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  1. 4 points
    Here is an interesting review: http://www.50northspatial.org/uav-image-processing-software-photogrammetry/ 😉😊
  2. 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
  3. 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]
  4. 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
  5. 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
  6. 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
  7. 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.
  8. 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.
  9. 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
  10. 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.
  11. 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/
  12. 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
  13. 2 points
    just found this interesting articles on Agisoft forum : source: https://www.agisoft.com/forum/index.php?topic=7851.0
  14. 2 points
    one of my favorite image hosting, , this is their announcement : Rest in Peace TinyPic
  15. 2 points
    not necessary to excel environment but : https://github.com/orbisgis/h2gis/wiki/4.2-LibreOffice
  16. 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.
  17. 2 points
    multifunction casing. you can run 3d games and grating cheese for your hamburger. excelent thought apple as always LOL
  18. 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/
  19. 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.
  20. 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
  21. 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/
  22. 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 😁
  23. 2 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
  24. 1 point
  25. 1 point
    Five new remote-sensing satellites were sent into planned orbit from the Jiuquan Satellite Launch Center in northwest China's Gobi Desert Thursday. The five satellites were launched by a Long March-11 carrier rocket at 2:42 p.m. (Beijing Time). The satellites belong to a commercial remote-sensing satellite constellation project "Zhuhai-1," which will comprise 34 micro-nano satellites, including video, hyperspectral, and high-resolution optical satellites, as well as radar and infrared satellites. The carrier rocket was developed by the China Academy of Launch Vehicle Technology, and the satellites were produced by the Harbin Institute of Technology and operated by the Zhuhai Orbita Aerospace Science and Technology Co. Ltd. Thursday's launch was the 311th mission for the Long March series carrier rockets. The newly launched satellites comprise four hyperspectral satellites with 256 wave-bands and a coverage width of 150 km, and a video satellite with a resolution of 90 centimeters. The Zhuhai-1 hyperspectral satellites have the highest spatial resolution and the largest coverage width of their type in China. The data will be used for precise quantitative analysis of vegetation, water and crops, and will provide services for building smart cities, said Orbita, the largest private operator of hyperspectral satellites in orbit. The company aims to cooperate with government organizations and enterprises to expand the big data satellite services. source: https://www.spacedaily.com/reports/China_launches_new_remote_sensing_satellites_999.html
  26. 1 point
    sorry i cant see your picture, our country block imgur, really suck 😷
  27. 1 point
    Simple Analysis of Vegetative Trends in Earth Engine - SAVETREE - is a tool developed in Google Earth Engine for the Lassen Volcanic National park, it estimates tree mortality by fitting a linear trend to time serries data of a user chosen spectral index. The user can export their new map in the form of TIFF files,add historic fire layers, and the user can produce graphs which view the values in the time series for a particular pixel by clicking on the layer. Running SAVETREE Hit the “run” button in the center panel to make the widget appear. Using SAVETREE the user can do the following things: * Spectral Index: Choose from NDMI, NDVI, NDWI or NBR to select which spectral index you would like to create a linear regression layer for. The default is NDMI. * Area of interest: Choose from Lassen Volcanic National Park, Lassen National Forest, DEVELOP T2 Study Area, the Badger Planning area or choose Your asset (below) to perform the analysis on an asset you load yourself see Loading an Asset for instructions on loading your own asset. The default is LVNP. * End year and duration: The year must be in YYYY format, it is the last year of the duration of the analysis. The duration should be a number less than 20, with the most meaningful results coming from 3-7 years, it is the number of years it will create the time series for. For example, if you put in 1990 and a duration of 3, the analysis will be run on 1988, 1989, and 1990. The defaults are 2016 and 5. * Add Coefficient map: Performs the coefficient trend map analysis on the spectral index and area of interest for the duration you supplied ending with the year you specified and adds that layer to the map. * Add Bivariate map: Performs the Bivariate map analysis on the spectral index and area of interest for the duration you supplied ending with the year you specified and adds that layer to the map. * Reset Map: Clears all layers. Note: it does not reset the area of interest or any items in the widget. To reset the area of interest, choose a different area of interest from the dropdown before running a new analysis. * Fire history start and end years: These years must be in YYYY format. These numbers create a filter for the fire history data where the only data to be added to the map will be fires or treatments that occurred during those years. * Fire History Dataset: Select from FRAP Statewide Wildfire Dataset, RX fire, Other treatment, or load your own fire data asset. To load your own asset see Loading an Asset. The wildfire, rx fire and other treatments are FRAP datasets, for more details on the FRAP data and for the most up-to-date data sets please go to http://frap.fire.ca.gov/projects/fire_data/fire_perimeters_index * Export Coefficient Map: Exports the Coefficient trend layer as a TIFF file. See Exporting a Layer to get details on how to export layers to your Google Drive. * Export Bivariate Map: Exports the Bivariate map layer as a TIFF file. See Exporting a Layer to get details on how to export layers to your Google Drive. * Change Inspector: Click on any part of the Coefficient Trend or Bivariate Map layers and a graph of the change during each year for your duration for that particular point will appear at the bottom of the widget. Click the little box with the arrow in the upper right hand corner of the graph to open the graph in a new tab. You can download this graph from this new tab. SAVETREE was developed over two terms with DEVELOP: * Authors v1.0: Joshua Verkerke, Anna McGarrigle, John Dilger * Authors v2.0: Heather Myers, Anna McGarrigle, Peter Norton, Andrea Ferrer Download code
  28. 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
  29. 1 point
    The British Geological Survey (BGS) has amassed one of the world’s premier collections of geologic samples. Housed in three enormous warehouses in Nottingham, U.K., it contains about 3 million fossils gathered over more than 150 years at thousands of sites across the country. But this data trove “was not really very useful to anybody,” says Michael Stephenson, a BGS paleontologist. Notes about the samples and their associated rocks “were sitting in boxes on bits of paper.” Now, that could change, thanks to a nascent international effort to meld earth science databases into what Stephenson and other backers are describing as a “geological Google.” This network of earth science databases, called Deep-time Digital Earth (DDE), would be a one-stop link allowing earth scientists to access all the data they need to tackle big questions, such as patterns of biodiversity over geologic time, the distribution of metal deposits, and the workings of Africa’s complex groundwater networks. It’s not the first such effort, but it has a key advantage, says Isabel Montañez, a geochemist at University of California, Davis, who is not involved in the project: funding and infrastructure support from the Chinese government. That backing “will be critical to [DDE’s] success given the scope of the proposed work,” she says. In December 2018, DDE won the backing of the executive committee of the International Union of Geological Sciences, which said ready access to the collected geodata could offer “insights into the distribution and value of earth’s resources and materials, as well as hazards—while also providing a glimpse of the Earth’s geological future.” At a meeting this week in Beijing, 80 scientists from 40 geoscience organizations including BGS and the Russian Geological Research Institute are discussing how to get DDE up and running by the time of the International Geological Congress in New Delhi in March 2020. DDE grew out of a Chinese data digitization scheme called the Geobiodiversity Database (GBDB), initiated in 2006 by Chinese paleontologist Fan Junxuan of Nanjing University. China had long-running efforts in earth sciences, but the data were scattered among numerous collections and institutions. Fan, who was then at the Chinese Academy of Sciences’s Nanjing Institute of Geology and Paleontology, organized GBDB around the stacks of geologic strata called sections and the rocks and fossils in each stratum. Norman MacLeod, a paleobiologist at the Natural History Museum in London who is advising DDE, says GBDB has succeeded where similar efforts have stumbled. In the past, he says, volunteer earth scientists tried to do nearly everything themselves, including informatics and data management. GBDB instead pays nonspecialists to input reams of data gleaned from earth science journals covering Chinese findings. Then, paleontologists and stratigraphers review the data for accuracy and consistency, and information technology specialists curate the database and create software to search and analyze the data. Consistent funding also contributed to GBDB’s success, MacLeod says. Although it started small, Fan says GBDB now runs on “several million” yuan per year. Earth scientists outside China began to use GBDB, and it became the official database of the International Commission on Stratigraphy in 2012. BGS decided to partner with GBDB to lift its data “from the page and into cyberspace,” as Stephenson puts it. He and other European and Chinese scientists then began to wonder whether the informatics tools developed for GBDB could help create a broader union of databases. “Our idea is to take these big databases and make them use the same standards and references so a researcher could quickly link them to do big science that hasn’t been done before,” he says. The Beijing meeting aims to finalize an organizational structure for DDE. Chinese funding agencies are putting up $75 million over 10 years to get the effort off the ground, Fan says. That level of support sets DDE apart from other cyberinfrastructure efforts “that are smaller in scope and less well funded,” Montañez says. Fan hopes DDE will also attract international support. He envisions nationally supported DDE Centers of Excellence that would develop databases and analytical tools for particular interests. Suzhou, China, has already agreed to host the first of them, which will also house the DDE secretariat. DDE backers say they want to cooperate with other geodatabase programs, such as BGS’s OneGeology project, which seeks to make geologic maps of the world available online. But Mohan Ramamurthy, project director of the U.S. National Science Foundation–funded EarthCube project, sees little scope for collaboration with his effort, which focuses on current issues such as climate change and biosphere-geosphere interactions. “The two programs have very different objectives with little overlap,” he says. Fan also hopes individual institutions will contribute, by sharing data, developing analytical tools, and encouraging their scientists to participate. Once earth scientists are freed of the drudgery of combing scattered collections, he says, they will have time for more important challenges, such as answering “questions about the evolution of life, materials, geography, and climate in deep time.” source: https://www.sciencemag.org/news/2019/02/earth-scientists-plan-meld-massive-databases-geological-google
  30. 1 point
    NRM638 Python Scripting with ArcGIS10 Objectives: To learn basic Python scripting independent of GIS To learn how to write stand-alone geoprocessing scripts for ArcGIS10 To learn how to make and document script tools for ArcGIS10 To learn how to automate mapping tasks using arcpy.mapping http://nrm.salrm.uaf.edu/~dverbyla/nrm638/
  31. 1 point
    really nice, is it possible to leverage into forecast? that would be interesting
  32. 1 point
    update: this how i do till i post this update 1. disable hardware acceleration 2. Turn off all compatibility settings except for Run This Program As Administrator (point 4 on above article) 3. Stop the Windows Presentation Foundation Font Cache (point 5 on above article). I saw little increase on performance, the workspace almost playable now, but i still find the lag when scroll it.
  33. 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, 😉
  34. 1 point
  35. 1 point
    This is a very interesting mapping platform for the agriculture community. The Belarus-based startup platform uses Sentinel-2 data and AI to instantly delineate thousands of crop fields and status of 20 plus crops in USA and Europe. They also have smartphone-based apps which you can use to find these solutions for your field as well. The platform applies Machine Learning, which constantly improves the service as more data and feedback is collected. Considering that a mind-boggling 376,835,301 hectares of fields across Europe and the USA have already been analyzed and catalogued, the system has reached a remarkable level of maturity. OneSoil — a Copernicus-enabled start-up from Belarus Check out their interactive map. Onesoil homepage
  36. 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
  37. 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
  38. 1 point
    Dapat data shp untuk peta multirawan se Indonesia. silahkan dicek https://drive.google.com/file/d/1anG5xcA9uMo1P9jLeppBvEXpaExJsLhk/view untested, lupa dapet darimana link ini
  39. 1 point
    Look forward to play mario and legend of zelda on those device 😁
  40. 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
  41. 1 point
    This year at WWDC 2019, Apple unveiled a cheese grater and called it the new Mac Pro. But, to see the 2019 Mac Pro once is enough to remember it for a long time. Specs - According to Apple website, you can spend as much as $35,000+ in it !! 🤯😬 Source
  42. 1 point
    The state of Alaska is beautiful and wild — no wonder it is called the “Last Frontier”. The land has more than 130 volcanoes that pose a grave threat to the residents. A joint project by National Oceanic and Atmospheric Administration (NOAA) and NASA (National Aeronautics and Space Administration) has given scientists and forecasters a platform to protect people from volcanic ash. This is one of the many case studies highlighted by the space agency on its new website, SpaceforUS, which intends to highlight how NASA has used its earth observation data to better the living conditions of people in all 50 states of America. NASA, for the past six decades, the agency has used interpretations from the space to understand the “Blue Planet” better. By using its fleet of space technologies, it has improved the lives of the people of America. Some 25,000 flights flights fly over Alaskan volcanoes which can be even more hazardous during eruptions and when volcanoes discharge volcanic ash. An joint initiative by NOAA and NASA, the project tracks clouds and guides regulators and airlines. Through this new and interactive website, SpaceforUS, NASA wishes to highlight the innumerable ways in which its earth observations has helped administrators take informed decisions in the areas of public health, disaster response and environmental protection. The site, also being termed NASA’s communication project, explores the stories behind the innovative technology, ground-breaking insights and extraordinary collaborations. Single platform SpaceforUS has a total of 56 stories that illustrate NASA’s science and the impact it has managed to have in all 50 states, including the District of Columbia, Puerto Rico and regions along the Atlantic, Pacific, Gulf of Mexico and the Great Lakes. On the website, readers can browse stories on animals, disasters, energy, health, land and water either by state or by topics. The website showcases the power of earth observation through state-by-state project examples — from guiding pilots around hazardous volcanic ash plumes over Alaska to first responders to devastating hurricanes in North Carolina. During Hurricane Rita, NASA created high quality satellite images that identified power averages guiding first responders for life saving aid. On SpaceforUS, each click brings to the readers a story about the different ways in which people are using NASA data in their day-to-day lives. Open Data To all those seeking solutions to imperative global issues, NASA also provides free and open earth observation data on issues related to changing freshwater availability, food security and human health. NASA’s Applied Sciences platform provides financial assistance to those projects that facilitate innovative uses of NASA Earth science data to ensure that well-informed decisions are made to not only strengthen America’s economy, but to also improve the quality of life globally. In the state of Arizona, NASA Earth observations have already identified the hottest areas around the Phoenix metropolitan area making civic planning and environmental monitoring a lot easier. website : https://www.nasa.gov/SpaceforUS/
  43. 1 point
    Hello everyone, I'm Halid, from Bosnia and Herzegovina, geodetic engineer. I don't have much experience in GIS area, but I'll give my best to contribute, and hope I'll get info that I need also :))
  44. 1 point
    Check my latest fixes (updated 15th April 2019) - http://www.mediafire.com/file/61joa3j8u4e51ii/list.txt
  45. 1 point
    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/
  46. 1 point
    TopoMapCreator (beta) A set of GIS tools that helps creating topographic map TopoMapCreatorThe TopoMapCreator consists of of 5 Programs: MapCreator, GeoToolsCmd, TopoMap, EcwToMobile and ExtendedMapCreator. More information for example about how to install it, you find under TopoMapCreator. Now read, what the 5 Programs are doing: 1. ExtendedMapCreatorExtendedMapCreator is a Desktop-Program, that creates "Topographic Maps" from OSM, NASA and ESA. You simply define a map extent by dragging over a browsable word map, click on start and wait till the GeoTIFF, ECW, GALILEO, ORUXMAPS or NAVIMAP files got created. ExtendedMapCreator is based on the Mapnik-Renderer, nevertheless all data downloading and processing is fully automatic. Click on ExtendedMapCreator to read more about the Program! 2. MapCreatorMapCreator is a GIS toolset. The tools have the common goal to create Topographic Maps. Currently it consists of 10 tools: The GeoreferencingTool georeferences scanned map series. The EcwHillshaderTool adds hillshades to a map. The SrtmHillshadesTool creates hillshades. The EcwToMobileTool converts a map to a Smartphone App Format. The GeonamesToShapeTool creates a shapefile from a GeoNames file. The ShapeToOsmTool creates an OSM file from shapefiles. The WarpEcwTool warps (reprojects) huge maps. The RussianMapsCreatorTool downloads and processes Russian maps. The QgisToEcwTool makes a Print-Screen of a qGis view. The USGSTopoMapTool downloads and processes USGS maps. Click on any of the tools to know more about it! 3. GeoToolsCmdGeoToolsCmd provides the same GIS toolset as the MapCreator, but accessible over the Command-Prompt. With GeoToolsCmd it is possible to write batch files. 4. TopoMapTopoMap is simple Desktop-Program to download specific Maps. 5. EcwToMobileEcwToMobile is a simple Desktop-Program to convert an ECW file to a Mobile App Format. The program is redundant to the EcwToMobileTool. darksabersan.
  47. 1 point
    Hi Darksabersan, the links are dead, could you reactivate and perhaps upload to a different site like Mega.nz, please? thanks
  48. 1 point
    It does really work. You probably missed one step or two during the process. One method you can do to check whether the file is NT or non-NT is by using GPSMapEdit, because it won't open NT format otherwise it will. Here I show you the snapshot when I try to open an NT format file in GPSMapEdit. Here is the same file with a non-NT format. OK, since I cannot edit my previous post, I am going to re-explain the procedures here. I enclose some snapshots for the clarity. Open the GMAP Tool Add the NT formatted img file/s (in my case, filename is 62320070.img) Go to Split tab and create subfiles. Click Split All. Download Garmin-GMP-extractor.exe tool and then put it in the same folder with your working files. Drag the GMP file into the Garmin GMP extractor tools. It will explod/extract the GMP fiel into five type of subfiles (.LBL, .NET, .NOD, .RGN and .TRE) Back to GMAPTool. Add those subfiles. Go to Join tab. Name the output file and directory. We can give mapset name. And the click Join all. FINALLY, the result is another img file with non-NT format. If you watch closely there is a slight difference of filesize between both files.
  49. 1 point
    I getting some images of a oil palm estate taken from a DIY drone. The image is stitch and mosaic. Has anyone done automated tree counting on these images. Seen some examples using eCognition but that was with multi-spectral images.
  50. 1 point
    It might not be a very good idea to use ArcGIS but somehow we can use this software for tree counting! My method is quite simple: 1. Get a piece of hi-res image from Google Earth 2. Open the image in ArcGIS 3. Make sure toolbar "Image Classification" is checked (turned on). 4. From "Image Classification" toolbar, select "Classification" --> "Iso Cluster Unsupervised Classification" command. 5. Chose "2" for "Number of Classes" in the dialogbox. By doing it, you will have only trees (or something else similar) and non-tree objects in the result. Run it and see the result (below). C'mon, it also shows "Google Earth" trademark on the result (LOL). 6. Now go to ArcGIS ToolBox, select "Conversion" --> "From Raster" --> "Raster to Polygon". In the dialog box, check to "Simplify Polygion (optional)" checkbox. 7. The result is as below 8. Mask the area where you want to count the trees, then count the number of polygons within that mask. Bingo!!!! If you want to do it better, you can do some pre-processing steps for your image. Also, when you have the polygon layer, you can try to simplify the layer again (using Eliminate, Integrate... functions) before counting trees. Enjoy ESRI.
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