HashMap): start_time_processing = datetime.datetime.now() deburst = self.input_dir + "\\dburs_" + timestamp if not os.path.isfile(deburst): parameters = HashMap() parameters.put('outputSigmaBand', True) parameters.put('outputImageScaleInDb', False) deburst_param = snappy.GPF.createProduct("TOPSAR-Deburst", parameters, ProductIO.readProduct(self.input_dir + "\\calib_" + timestamp + ".dim")) ProductIO.writeProduct(deburst_param, deburst, 'BEAM-DIMAP') print("deburst complete: " + deburst + " | took: " + str(datetime.datetime.now() - start_time_processing).split('.', 2)[0]) else: print("file exists - " + deburst) def sentinel1_preprocess_multilook(self, timestamp, HashMap): start_time_processing = datetime.datetime.now() multi = self.input_dir + "\\multi_" + timestamp if not os.path.isfile(multi + ".dim"): parameters = HashMap() parameters.put('outputSigmaBand', True) parameters.put('outputImageScaleInDb', False) multi_param = snappy.GPF.createProduct("Multilook", parameters, ProductIO.readProduct(self.input_dir + "\\calib_" + timestamp + ".dim")) ProductIO.writeProduct(multi_param, multi, 'BEAM-DIMAP') print("multilook complete: " + multi + " | took: " + str(datetime.datetime.now() - start_time_processing).split('.', 2)[0]) else: print("file exists - " + multi) def sentinel1_preprocess_subset(self, timestamp, HashMap): start_time_processing = datetime.datetime.now() subset = self.input_dir + "\\subset_" + timestamp if not os.path.isfile(subset + ".dim"): WKTReader = snappy.jpy.get_type('com.vividsolutions.jts.io.WKTReader') # converting shapefile to GEOJSON and WKT is easy with any free online tool wkt = "POLYGON((92.330290184197 20.5906091141114,89.1246637610338 21.6316051481971," \ "89.0330319081811 21.7802436586492,88.0086282580443 24.6678836192818,88.0857830091018 " \ "25.9156771178278,88.1771488779853 26.1480664053835,88.3759125970998 26.5942658997298," \ "88.3876586919721 26.6120432770312,88.4105534167129 26.6345128356038,89.6787084683935 " \ "26.2383305017275,92.348481691233 25.073636976939,92.4252199249342 25.0296592837972," \ "92.487261172615 24.9472465376954,92.4967290851295 24.902213855393,92.6799861774377 " \ "21.2972058618174,92.6799346581579 21.2853347419811,92.330290184197 20.5906091141114))" geom = WKTReader().read(wkt) parameters = HashMap() parameters.put('geoRegion', geom) subset_param = snappy.GPF.createProduct("Subset", parameters, ProductIO.readProduct(self.input_dir + "\\multi_" + timestamp + ".dim")) ProductIO.writeProduct(subset_param, subset, 'BEAM-DIMAP') print("subset complete: " + subset + " | took: " + str(datetime.datetime.now() - start_time_processing).split('.', 2)[0]) else: print("file exists - " + subset) def sentinel1_preprocess_terrain_correction(self, timestamp, HashMap): start_time_processing = datetime.datetime.now() terr = self.input_dir + "\\terr_" + timestamp if not os.path.isfile(terr + ".dim"): parameters = HashMap() # parameters.put('demResamplingMethod', 'NEAREST_NEIGHBOUR') # parameters.put('imgResamplingMethod', 'NEAREST_NEIGHBOUR') # parameters.put('pixelSpacingInMeter', 10.0) terr_param = snappy.GPF.createProduct("Terrain-Correction", parameters, ProductIO.readProduct(self.input_dir + "\\subset_" + timestamp + ".dim")) ProductIO.writeProduct(terr_param, terr, 'BEAM-DIMAP') print("terrain corrected: " + terr + " | took: " + str(datetime.datetime.now() - start_time_processing).split('.', 2)[0]) else: print("file exists - " + terr) input_dir = "path_to_project_folder\Sentinel_1" start_date = '01Mar2019' end_date = '10Mar2019' query_style = 'footprint' # 'footprint' to use a GEOJSON, 'coordinate' to use a lat-lon footprint = 'path_to_project_folder\bd_bbox.geojson' lat = 26.23 lon = 88.56 sar = sentinel1_download_preprocess(input_dir, start_date, end_date, query_style, footprint, lat, lon, True) # proceed to download by setting 'True', default is 'False' sar.sentinel1_download() The geojson file is created from a very generalised shapefile of Bangladesh by using ArcGIS Pro. There are a lot of free online tools to convert shapefile to geojson and WKT. Notice that the code will skip download if the file is already there but will keep the processing on, so comment out line 197 when necessary. Updated the code almost completely. The steps of processing raw files of Sentinel-1 used here are not the most generic way, note that there are no authentic way for this. Since different research require different steps to prepare raw data, you will need to follow yours. Also published at clubgis.
  11. 3 points
    News release April 1, 2019, Saint-Hubert, Quebec – The Canadian Space Agency and the Canada Centre for Mapping and Earth Observation are making RADARSAT-1 synthetic aperture radar images of Earth available to researchers, industry and the public at no cost. The 36,500 images are available through the Government of Canada's Earth Observation Data Management System. The RADARSAT-1 dataset is valuable for testing and developing techniques to reveal patterns, trends and associations that researchers may have missed when RADARSAT-1 was in operation. Access to these images will allow Canadians to make comparisons over time, for example, of sea ice cover, forest growth or deforestation, seasonal changes and the effects of climate change, particularly in Canada's North. This image release initiative is part of Canada's Open Government efforts to encourage novel Big Data Analytic and Data Mining activities by users. Canada's new Space Strategy places priority on acquiring and using space-based data to support science excellence, innovation and economic growth. Quick facts The RADARSAT Constellation Mission, scheduled for launch in May 2019, builds on the legacy of RADARSAT-1 and RADARSAT-2, and on Canada's expertise and leadership in Earth observation from space. RADARSAT-1 launched in November 1995. It operated for 17 years, well over its five-year life expectancy, during which it orbited Earth 90,828 times, travelling over 2 billion kilometres. It was Canada's first Earth observation satellite. RADARSAT-1 images supported relief operations in 244 disaster events. RADARSAT-2 launched in December 2007 and is still operational today. This project represents a unique collaboration between government and industry. MDA, a Maxar company, owns and operates the satellite and ground segment. The Canadian Space Agency helped to fund the construction and launch of the satellite. It recovers this investment through the supply of RADARSAT-2 data to the Government of Canada during the lifetime of the mission. Users can download these images through the Earth Observation Data Management System of the Canada Centre for Mapping and Earth Observation, a division of Natural Resources Canada (NRCan). NRCan is responsible for the long-term archiving and distribution of the images as well as downlinking of satellite data at its ground stations. source: https://www.canada.ca/en/space-agency/news/2019/03/open-data-over-36000-historical-radarsat-1-satellite-images-of-the-earth-now-available-to-the-public.html
  12. 3 points
    premium web application for ArcGIS Enterprise 10.7 that provides users with tools and capabilities in a project-based environment that streamlines image analysis and structure observation management. Interested in working with imagery in a modern, web-based experience? Here’s a look at some of the features ArcGIS Excalibur 1.0 has to offer: Search for Imagery ArcGIS Excalibur makes it easy to search and discover imagery available to you within your organization through a number of experiences. You can connect directly to an imagery layer, an image service URL, or even through the imagery catalog search. The imagery catalog search allows you to quickly search for imagery layers over areas of interest to discover and queue images for further use. Work with imagery Once you have located the imagery of interest, you can easily connect to the imagery exploitation canvas where you can utilize a wide variety of tools to begin working with your imagery. The imagery exploitation canvas allows you to view your imagery on top of a default basemap where the imagery is automatically orthorectified and aligned with the map. The exploitation canvas also enables you to simultaneously view the same image in a more focused manner as it was captured in its native perspective. Display Tools Optimizing imagery to get the most value out of each image pixel is a breeze with ArcGIS Excalibur display tools. The image display tools include image renderers, filters, the ability to change band combinations, and even apply settings like DRA and gamma. Settings to change image transparency and compression are also included. Exploitation Tools Ever need to highlight key areas of interest through mark up, labeling, and measurement? Through the mark-up tools, you can create simple graphics on top of your imagery using text and shape elements to call attention to areas of interest through outline, fill, transparency, and much more. The measurements tool allows you to measure horizontal and vertical distances, areas, and feature locations on an image. Export Tools The exploitation results saved in an image project can be easily shared using the export tools. The create presentation tool exports your current view directly to a Microsoft PowerPoint presentation, along with the metadata of the imagery. Introducing an Imagery Project ArcGIS Excalibur also introduces the concept of an imagery project to help streamline imagery workflows by leveraging the ArcGIS platform. An ArcGIS Excalibur imagery project is a dynamic way to organize resources, tools, and workflows required to complete an image-based task. An imagery project can contain geospatial reference layers and a set of tools for a focused image analysis and structured observation management workflows. Content created within imagery projects can be shared and made available to your organization to leverage in downstream analysis and shared information products.
  13. 3 points
    Klau Geomatics has released Real-Time Precise Point Positioning (PPP) for aerial mapping and drone positioning that enables 3 to 5 cm initial positioning accuracy, anywhere in the world, without any base station data or network corrections. With this, you Just need to fly your drone at any distance, anywhere. The system allows to navigate with real-time cm level positioning or geotag your mapping photos and Lidar data. You don’t need to think about setting up a base station, finding quality CORS data or setting up an RTK radio link. You don’t need to be in range of a CORS station, you can fly autonomously, in remote areas, long corridors, unlimited range, it just works, giving you centimetre level accuracy, anywhere. Now, with this latest satellite-based positioning technology, 3 to 5cm accuracy can be achieved, anywhere in the world, with no base station. KlauPPP leverages NovAtel’s industry-leading technology to achieve this quantum leap in PPP accuracy. NovAtel PPP and Klau Geomatics hardware/software system is now the simplest, most convenient and accurate positioning system for UAVs and manned aircraft. The bundled solution enables accurate positioning in any published or custom coordinate system and datum. This technology is very applicable to surveying, mapping, navigation and particularly the emerging drone inspection industry, starting to realize that absolute accuracy is essential to analyze change over time in 3D assets. A BVLOS parcel delivery drone can now travel across a country and arrive exactly on it’s landing pad. No range limitations, no base station requirements or radio links. Highly accurate autonomous flight. Large scale enterprise drone companies can deploy their fleet of operators with a simple, mechanical workflow to capture accurate, repeatable data, without the complications of the survey world; of RTK radio links and network connections or logging base station data within a range of each of their many projects. Now they have a simple consistent operation that just works, every time, every location. “Just as Klau Geomatics led the industry from RTK and GCPs to PPK, we now lead the charge to PPP as the next technology for simple, accurate drone operations”, says Rob Klau, Director of Klau Geomatics source : http://geomatics.com.au/
  14. 3 points
    Details geological-geophysical aspects of groundwater treatment Discusses regulatory legislations regarding groundwater utilization Serves as a reference material for scientists in geology, geophysics and environmental studies
  15. 2 points
    for those like me who are not English mother tongue I recommend this site for translations (English - French - German - Italian - Spanish - Portuguese - Russian - Chinese - Japanese etc.)... fantastic and intuitive that is based on artificial intelligence https://www.deepl.com/ another interesting website https://www.linguee.com/
  16. 2 points
    A new set of 10 ArcGIS Pro lessons empowers GIS practitioners, instructors, and students with essential skills to find, acquire, format, and analyze public domain spatial data to make decisions. Described in this video, this set was created for 3 reasons: (1) to provide a set of analytical lessons that can be immediately used, (2) to update the original 10 lessons created by my colleague Jill Clark and I to provide a practical component to our Esri Press book The GIS Guide to Public Domain Data, and (3) to demonstrate how ArcGIS Desktop (ArcMap) lessons can be converted to Pro and to reflect upon that process. The activities can be found here. This essay is mirrored on the Esri GeoNet education blog and the reflections are below and in this video. Summary of Lessons: Can be used in full, in part, or modified to suit your own needs. 10 lessons. 64 work packages. A “work package” is a set of tasks focused on solving a specific problem. 370 guided steps. 29 to 42 hours of hands-on immersion. Over 600 pages of content. 100 skills are fostered, covering GIS tools and methods, working with data, and communication. 40 data sources are used, covering 85 different data layers. Themes covered: climate, business, population, fire, floods, hurricanes, land use, sustainability, ecotourism, invasive species, oil spills, volcanoes, earthquakes, agriculture. Areas covered: The Globe, and also: Brazil, New Zealand, the Great Lakes of the USA, Canada, the Gulf of Mexico, Iceland, the Caribbean Sea, Kenya, Orange County California, Nebraska, Colorado, and Texas USA. Aimed at university-level graduate and university or community college undergraduate student. Some GIS experience is very helpful, though not absolutely required. Still, my advice is not to use these lessons for students’ first exposure to GIS, but rather, in an intermediate or advanced setting. How to access the lessons: The ideal way to work through the lessons is in a Learn Path which bundle the readings of the book’s chapters, selected blog essays, and the hands-on activities.. The Learn Path is split into 3 parts, as follows: Solving Problems with GIS and public domain geospatial data 1 of 3: Learn how to find, evaluate, and analyze data to solve location-based problems through this set of 10 chapters and short essay readings, and 10 hands-on lessons: https://learn.arcgis.com/en/paths/the-gis-guide-to-public-domain-data-learn-path/ Solving Problems with GIS and public domain geospatial data 2 of 3: https://learn.arcgis.com/en/paths/the-gis-guide-to-public-domain-data-learn-path-2/ Solving Problems with GIS and public domain geospatial data 3 of 3: https://learn.arcgis.com/en/paths/the-gis-guide-to-public-domain-data-learn-path-3/ The Learn Paths allow for content to be worked through in sequence, as shown below: You can also access the lessons by accessing this gallery in ArcGIS Online, shown below. If you would like to modify the lessons for your own use, feel free! This is why the lessons have been provided in a zipped bundle as PDF files here and as MS Word DOCX files here. This video provides an overview. source: https://spatialreserves.wordpress.com/2020/05/14/10-new-arcgis-pro-lesson-activities-learn-paths-and-migration-reflections/
  17. 2 points
    Stop me if you’ve heard this before. DJI has introduced its latest enterprise powerhouse drone, the DJI Matrice 300 RTK. We learned a lot about the drone earlier this week due to a few huge leaks of specs, features, photos, and videos. But it’s worth looking at the drone again now that it’s official – and an incredible intro video. Also called the M300 RTK, this drone is an upgrade in every way over its predecessor, the M200 V2. That includes a very long flight time of 55 minutes, six-direction obstacle avoidance, and a doubled (6 pound) payload capability. That allows it to carry a range of powerful cameras, which we’ll get to in a bit. The drone is also built for weather extremes. IP45 weather sealing keeps out rain and dust. And a self-heating battery helps the drone to run in a broad range of temperatures, from -4 to 122 Fahrenheit. The DJI Matrice 300 RTK can fly up to 15 kilometers (9.3 miles) from its controller and still stream 1080p video back home. That video and other data can be protected using AES-256 encryption. The drone can also be flown by two co-pilots, with one able to take over for the other if any problem arises or a handoff scenario. A workhorse inspection drone All these capabilities are targeted to the DJI Matrice 300 RTK’s purpose as a drone for heavy-duty visual inspection and data collection work, such as surveys of power lines or railways. In fact, it incorporates many advanced camera features for the purpose. Smart inspection is a new set of features to optimize data collection. It includes live mission recording, which allows the drone to record every aspect of a flight, even camera settings. This allows workers to train a drone on an inspection mission that it will repeat again and again. With AI spot check, operators can mark the specific part of the photo, such as a transformer, that is the subject of inspection. AI algorithms compare that to what the camera sees on a future flight, so that it can frame the subject identically on every flight. An inspection drone is only as good as its cameras, and the M300 RTK offers some powerful options from DJI’s Zenmuse H20 series. The first option is a triple-camera setup. It includes a 20-megapixel, 23x zoom camera; a 12MP wide-angle camera; and a laser rangefinder that measures out to 1,200 meters (3,937 feet). The second option adds a radiometric thermal camera. TO make things simpler for operators, the drone provides a one-click capture feature that grabs videos or photos from three cameras at once, without requiring the operator to switch back and forth. Eyes and ears ready for danger With its flight time and range, the DJI Matrice 300 RTK could be flying some long, complex missions, easily beyond visual line of site (if its owner gets an FAA Part 107 waiver for that). This requires some solid safety measures. While the M200 V2 has front-mounted sensors, the M300 RTK has sensors in six directions for full view of the surroundings. The sensors can register obstacles up to 40 meters (98 feet) away. Like all new DJI drones, the M300 RTK also features the company’s AirSense technology. An ADS-B receiver picks up signals from manned aircraft that are nearby and alerts the drone pilot of their location. It’s been quite a few weeks for DJI. On April 27, it debuted its most compelling consumer drone yet, the Mavic Air 2. Now it’s showing off its latest achievement at the other end of the drone spectrum with the industrial grade Matrice 300 RTK. These two, very different drones help illustrate the depth of product that comes from the world’s biggest drone maker. And the company doesn’t show signs of slowing down, despite the COVID-19 economic crisis. Next up, we suspect, will be a revision to its semi-pro quadcopter line in the firm of a Mavic 3. It is available at DJI.It’s been quite a few weeks for DJI. On April 27, it debuted its most compelling consumer drone yet, the Mavic Air 2. Now it’s showing off its latest achievement at the other end of the drone spectrum with the industrial grade Matrice 300 RTK. These two, very different drones help illustrate the depth of product that comes from the world’s biggest drone maker. And the company doesn’t show signs of slowing down, despite the COVID-19 economic crisis. Next up, we suspect, will be a revision to its semi-pro quadcopter line in the firm of a Mavic 3. It is available at DJI. source: https://dronedj.com/2020/05/07/dji-matrice-300-rtk-drone-official/
  18. 2 points
    @intertronic, thanks for your input. I found a solution that suite my case better due to the fact that we are using both version of QGIS and also because I was looking for interoperability. Therefore I have decided to use QSphere. Most probably not well known around the globe. https://qgis.projets.developpement-durable.gouv.fr/projects/qsphere GUI quiete ugly but at least is doing the job. 😉 darksabersan
  19. 2 points
    DRONE MAKER DJI announced an update to its popular Mavic Air quadcopter today. The Mavic Air 2 will cost $799 when it ships to US buyers in late May. That's the same price as the previous Mavic Air model, so the drone stays as DJI's mid-range option between its more capable Mavic 2 and its smaller, cheaper Mavic Mini. The Mavic Air 2 is still plenty small, but the new version has put on some weight. DJI says that testing and consumer surveys suggested that most people don't mind lugging a few extra grams in exchange for a considerable upgrade in flight time and, presumably, better handling in windy conditions. Even better, thanks to a new rotor design and other aerodynamic improvements, DJI is claiming the Mavic Air 2 can remain aloft for 34 minutes—a big jump from the 21 minutes of flight time on the original Mavic Air. The Camera Eye he big news in this update is the new larger imaging sensor on the drone's camera. The Mavic Air 2's camera ships with a half-inch sensor, up from the 1 2/3-inch sensor found in the previous model. That should mean better resolution and sharper images, especially because the output specs haven't changed much. The new camera is still outputting 12-megapixel stills, but now has a bigger sensor to fill that frame with more detail. There's also a new composite image option that joins together multiple single shots into a large, 48-megapixel image. On the video side, there's some exciting news. The Mavic Air 2 is DJI's first drone to offer 4K video at 60 frames per second and 120 Mbps—previous DJI drones topped out at 30 fps when shooting in full 4K resolution. There are also slow-motion modes that slow down footage to four times slower than real life (1080p at 120 fps), or eight-times slower (1080 at 240 fps). Combine those modes with the more realistic contrast you get with the HDR video standard, and you have considerably improved video capabilities in a sub-$1,000 drone. More interesting in some ways is DJI's increasing forays into computational photography, which the company calls Smart Photo mode. Flip on Smart Photo and the Mavic Air 2 will do scene analysis, tap its machine intelligence algorithm and automatically choose between a variety of photo modes. There's a scene recognition mode where the Mavic Air 2 sets the camera up to best capture one of a variety of scenarios you're likely to encounter with drone photography, including blue skies, sunsets, snow, grass, and trees. In each case, exposure is adjusted to optimize tone and detail. The second Smart Photo mode is dubbed Hyperlight, which handles low-light situations. To judge by DJI's promo materials, this is essentially an HDR photography mode specifically optimized for low-light scenes. It purportedly cuts noise and produces more detailed images. The final smart mode is HDR, which takes seven images in rapid succession, the combines elements of each to make a final image with a higher dynamic range. One last note about the camera: The shape of the camera has changed, so if you have any lenses or other accessories for previous DJI drones, they won't attach to the Air 2. Automatic Flight for the People If you dig through older YouTube videos there's a ton of movies that play out like this: unbox new drone, head outside, take off, tree gets closer, closer, closer, black screen. Most of us just aren't that good at flying, and the learning curve can be expensive and steep. Thankfully drone companies began automating away most of what's difficult about piloting a quadcopter, and DJI is no exception. The company has added some new automated flight tricks to the Air's arsenal. DJI's Active Track has been updated to version 3.0, which brings better subject recognition algorithms and some new 3D mapping tricks to make it easier to automatically track people through a scene, keeping the camera on the subject as the drone navigates overhead to stay with them. DJI claims the Point of Interest mode—which allows you to select an object and fly around it in a big circle while the camera stays pointed at the subject—is better at tracking some of the objects that previous versions struggled with, like vehicles or even people. The most exciting new flight mode is Spotlight, which comes from DJI's high-end Inspire drone used by professional photographers and videographers to carry their DSLR cameras into the sky. Similar to the Active Track mode, Spotlight keeps the camera pointed a moving subject. But while Active Track automates the drone's flight, the new Spotlight mode allows the human pilot to retain control of the flight path for more complex shots. Finally, the range of the new Mavic Air 2 has been improved, and it can now wander an impressive six miles away from the pilot in ideal conditions. The caveat here is that you should always maintain visual contact with your drone for safety reasons. However, you aren't going to be able to see the Mavic Air 2 when it's two miles away, let alone six. Despite a dearth of competitors, DJI continues to put out new drones and improve its lineup as it progresses. The Mavic Air 2 looks like an impressive update to what was already one of our favorite drones, especially considering several features—the 60 fps 4K video and 34 minute flight time—even best those found on the more expensive Mavic 2 Pro. links: https://www.dji.com/id/mavic-air-2
  20. 2 points
    I like drones but just got more interested in this,
  21. 2 points
    Harvard Online Courses Advance your career. Pursue your passion. Keep learning. links: https://online-learning.harvard.edu/CATALOG/FREE
  22. 2 points
  23. 2 points
    Saw a similar news last month - Using Machine Learning to “Nowcast” Precipitation in High Resolution by Google. The result seemed pretty good. Here, A visualization of predictions made over the course of roughly one day. Left: The 1-hour HRRR prediction made at the top of each hour, the limit to how often HRRR provides predictions. Center: The ground truth, i.e., what we are trying to predict. Right: The predictions made by our model. Our predictions are every 2 minutes (displayed here every 15 minutes) at roughly 10 times the spatial resolution made by HRRR. Notice that we capture the general motion and general shape of the storm. The two method seem similar.
  24. 2 points
    With Huawei basically blocked from using Google services and infrastructure, the firm has taken steps to replace Google Maps on its hardware by signing a partnership with TomTom to provide maps, navigation, and traffic data to Huawei apps. Reuters reports that Huawei is entering this partnership with TomTom as the mapping tech company is based in the Netherlands — therefore side-stepping the bans on working with US firms. TomTom will provide the Chinese smartphone manufacturer with mapping, live traffic data, and software on smartphones and tablets. TomTom spokesman Remco Meerstra confirmed to Reuters that the deal had been closed some time ago but had not been made public by the company. This comes as TomTom unveiled plans to move away from making navigation hardware and will focus more heavily on offering software services — making this a substantial step for TomTom and Huawei. While TomTom doesn’t quite match the global coverage and update speed of Google Maps, having a vital portion of it filled by a dedicated navigation and mapping firm is one step that might appease potential global Huawei smartphone buyers. There is no denying the importance of Google app access outside of China but solid replacements could potentially make a huge difference — even more so if they are recognizable by Western audiences. It’s unclear when we may see TomTom pre-installed on Huawei devices but we are sure that this could be easily added by way of an OTA software update. The bigger question remains if people are prepared to switch from Google Maps to TomTom for daily navigation. resource: https://9to5google.com/2020/01/20/huawei-tomtom/
  25. 2 points
    January 3, 2020 - Recent Landsat 8 Safehold Update On December 19, 2019 at approximately 12:23 UTC, Landsat 8 experienced a spacecraft constraint which triggered entry into a Safehold. The Landsat 8 Flight Operations Team recovered the satellite from the event on December 20, 2019 (DOY 354). The spacecraft resumed nominal on-orbit operations and ground station processing on December 22, 2019 (DOY 356). Data acquired between December 22, 2019 (DOY 356) and December 31, 2019 (DOY 365) exhibit some increased radiometric striping and minor geometric distortions (see image below) in addition to the normal Operational Land Imager/Thermal Infrared Sensor (OLI/TIRS) alignment offset apparent in Real-Time tier data. Acquisitions after December 31, 2019 (DOY 365) are consistent with pre-Safehold Real-Time tier data and are suitable for remote sensing use where applicable. All acquisitions after December 22, 2019 (DOY 356) will be reprocessed to meet typical Landsat data quality standards after the next TIRS Scene Select Mirror (SSM) calibration event, scheduled for January 11, 2020. Landsat 8 Operational Land Imager acquisition on December 22, 2019 (path 148/row 044) after the spacecraft resumed nominal on-orbit operations and ground station processing. This acquisition demonstrates increased radiometric striping and minor geometric distortions observed in all data acquired between December 22, 2019 and December 31, 2019. All acquisitions after December 22, 2019 will be reprocessed on January 11, 2020 to achieve typical Landsat data quality standards. Data not acquired during the Safehold event are listed below and displayed in purple on the map (click to enlarge). Map displaying Landsat 8 scenes not acquired from Dec 19-22, 2019 Path 207 Rows 160-161 Path 223 Rows 60-178 Path 6 Rows 22-122 Path 22 Rows 18-122 Path 38 Rows 18-122 Path 54 Rows 18-214 Path 70 Rows 18-120 Path 86 Rows 24-110 Path 102 Rows 19-122 Path 118 Rows 18-185 Path 134 Rows 18-133 Path 150 Rows 18-133 Path 166 Rows 18-222 Path 182 Rows 18-131 Path 198 Rows 18-122 Path 214 Rows 34-122 Path 230 Rows 54-179 Path 13 Rows 18-122 Path 29 Rows 20-232 Path 45 Rows 18-133 After recovering from the Safehold successfully, data acquired on December 20, 2019 (DOY 354) and from most of the day on December 21, 2019 (DOY 355) were ingested into the USGS Landsat Archive and marked as "Engineering". These data are still being assessed to determine if they will be made available for download to users through all USGS Landsat data portals. source: https://www.usgs.gov/land-resources/nli/landsat/january-3-2020-recent-landsat-8-safehold-update
  26. 2 points
    just found this interesting articles on Agisoft forum : source: https://www.agisoft.com/forum/index.php?topic=7851.0
  27. 2 points
    one of my favorite image hosting, , this is their announcement : Rest in Peace TinyPic
  28. 2 points
    not necessary to excel environment but : https://github.com/orbisgis/h2gis/wiki/4.2-LibreOffice
  29. 2 points
    This is an interesting topic from not quite an old webpage. I was searching for some use case of blockchain in geospatial context and found this. The contexts still challenging, but very noteworthy. What is a blockchain and how is it relevant for geospatial applications? (By Jonas Ellehauge, awesome map tools, Norway) A blockchain is an immutable trustless registry of entries, hosted on an open distributed network of computers (called nodes). It is potentially safer and cheaper than traditional centralised databases, is resilient to attacks, enhances transparency and accountability and puts people in control of their own data. Blockchain technology is already being used in some geospatial applications, as explained here. As an immutable registry for transactions of digital tokens, blockchain is suitable for geospatial applications involving data that is sensitive or a public good, autonomous devices and smart contracts. Use Cases The use cases are discussed further below. I have given a few short talks about this topic at various conferences, most recently at the international FOSS4G conference in Bonn, Germany, 2016. Public-good data Open Data is Still Centralised Data Over the past two decades, I have seen how ‘public-good’ geospatial data has generally become much easier to get hold of, having originally been very inaccessible to most people. Gradually, the software to display and process the data became cheaper or even free, but the data itself – data that people had already paid for through their taxes – remained inaccessible. Some national mapping institutions and cadastres began distributing the data via the internet, although mostly with a price tag. Only in recent years have a few countries in Europe made public map data freely accessible. In the meantime, projects like OpenStreetMap have emerged in order to meet people’s need for open data. It is hardly a surprise, then, that a myriad of new apps, mock-ups and business cases emerge in a region shortly after data is made available to the public there. Truly Public Open Data One of the reasons that this data has remained inaccessible for so long is that it is collected and distributed through a centralised organisation. A small group of people manage enormous repositories of geospatial data and can restrict or grant access to it. As I see it, this is where blockchain and related technologies like IPFS can enable people to build systems where the data is inherently public, no one controls it, anyone can access it, and anyone can review the full history of contributions to the data. Would it be free of charge to use data from such a system? Who would pay for it? I guess time will tell which business model is the most sustainable in that respect. OpenStreetMap is free to use, it is immensely popular and yet people gladly contribute to it – so who pays the cost for OSM? Bear in mind that there’s no such thing as ‘free data’. For example, the ‘free’ open data in Denmark today is paid for through taxes. So, even if it would cost a little to use the blockchain-based data, that wouldn’t be so different from now – just that no one would be able to restrict access to the data, plus the open nature of competing nodes and contributors will minimise the costs. Autonomous Devices & Apps Uber and Airbnb are examples of consumer applications that rely on geospatial data and processing. They represent a centralised approach where the middleman owns and controls the data and charges a significant fee for connecting clients and providers with each other. If such apps were replaced by distributed peer-to-peer systems, they could be cheaper and give their users full control of their data. There is already such an alternative to Uber called Arcade.City. A peer-to-peer market app like OpenBazar may also benefit from geospatial components with regards to e.g. search and logistics. Such autonomous apps may currently have to rely on third parties for their geospatial components – e.g. Google Maps, Mapbox, OpenStreetMap, etc. With access to truly publicly distributed data as described above, such apps would be even more reliable and cheaper to run. An autonomous device such as a drone or a self-driving car inherently runs an autonomous application, so these two concepts are heavily intertwined. There’s no doubt that self-navigating cars and drones will be a growing market in the near future. Uber and Tesla have big ambitions regarding cars, drones are being designed for delivery of consumer products (Amazon), and drone-based emergency response (drone defibrillator) and imaging (automatic selfie drone ‘Lily’) applications are emerging. Again, distributed peer-to-peer apps could cut out the middleman and reliance on third parties for their navigation and other geospatial components. Land Ownership What is Property? After some years in the GIS software industry, I realised that a very large part of my work revolved around cadastres/parcels and other administrative borders plus technical base maps featuring roads, buildings, etc. In view of my background in physical geography I thought that was pretty boring stuff and I dreamt about creating maps and applications that involved temperatures, wind, currents, salinity, terrain models, etc., because it felt more ‘real’. I gradually realised that something about administrative data was nagging me – as if it didn’t actually represent reality. Lately, I have taken an interest in philosophy about human interaction, voluntary association and self-ownership. It turns out that property is a moral, philosophical concept of assets acquired through voluntary transactions or homesteading. This perspective stretches at least as far back as John Locke in the 17th century. Such justly acquired property is reality, whereas law, governance services and computer code are systems that attempt to model reality. When such systems don’t fit reality, the system is wrong and should be dismissed, possibly adjusted or replaced. Land Ownership For the vast majority of people in many developing countries, there is no mapping of parcels or proof of ownership available to the actual landowners. Christiaan Lemmen, an expert on cadastres, has experience from field work to map parcels in developing countries such as Nigeria, Liberia, etc., where corruption can be a big challenge within land administration. In his experience, however, people mostly agree on who owns what in their local communities. These people often have a need for proof of identity and proof of ownership for their justly acquired land in order to generate wealth, invest in their future and prevent fraud – while they often face problems with inefficient, expensive or corrupt government services. Ideally, we could build inexpensive, reliable and easy-to-use blockchain-based systems that will enable people to map and register their land together with their neighbours – without involving any government officials, lawyers or other middlemen. Geodesic Grids It has been suggested to use geodesic grids of discrete cells to register land ownership on a blockchain. Such cells can be shaped, e.g. as squares, triangles, pentagons, hexagons, etc., and each cell has a unique identifier. In a traditional cadastral system, parcels are represented with flexible polygons, which allows users to register any possible shape of a parcel. Although a grid of discrete cells doesn’t allow such flexible polygons, it has an advantage in this case: each digital token on the blockchain (let’s call it a ‘Landcoin’) can represent one unique cell in the grid. Hence, whoever owns a particular Landcoin owns the corresponding piece of land. Owning such a Landcoin means possessing the private encryption key that controls it – which is how other cryptocurrencies work. In order to represent complex and high-resolution geometries, it is preferable to use a grid which is infinitely sub-divisible so that ever-smaller triangles, hexagons or squares, etc., can be tied together to represent any piece of land. A digital token can also be infinitely sub-divisible. For comparison, the smallest unit of a Bitcoin is currently a 100-millionth – aka a ‘Satoshi’. If needed, the core software could be upgraded to support even smaller units. What is a Blockchain? A blockchain is an immutable trustless registry of entries, hosted on an open distributed network of computers (called nodes). It is potentially safer and cheaper than traditional centralised databases, is resilient to attacks, enhances transparency and accountability and puts people in control of their own data. Safer – because no one controls all the data (known as root privilege in existing databases). Each entry has its own pair of public and private encryption keys and only the holder of the private key can unlock the entry and transfer it to someone else. Immutable – because each block of entries (added every 1-10 minutes) carries a unique hash ‘fingerprint’ of the previous block. Hence, older blocks cannot be tampered with. Cheaper – because anyone can set up a node and get paid in digital tokens (e.g. Bitcoin or Ether) for hosting a blockchain. This ensures that competition between nodes will minimise the cost of hosting it. It also saves the costs of massive security layers that otherwise apply to servers with sensitive data – this is because of the no-root-privilege security model and, with old entries being immutable, there’s little need to protect them. Resilient – because there is no single point of failure, there’s practically nothing to attack. In order to compromise a blockchain, you’d have to hack each individual user one by one in order to get hold of their private encryption keys that give access to that user’s data only. Another option is to run over 50% of the nodes, which is virtually impossible and economically impractical. Transparency and accountability – the fact that existing entries cannot be tampered with makes a blockchain a transparent source of truth and history for your application. The public nature of it makes it easy to hold people accountable for their activities. Control – the immutable and no-root-privilege character puts each user in full control of his/her own data using the private encryption keys. This leads to real peer-to-peer interaction without any middleman and without an administrator that can deny users access to their data. Trustless – because each user fully controls his/her own data, users can safely interact without knowing or trusting each other and without any trusted third parties. Smart Contracts and DAPPs A blockchain can be more than a passive registry of entries or transactions. The original Bitcoin blockchain supports limited scripting allowing for programmable transactions and smart contracts – e.g. where specified criteria must be fulfilled leading to transactions automatically taking place. Possibly the most popular alternative to Bitcoin is Ethereum, which is a multi-purpose blockchain with a so-called ‘Turing complete’ programming interface, which allows developers to create virtually any imaginable application on this platform. Such applications are referred to as decentralised autonomous applications (DAPPs) and are virtually impossible for third parties to stop or censor. [1] IFPS IPFS is a distributed file system and web protocol, which can complement or even replace HTTP. Instead of referring to files by their location on a host or IP address, it refers to files by their content. This means that when requested, IPFS will return the content from the nearest possible or even multiple computers rather than from a central server. That could be on the computer next to you, on your local network or somewhere in the neighbourhood. Jonas Ellehauge is an expert on geospatial software, GIS and web development, enthusiastic about open source, Linux and UI/UX. Ellehauge is passionate about science, philosophy, entrepreneurship, economy and communication. His background in physical geography provides extensive knowledge of spatial analyses and spatial problem solving.
  30. 2 points
    multifunction casing. you can run 3d games and grating cheese for your hamburger. excelent thought apple as always LOL
  31. 2 points
    We are all already familiar with GPS navigation outdoors and what wonders it does not only for our everyday life, but also for business operations. Outdoor maps, allowing for navigation via car or by foot, have long helped mankind to find even the most remote and hidden places. Increased levels of efficiency, unprecedented levels of control over operational processes, route planning, monitoring of deliveries, safety and security regulations and much more have been made possible. Some places are, however, harder to reach and navigate than others. For instance, places like big indoor areas – universities, hospitals, airports, convention centers or factories, among others. Luckily, that struggle is about to become a thing of the past. So what’s the solution for navigating through and managing complex indoor buildings? Indoor Mapping and Visualization with ArcGIS Indoors The answer is simple – indoor mapping. Indoor mapping is a revolutionary concept that visualizes an indoor venue and spatial data on a digital 2D or 3D map. Showing places, people and assets on a digital map enables solutions such as indoor positioning and navigation. These, in turn, allow for many different use cases that help companies optimize their workflows and efficiencies. Mobile Navigation and Data The idea behind this solution is the same as outdoor navigation, only instead it allows you to see routes and locate objects and people in a closed environment. As GPS signals are not available indoors, different technology solutions based on either iBeacons, WiFi or lighting are used to create indoor maps and enable positioning services. You can plan a route indoors from point A to point B with customized pins and remarks, analyze whether facilities are being used to their full potential, discover new business opportunities, evaluate user behaviors and send them real-time targeted messages based on their location, intelligently park vehicles, and the list goes on! With the help of geolocation, indoor mapping stores and provides versatile real-time data on everything that is happening indoors, including placements and conditions of assets and human movements. This allows for a common operating picture, where all stakeholders share the same level of information and insights into internal processes. Having a centralized mapping system enables effortless navigation through all the assets and keeps facility managers updated on the latest changes, which ultimately improves business efficiency. Just think how many operational insights can be received through visualizations of assets on your customized map – you can monitor and analyze the whole infrastructure and optimize the performance accordingly. How to engage your users/visitors at the right time and place? What does it take to improve security management? Are the workflow processes moving seamlessly? Answers to those and many other questions can be found in an indoor mapping solution. Interactive indoor experiences are no longer a thing of the future, they are here and now. source: https://www.esri.com/arcgis-blog/products/arcgis-indoors/mapping/what-is-indoor-mapping/
  32. 2 points
    Hi evrybody I'm an italian architect, dealing few times with GIS related topics. I also like to draw my own seamaps for my Chartplotter.
  33. 2 points
    SarVision was created in 2000, as a spin-off from Wageningen University (WUR) in the Netherlands. SarVision pioneers the operational application of systematic satellite monitoring and mapping systems for environmental and natural resource management. Our innovative systems provide our partners with the latest maps and information on agriculture and land use, forest cover change, fire and hydrology. Our inhouse cutting edge radar technology, which « sees » through clouds, smoke and haze, enables continuous land surface monitoring, updating data on a continuous basis (bi-weekly to yearly). SarVision contributes to numerous sustainable development efforts in tropical regions around the globe, working directly with organisations as diverse as space agencies, multilateral institutions, government agencies, local community associations, farmers, agribusiness, logging and plantation companies, nature conservation organisations, oil and gas companies, universities and insurance companies. Job description We are looking for a remote sensing expert to join our team. Together with SarVision experts, you will contribute to the development and implementation of operational services in the areas of agriculture, water, forest and land use mapping and monitoring. You will have the opportunity to apply and further develop your skills in: • The processing of satellite images: pre-processing tasks, image classification using in-house and external software packages; • GIS: quality control and validation, data analysis and presentation, integration of multiple data sources; • IT and programming: automation of processing tasks and processing chains from data acquisition to delivery of final product. You will mainly work in a team with SarVision remote sensing experts, but also carry out operational tasks autonomously. Requirements • A Bachelor or Master’s Degree with main focus on Remote Sensing, Geoinformatics, Geography, Agriculture, Forestry or related area of expertise; • Professional experience in a remote sensing company would be beneficial; • Ability to work in complex, multi-task team situation; • Willingness and ability to learn new skills quickly; • Ability to work under time pressure and respect deadlines, keeping track of long term objectives; • Ability to travel occasionally to developing countries; • Very good English language skills, Dutch and/or Spanish advantageous. Technical skills: • Remote sensing background; • Experience in image processing for agriculture, forest, and land cover/land use applications; • Knowledge in statistical analyses (sampling design, accuracy assessment); • Programming skills: experience/knowledge of Python, GDAL; IDL, Matlab, R, C++, Java: advantageous • Experience with Linux and Bash: advantageous • Experience with QGIS, PostGIS: advantageous; • Radar data processing and machine learning skills: advantageous. Duration & starting date We offer a fix-term contract of 1 year, with possibility of extension. Starting date as soon as possible. How to apply? Send a CV and motivation letter in English to Wilbert van Rooij ([email protected]) before June 25th 2019. www.sarvision.nl
  34. 2 points
    Topcon Positioning Group’s Dave Henderson offers a rundown on the company’s latest products, including the Falcon 8+ drone, Sirius Pro, MR-2 modular receiver, and B210 and B125 receiver boards, at Xponential 2019. source: https://www.gpsworld.com/topcon-showcases-falcon-8-drone-sirius-pro-and-receiver-boards-at-xponential-2019/
  35. 2 points
    As part of ArcGIS Enterprise 10.7, we (ESRI) are thrilled to release a new capability that unlocks versatile data science tools and the limitless potential of Python in your Web GIS deployment. ArcGIS Notebooks provide users with a Jupyter notebook environment, hosted in your ArcGIS Enterprise portal and powered by the new ArcGIS Notebook Server. ArcGIS Notebooks are built to run big data analysis, deep learning models, and dynamic visualization tools. Notebooks are implemented using Docker containers – a virtualized operating system that provides an isolated “sandbox” style environment for each notebook author. The computational resources for each container can be configured by the organization – allowing the flexibility for notebook authors to get the computing resources they need, when they need it. Seamless integration with the portal ArcGIS Notebook Server is a new licensing role for ArcGIS Server. Because it works with the Docker container allocation technology to deliver a separate container for each notebook author, it requires specific installation steps to get up and running. Take a look at the ArcGIS Notebook Server install guide to see how it works. Once you’ve installed ArcGIS Notebook Server and configured it with your portal, you can create custom roles to grant notebook privileges to the members of your organization so that they can create and edit notebooks. Put Python to work for you At the core of the ArcGIS Notebook experience are Esri’s powerful Python resources: ArcPy and the ArcGIS API for Python. Alongside these are hundreds of popular Python libraries, such as TensorFlow, scikit-learn, and fast.ai. It all comes together to give you a complete Python workstation for spatial analysis, data science, deep learning, and content management. The Standard license of ArcGIS Notebook Server, which comes at no additional cost for ArcGIS Enterprise customers, bundles the Python API and nearly 300 other third-party Python libraries built-in. The Jupyter notebook environment has long been an essential medium for Python API users; with ArcGIS Notebooks, that environment is now available directly in the ArcGIS Enterprise portal. Turn analysis into action Location is the common thread that runs through almost any problem. What you buy, who your customers are, the impact that your business has on the natural world, and that the natural world has on your business are all problems of location. Traditional data science has many powerful tools and algorithms for solving problems. Spatial data science – GeoAI – also brings in spatial data, methods, and tools. GeoAI can help you create more effective models that more closely resemble problems you want to solve. Because of this, spatial data science models are better suited to model the impact of the solution you create. . Installation and getting started Esri Jupyter Notebook And those who wants their own free jupyter notebook # install miniconda and hit conda install -y jupyter 😁
  36. 1 point
    preciso de ajuda para trabalhar operaction Dashbord ArcGis ja crie a conta esri mais não me deixa usar operaction Dashbord ArcGis
  37. 1 point
    Take a look on this: links: https://www.cambridge.org/core/what-we-publish/textbooks untested, maybe you need make free user account first they have nice collection of engineering and geosciences books https://www.cambridge.org/core/what-we-publish/textbooks/listing?aggs[productSubject][filters]=F470FBF5683D93478C7CAE5A30EF9AE8 https://www.cambridge.org/core/what-we-publish/textbooks/listing?aggs[productSubject][filters]=CCC62FE56DCC1D050CA1340C1CCF46F5
  38. 1 point
    New algorithm solves complex problems more easily and more accurately on a personal computer while requiring less processing power than a supercomputer The exponential growth in computer processing power seen over the past 60 years may soon come to a halt. Complex systems such as those used in weather forecast, for example, require high computing capacities, but the costs for running supercomputers to process large quantities of data can become a limiting factor. Researchers at Johannes Gutenberg University Mainz (JGU) in Germany and Università della Svizzera italiana (USI) in Lugano in Switzerland have recently unveiled an algorithm that can solve complex problems with remarkable facility – even on a personal computer. Exponential growth in IT will reach its limit In the past, we have seen a constant rate of acceleration in information processing power as predicted by Moore's Law, but it now looks as if this exponential rate of growth is limited. New developments rely on artificial intelligence and machine learning, but the related processes are largely not well-known and understood. "Many machine learning methods, such as the very popular deep learning, are very successful, but work like a black box, which means that we don't know exactly what is going on. We wanted to understand how artificial intelligence works and gain a better understanding of the connections involved," said Professor Susanne Gerber, a specialist in bioinformatics at Mainz University. Together with Professor Illia Horenko, a computer expert at Università della Svizzera italiana and a Mercator Fellow of Freie Universität Berlin, she has developed a technique for carrying out incredibly complex calculations at low cost and with high reliability. Gerber and Horenko, along with their co-authors, have summarized their concept in an article entitled "Low-cost scalable discretization, prediction, and feature selection for complex systems" recently published in Science Advances. "This method enables us to carry out tasks on a standard PC that previously would have required a supercomputer," emphasized Horenko. In addition to weather forecasts, the research see numerous possible applications such as in solving classification problems in bioinformatics, image analysis, and medical diagnostics. Breaking down complex systems into individual components The paper presented is the result of many years of work on the development of this new approach. According to Gerber and Horenko, the process is based on the Lego principle, according to which complex systems are broken down into discrete states or patterns. With only a few patterns or components, i.e., three or four dozen, large volumes of data can be analyzed and their future behavior can be predicted. "For example, using the SPA algorithm we could make a data-based forecast of surface temperatures in Europe for the day ahead and have a prediction error of only 0.75 degrees Celsius," said Gerber. It all works on an ordinary PC and has an error rate that is 40 percent better than the computer systems usually used by weather services, whilst also being much cheaper. SPA or Scalable Probabilistic Approximation is a mathematically-based concept. The method could be useful in various situations that require large volumes of data to be processed automatically, such as in biology, for example, when a large number of cells need to be classified and grouped. "What is particularly useful about the result is that we can then get an understanding of what characteristics were used to sort the cells," added Gerber. Another potential area of application is neuroscience. Automated analysis of EEG signals could form the basis for assessments of cerebral status. It could even be used in breast cancer diagnosis, as mammography images could be analyzed to predict the results of a possible biopsy. "The SPA algorithm can be applied in a number of fields, from the Lorenz model to the molecular dynamics of amino acids in water," concluded Horenko. "The process is easier and cheaper and the results are also better compared to those produced by the current state-of-the-art supercomputers." The collaboration between the groups in Mainz and Lugano was carried out under the aegis of the newly-created Research Center Emergent Algorithmic Intelligence, which was established in April 2019 at JGU and is funded by the Carl Zeiss Foundation. https://www.uni-mainz.de/presse/aktuell/10864_ENG_HTML.php
  39. 1 point
    really nice, is it possible to leverage into forecast? that would be interesting
  40. 1 point
    I have a project with autocad files fire up my Workstation Laptop (Dell Precission 5510) and load CAD data. Holly cr*p, this software run like a snail, 🤣 try to disable Hardware acceleration, yeah much better experience, but still laggy as old Arcgis Pro beta 😂 searching around and found this article: https://knowledge.autodesk.com/support/autocad/troubleshooting/caas/sfdcarticles/sfdcarticles/Optimize-Performance-within-Windows-7-Environments.html?_ga=2.205082898.303799305.1579712200-1066991414.1579712200 didnt have time to try all the suggestion yet, but, hey all GISArea members, do you use Autocad? how to improve your CAD Experience? share with me, 😉
  41. 1 point
  42. 1 point
    Interesting articles : North-South displacement field - 1999 Hector-Mine earthquake, California In complement to seismological records, the knowledge of the ruptured fault geometry and co-seismic ground displacements are key data to investigate the mechanics of seismic rupture. This information can be retrieved from sub-pixel correlation of optical images. We are investigating the use of SPOT (Satellite pour l'Observation de la Terre) satellites images. The technique developed here is attractive due to the operational status of a number of optical imaging programs and the availability of archived data. However, uncertainties on the imaging system itself and on its attitude dramatically limit its potential. We overcome these limitations by applying an iterative corrective process allowing for precise image registration that takes advantage of the availability of accurate Digital Elevation Models with global coverage (SRTM). This technique is thus a valuable complement to SAR interferometry which provides accurate measurements kilometers away from the fault but generally fails in the near-fault zone where the fringes get noisy and saturated. Comparison between the two methods is briefly discussed, with application on the 1992 Landers earthquake in California (Mw 7.3). Applications of this newly developped technique are presented: the horizontal co-seismic displacement fields induced by the 1999 Hector-Mine earthquake in California (Mw 7.1) and by the 1999 Chichi earthquake in Taiwan (Mw 7.5) have recently been retrieved using archive images. Data obtained can be downloaded (see further down) Latest Study Cases Sub-pixel correlation of optical images Following is the flow chart of the technique that as been developped. It allows for precise orthorectification and coregistration of the SPOT images. More details about the optimization process will be given in the next sections. Understanding the disparities measured from Optical Images Differences in geometry between the two images to be registered: - Uncertainties on attitudes parameters (roll, pitch, yaw) - Inaccuracy on orbital parameters (position, velocity) - Incidence angle differences + topography uncertainties (parallax effect) - Optical and Electronic biases (optical aberrations, CCD misalignment, focal length, sampling period, etc… ) » May account for disparities up to 800 m on SPOT 1,2,3,4 images; 50m for SPOT 5 (see [3]). Ground deformations: - Earthquakes, land slides, etc… » Typically subpixel scale: ranging from 0 to 10 meters. Temporal decorrelation: - Changes in vegetation, rivers, changes in urban areas, etc… » Correlation is lost: add noise to the measurement – up to 1m. » Ground deformations are largely dominated by the geometrical artifacts. Precise registration: geometrical corrections SPOT (Systeme pour l'Observation de la Terre) satellites are pushbroom imaging systems ([1],[2]): all optical parts remain fixed during acquisition and the scanning is accomplished by the forward motion of the spacecraft. Each line in the image is then acquired at a different time and submitted to the different variations of the platform. The orthorectification process consists in modeling and correcting these variations to produce cartographic distortion free images. It is then possible to accurately register images and look for their disparities using correlation techniques. Attitude variations (roll, pitch, and yaw) during the scanning process have to be integrated in the image model (see [1],[2]). Errors in correcting the satellite look directions will result in projecting the image pixels at the wrong location on the ground: important parallax artifacts will be seen when measuring displacement between two images. Exact pixel projection on the ground is achieved through an optimization algorithm that iteratively corrects the look directions by selecting ground control points. An accurate topography model has to be used. What parameters to optimize? - Initial attitudes values of the platform (roll, pitch, yaw), - Constant drift of the attitude values along the image acquisition, - Focal length (different value depending on the instrument , HRG1 – HRG2), - Position and velocity. How to optimize: Iterative algorithm using a set of GCPs (Ground Control Points). GCPs are generated automatically with a subpixel accuracy: they result from a correlation between an orthorectified reference frame and the rectified image whose parameters are to be optimized. A two stages procedure: - One of the image is optimized with respect to the shaded DEM (GCP are generated from the correlation with the shaded DEM). The DEM is then considered as the ground truth. No GPS points are needed. - The other image is then optimized using another set of GCP resulting from the correlation with the first image (co-registration). Measuring co-seismic deformation with InSAR, a comparison A fringe represents a near-vertical displacement of 2.8 cm SAR interferogram (ERS): near-vertical component of the ground displacement induced by the 1992 Landers earthquake [Massonnet et al., 1993]. No organized fringes in a band within 5-10 km of the fault trace: displacement sufficiently large that the change in range across a radar pixel exceeds one fringe per pixel, coherence is lost. http://earth.esa.int/applications/data_util/ndis/equake/land2.htm » SAR interferometry is not a suitable technique to measure near fault displacements The 1992 Landers earthquake revisited: Profile in offsets and elastic modeling show good agreement From: [6] - Measuring earthqakes from optical satellite images, Van Puymbroeck, Michel, Binet, Avouac, Taboury - Applied Optics Vol. 39, No 20, 10 July 2000 Other applications of the technique, see [4], [5]. » Fault ruptures can be imaged from this technique Applying the precise rectification algorithm + subpixel correlation: The 1999 Hector-Mine earthquake (Mw 7.1, California) Obtaining the Data (available in ENVI file Format. Load banbs as gray scale images. Bands are: N/S offsets, E/W offsets, SNR): Raw and filtered results: HectorMine.zip Pre-earthquake image: SPOT 4, acquisition date: 08-17-1998 Ground resolution: 10m Post-earthquake image: SPOT 2, acquisition date: 08-18-2000 Ground resolution: 10m Offsets measured from correlation: Correspond to sub-pixel offsets in the raw images. Correlation windows: 32 x 32 pixels 96m between two measurements So far we have: - A precise mapping of the rupture zone: the offsets field have a resolution of 96 m, - Measurements with a subpixel accuracy (displacement of at most 10 meters), - Improved the global georeferencing of the images with no GPS measurements, - Improved the processing time since the GCP selection is automatic, - Suppressed the main attitude artifacts. The profiles do not show any long wavelength deformations (See Dominguez et al. 2003) We notice: - Linear artifacts in the along track direction due to CCD misalignments, Schematic of a DIVOLI showing four CCD linear arrays. - Some topographic artifacts: the image resolution is higher than the DEM one, - Several decorrelations due to rivers and clouds, - High frequency noise due to the noise sensitivity of the Fourier correlator (See Van Puymbroeck et al.). Conclusion Subpixel correlation technique has been improved to overcome most of its limitations: » Precise rectification and co-registration of the images, » No more topographic effects (depending on the DEM resolution), » No need for GPS points – independent and automatic algorithm, » Better spatial resolution (See Van Puymbroeck et al.) To be improved: » Stripes due to the CCD’s misalignment, » high frequency noise from the correlator, » Process images with corrupted telemetry. » The subpixel correlation technique appears to be a valuable complement to SAR interferometry for ground deformation measurements. References: [1] SPOT 5 geometry handbook: ftp://ftp.spot.com/outgoing/SPOT_docs/geometry_handbook/S-NT-73-12-SI.pdf [2] SPOT User's Handbook Volume 1 - Reference Manual: ftp://ftp.spot.com/outgoing/SPOT_docs/SPOT_User's Handbook/SUHV1RM.PDF [3] SPOT 5 Technical Summary ftp://ftp.spot.com/outgoing/SPOT_docs/technical/spot5_tech_slides.ppt [4] Dominguez S., J.P. Avouac, R. Michel Horizontal co-seismic deformation of the 1999 Chi-Chi earthquake measured from SPOT satellite images: implications for the seismic cycle along the western foothills of Central Taiwan, J. Geophys. Res., 107, 10 1029/2001JB00482, 2003. [5] Michel, R. et J.P., Avouac, Deformation due to the 17 August Izmit earthquake measured from SPOT images, J. Geophys. Res., 107, 10 1029/2000JB000102, 2002. [6] Van Puymbroeck, N., Michel, R., Binet, R., Avouac, J.P. and Taboury, J. Measuring earthquakes from optical satellite images, Applied Optics Information Processing, 39, 23, 3486–3494, 2000. Publications: Leprince S., Barbot S., Ayoub F., Avouac, J.P. Automatic, Precise, Ortho-rectification and Co-registration for Satellite Image Correlation, Application to Seismotectonics. To be submitted. Conferences: F Levy, Y Hsu, M Simons, S Leprince, J Avouac. Distribution of coseismic slip for the 1999 ChiChi Taiwan earthquake: New data and implications of varying 3D fault geometry. AGU 2005 Fall meeting, San Francisco. M Taylor, S Leprince, J Avouac. A Study of the 2002 Denali Co-seismic Displacement Using SPOT Horizontal Offsets, Field Measurements, and Aerial Photographs. AGU 2005 Fall meeting, San Francisco. Y Kuo, F Ayoub, J Avouac, S Leprince, Y Chen, J H Shyu, Y Kuo. Co-seismic Horizontal Ground Slips of 1999 Chi-Chi Earthquake (Mw 7.6) Deduced From Image-Comparison of Satellite SPOT and Aerial Photos. AGU 2005 Fall meeting, San Francisco. source: http://www.tectonics.caltech.edu/geq/spot_coseis/
  43. 1 point
    please elaborate, what do you plan on using remote sensing data to make some animation? please add the details simple example for their functions can be see here: http://animove.org/wp-content/uploads/2019/04/Daniel_Palacios_animate_moveVis.html
  44. 1 point
    Google says it has built a computer that is capable of solving problems that classical computers practically cannot. According to a report published in the scientific journal Nature, Google's processor, Sycamore, performed a truly random-number generation in 200 seconds. That same task would take about 10,000 years for a state-of-the-art supercomputer to execute. The achievement marks a major breakthrough in the technology world's decadeslong quest to use quantum mechanics to solve computational problems. Google CEO Sundar Pichai wrote that the company started exploring the possibility of quantum computing in 2006. In classical computers, bits can store information as either a 0 or a 1 in binary notation. Quantum computers use quantum bits, or qubits, which can be both 0 and 1. According to Google, the Sycamore processor uses 53 qubits, which allows for a drastic increase in speed compared with classical computers. The report acknowledges that the processor's practical applications are limited. Google says Sycamore can generate truly random numbers without utilizing pseudo-random formulas that classical computers use. Pichai called the success of Sycamore the "hello world" moment of quantum computing. "With this breakthrough we're now one step closer to applying quantum computing to—for example—design more efficient batteries, create fertilizer using less energy, and figure out what molecules might make effective medicines," Pichai wrote. IBM has pushed back, saying Google hasn't achieved supremacy because "ideal simulation of the same task can be performed on a classical system in 2.5 days and with far greater fidelity." On its blog, IBM further discusses its objections to the term "quantum supremacy." The authors write that the term is widely misinterpreted. "First because, as we argue above, by its strictest definition the goal has not been met," IBM's blog says. "But more fundamentally, because quantum computers will never reign 'supreme' over classical computers, but will rather work in concert with them, since each have their unique strengths." News of Google's breakthrough has raised concerns among some people, such as presidential hopeful Andrew Yang, who believe quantum computing will render password encryption useless. Theoretical computer science professor Scott Aaronson refuted these claims on his blog, writing that the technology needed to break cryptosystems does not exist yet. The concept of quantum computers holding an advantage over classical computers has dated back to the early 1980s. In 2012, John Preskill, a professor of theoretical physics at Caltech, coined the term "quantum supremacy." source: https://www.npr.org/2019/10/23/772710977/google-claims-to-achieve-quantum-supremacy-ibm-pushes-back
  45. 1 point
    deck.gl (developed by Uber) is a WebGL-powered framework for visual exploratory data analysis of large datasets. deck.gl is designed to make visualization of large data sets simple. It enables users to quickly get impressive visual results with limited effort through composition of existing layers, while offering a complete architecture for packaging advanced WebGL based visualizations as reusable JavaScript layers. The basic idea of using deck.gl is to render a stack of visual overlays, usually (but not always) over maps. To make this simple concept work, deck.gl handles a number of challenges: Handling of large data sets and performant updates Interactive event handling such as picking Cartographic projections and integration with underlying map A catalog of proven, well-tested layers Easy to create new layers or customize existing layers Tutorials Getting started Uber's Vis.gl in Medium
  46. 1 point
    Check my latest fixes (updated 15th April 2019) - http://www.mediafire.com/file/61joa3j8u4e51ii/list.txt
  47. 1 point
    You may find such NDVI data via satellite imagery service called LandViewer. This tool has a vast database of satellite imagery that is publicly available and is updated on a regular basis. You may set any Index you need to analyze the area of your needs or create any Index of your own. Besides that there are already ready-made tools for obtaining multispectral indices, flexible processing of data on AOI, elementary clustering, using a raster calculator, visualization of scenes in 3D using digital elevation models, changes in territories based on multi-temporal multispectral analysis, as well as creating ready-made animations of changes in terrain and so much more. Here’s a brief guide to types satellite data that can be found on LandViewer. High resolution satellite imagery: SPOT 6, 7 (up to 1.5 m/pxl) SPOT 5 (up to 2.5 m/pxl) Pléiades 1A, 1B (up to 0.5 m/pxl) KOMPSAT-2 (up to 1 m/pxl) KOMPSAT-3А (up to 0.4 m/pxl) KOMPSAT-3 (up to 0.5 m/pxl) SuperView-1 (up to 0.5 m/pxl) Both optical and radar data is available — with global coverage, and short revisiting period that varies from 2 to 5 days. Low & medium resolution imagery: Landsat 4 - archive 1982-1993 Landsat 5 - archive 1984-2013 Landsat 7 - archive since 1999 MODIS - archive since 2012 Landsat 8 - archive since 2013 Sentinel-1 - archive since 2014 Sentinel-2 - archive since 2015 An example of such imagery can be seen below: https://eos.com/landviewer/?lat=33.39447&amp;lng=52.68974&amp;z=11&amp;side=R&amp;slider-id=LV-TEM4-MTYz-MDM3-MjAx-MzM2-NExH-TjAw&amp;slider-b=Red,Green,Blue&amp;slider-anti&amp;slider-pansharpening&amp;id=LV-TEM4-MTYz-MDM3-MjAx-MzM2-NExH-TjAw&amp;b=NIR,Red&amp;expression=(B5-B4)%2F(B5%2BB4)&amp;anti&amp;pansharpening
  48. 1 point
    Esri, the global leader in location intelligence, today announced the acquisition of indoo.rs GmbH, a world-leading provider of Indoor Positioning System (IPS) technology and Esri partner. The indoo.rs software will become part of Esri’s ArcGIS Indoors, a new mapping product that enables interactive indoor mapping of corporate facilities, retail and commercial locations, airports, hospitals, event venues, universities, and more. The acquisition will also provide users of Esri’s ArcGIS platform with imbedded IPS location services to support indoor mapping and analysis. The indoo.rs headquarters will also serve as a new Esri R&D center based in Vienna, Austria focused on cutting-edge IPS capability. The capability to accurately map, manage, navigate, and plan indoor spaces is a rapidly emerging market that promises to decrease costs, increase safety, and provide users of indoor spaces with a better workplace experience. ArcGIS Indoors does this by providing floor-aware 3D maps and focused apps to support a variety of workplace and facility users, including owner/operators, maintenance and service personnel, security staff, employees, and visitors. “indoo.rs is a leading provider of IPS software and services, working with organizations across the globe such as international hub airports, major rail stations, and corporate headquarters, and I am excited to welcome the company to the Esri family,” said Brian Cross, Esri director of professional services. “indoo.rs’ technology, experience, and leadership in the IPS field will be of tremendous benefit to our customers who want to bring the power of GIS to indoor spaces.” The new Vienna-based Esri R&D center will also provide support for IPS within ArcGIS Indoors and across the ArcGIS line of products. Existing indoo.rs customers will now have access to ArcGIS, adding the most powerful GIS software to their indoor mapping uses. “Becoming an integral part of Esri’s product catalog allows us to continue the provision of our services at the highest professional level,” said Bernd Gruber, founder of indoo.rs. “It also fosters new and exciting future developments as well as securing our leading-edge approach.” “We have seen the IPS market skyrocketing over the last few years,” said Rainer Wolfsberger, CEO of indoo.rs, “and our enterprise customers showed a high demand for deep integration of IPS technology to release the benefits of such a solution at all levels of their organization.” The initial release of ArcGIS Indoors will include the acquired indoo.rs IPS capability to enable ArcGIS Indoors mobile apps to work with iBeacon-based IPS systems, which provides “blue dot” accuracy on mobile devices. ArcGIS Indoors supports other IPS formats such as Apple’s indoor position service, and will add support for other IPS providers in coming releases. source: https://www.geospatialworld.net/news/esri-acquires-indoo-rs-and-announces-arcgis-indoors-release/
  49. 1 point
    Taking picture of a stranger has become easier though (oh I was just using the map, lady !!). 😉
  50. 1 point
    Hi, Please check out this tool - https://www.whatiswhere.com, which can be very useful in your research. Features: * OpenStreetMap based search which allows you to apply more than 1 criteria at once * Negative conditions (e.g. you could search for areas where some type of POI does not exist) * Access to global postal code information * EXPORT RESULTS TO CSV, which can be then uploaded to your GIS * Re-use of search projects Thanks, Andrei, WhatIsWhere www.whatiswhere.com


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