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  1. 4 points
    Here is an interesting review: http://www.50northspatial.org/uav-image-processing-software-photogrammetry/ 😉😊
  2. 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
  3. 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.
  4. 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
  5. 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.
  6. 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.
  7. 2 points
    multifunction casing. you can run 3d games and grating cheese for your hamburger. excelent thought apple as always LOL
  8. 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/
  9. 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.
  10. 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
  11. 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/
  12. 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 😁
  13. 2 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. 1 point
    if they do pull it off then they would be the third Titan in the mobile market, something Microsoft mobile failed miserably
  15. 1 point
    This is the real challenges for Huawei how to convince their user to use this new operating system. But nothing is impossible...
  16. 1 point
    Free Urban Analysis Toolbox Contains ARCPY tools for Urban Planners. Now its Developing and of-coarse FREE. At the moment you can download it at this ADDRESS. Remember before use check out for latest version. 2 tools are available: Land Use Entropy Index Calculator & Modified Huff Gravity Model (added custom Distance Decay Functions). Hope you enjoy Developer is [email protected] which is unknown
  17. 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
  18. 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/
  19. 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 :))
  20. 1 point
    Check my latest fixes (updated 15th April 2019) - http://www.mediafire.com/file/61joa3j8u4e51ii/list.txt
  21. 1 point
    The picture like above are not actual picture of Black Hole (it is a wallpaper 😁 ). Early Wednesday (April 10, 2019) morning, a huge collaboration of scientists are expected to release the first images of the event horizon of a black hole, constructed from data gathered by observatories all over the globe. Combined, the telescopes created a virtual telescope as big as the Earth itself that’s powerful enough to capture enough data from the supermassive black hole at the center of our galaxy. Tomorrow, we may finally see all of that data pieced together. Black holes, by their nature, are impossible to see with the naked eye since they are so dense that no light can escape them. Instead, any images that will be released will be the silhouette of a black hole, an outline against all of the super bright, hot gas that is thought to surround these weird celestial objects. It will be as close as we can get to a picture of a black hole’s infamous “event horizon,” the boundary of a black hole where the gravitational pull is so great that there is no escape. The Event Horizon Telescope actually observed two black holes during one week in April 2017: Sagittarius A*, the supermassive black hole at the center of our Milky Way galaxy, and M87, which is thought to be in the center of a nearby galaxy called Virgo. Both of these objects are thought to be incredibly dense. Sagittarius A*, or SgrA*, is thought to be 4 million times more massive than our Sun and 30 times larger than the star. But because it is so far away — a distance of about 26,000 light-years — the black hole appears to telescopes on Earth as though it is about the size of small ball on the surface of the Moon, according to the collaboration. To focus in on the massive but distant objects, the Event Horizon team employed telescopes in Chile, Hawaii, Arizona, the South Pole, and other locations around the globe. Each telescope measured the radiation coming from the large swaths of gas and dust that are thought to surround black holes. These clouds of gas heat up to billions of degrees, and because the material is so hot, they emit lots of radiation, which the team could then observe from Earth. All of that data was then combined in a supercomputer to make an image that looked as if it came from a single, giant telescope. “This is a picture you would have seen if you had eyes as big as the Earth and were observing in radio,” Psaltis says. Getting all of this data isn’t easy. In fact, the reason it’s taken so long to mount a project of this scale is that the telescopes gather so much information — about one petabyte, or a million gigabytes — of data each night of observing. It’s the largest amount of recording of any other experiment in physics or astronomy, says Psaltis. The team had to wait for hard drive technology to evolve so that it could hold the sheer amount of data that the team was collecting. “Five years ago, you couldn’t buy enough hard drives to have a terabyte of data on a telescope,” says Psaltis. What that enormous amount of data shows could change our understanding of black holes. These objects are so dense that it’s thought that they actually leave an imprint on the surrounding space-time, warping gravity and creating strange effects on their surroundings, which scientists are still trying to understand. A picture of a black hole could tell us more about these odd happenings at the event horizon. - the Verge UPDATE - This is an actual Black Hole ! At the announcement at Washington’s National Press Club. “We now have visual evidence. We know that a black hole sits at the center of the M87 galaxy.” How they took the image First-ever picture of a black hole unveiled
  22. 1 point
    The U.S. Air Force’s second new GPS III satellite, bringing higher-power, more accurate and harder-to-jam signals to the GPS constellation, has arrived in Florida for launch. On March 18, Lockheed Martin shipped the Air Force’s second GPS III space vehicle (GPS III SV02) to Cape Canaveral for an expected July launch. Designed and built at Lockheed Martin’s GPS III Processing Facility near Denver, the satellite traveled from Buckley Air Force Base, Colorado, to the Cape on a massive Air Force C-17 aircraft. The Air Force nicknamed the GPS III SV02 “Magellan” after Portuguese explorer Ferdinand Magellan. GPS III is the most powerful and resilient GPS satellite ever put on orbit. Developed with an entirely new design, for U.S. and allied forces, it will have three times greater accuracy and up to eight times improved anti-jamming capabilities over the previous GPS II satellite design block, which makes up today’s GPS constellation. GPS III also will be the first GPS satellite to broadcast the new L1C civil signal. Shared by other international global navigation satellite systems, like Galileo, the L1C signal will improve future connectivity worldwide for commercial and civilian users. The Air Force began modernizing the GPS constellation with new technology and capabilities with the December 23, 2018 launch of its first GPS III satellite. GPS III SV01 is now receiving and responding to commands from Lockheed Martin’s Launch and Checkout Center at the company’s Denver facility. Lockheed Martin shipped the U.S. Air Force’s first GPS III to Cape Canaveral, Florida ahead of its expected July launch. (Photo: Lockheed Martin} “After orbit raising and antenna deployments, we switched on GPS III SV01’s powerful signal-generating navigation payload and on Jan. 8 began broadcasting signals,” Johnathon Caldwell, Lockheed Martin’s Vice President for Navigation Systems. “Our on orbit testing continues, but the navigation payload’s capabilities have exceeded expectations and the satellite is operating completely healthy.” GPS III SV02 is the second of ten new GPS III satellites under contract and in full production at Lockheed Martin. GPS III SV03-08 are now in various stages of assembly and test. The Air Force declared the second GPS III “Available for Launch” in August and, in November, called GPS III SV02 up for its 2019 launch. In September 2018, the Air Force selected Lockheed Martin for the GPS III Follow On (GPS IIIF) program, an estimated $7.2 billion opportunity to build up to 22 additional GPS IIIF satellites with additional capabilities. GPS IIIF builds off Lockheed Martin’s existing modular GPS III, which was designed to evolve with new technology and changing mission needs. On September 26, the Air Force awarded Lockheed Martin a $1.4 billion contract for support to start up the program and to contract the 11th and 12th GPS III satellite. Once declared operational, GPS III SV01 and SV02 are expected to take their place in today’s 31 satellite strong GPS constellation, which provides positioning, navigation and timing services to more than four billion civil, commercial and military users. source: https://www.satellitetoday.com/launch/2019/03/26/lockheed-martin-ships-second-gps-iii-satellite/
  23. 1 point
    TAU-0707 series GNSS module. (Photo: Allystar) Allystar Technology Co. Ltd. has launched its smallest multi-band multi-GNSS module, the TAU-0707 series. Within its 7.6 x 7.6 millimeter size, the TAU-0707 series module supports major GNSS constellations (GPS / Galileo / GLONASS / BeiDou / QZSS / IRNSS) and all civil bands (L1, L2, L5, L6). As the latest addition to Allystar’s GNSS portfolio, the TAU-0707 series module is a concurrent multi-band multi-GNSS receiver embedded with a cynosure III single-die standalone positioning chipset, which offers multi-frequency measurements to improve positioning accuracy and simplifies integration for third-party applications, said Shi Xian Yang, Allystar marketing manager. Moreover, Allystar also provides the built-in low-noise amplifier in the TAU-1010 series module, which offers the module with improved RF sensitivity and exceptional acquisition and tracking performance even in weak signal areas. With more and more satellites supporting L1/L5 signals, Allystar offers two modules to fully support all civil signals on the L5 band for the standalone market. The TAU1206-0707 and TAU1205-1010 are expected to be better in multipath mitigation mainly due to the higher chipping rate of L5 signals relative to L1 C/A code. L1/L5 band module for standalone market. For professional applications, module TAU1303-0707 comes with built-in support for standard RTCM protocol (MSM), supporting multi-band multi-system high-precision raw data output, including pseudorange, phase range, Doppler, SNR for any kind of third-party integration and application. Module with Raw data output for professional market. Allystar TAU series module offers superior accuracy thanks to the onboard 26-MHz temperature compensated crystal oscillator and a reduced time to first fix relying on its dedicated 32-KHz real-time clock oscillator. Based on 40-nm manufacturing processes of the Cynosure III GNSS chipset, it comes with very low power consumption at less than 40 mA. According to the company, engineering samples and a reference design of the Allystar TAU-0707 and TAU-1010 series module will be available in April. source: http://www.allystar.com/en/index.php?g=&amp;m=news&amp;a=newsinfo&amp;id=32
  24. 1 point
    realy sad news WorldWind team would like to inform you that starting April 5, 2019, NASA WorldWind project will be suspended. All the WorldWind servers providing elevation and imagery will be unavailable. While you can still download the SDKs from GitHub, there will be no technical support. If you have questions and/or concerns, please feel free to email at: [email protected] Update on March 21, 2019 - Answers to common questions about the suspension are available in the NASA WorldWind Project Suspension FAQ. source : https://worldwind.arc.nasa.gov/news/2019-03-08-suspension-notice/
  25. 1 point
    DARPA is seeking information on state-of-the-art technologies and methodologies for advanced mapping and surveying in support of the agency’s Subterranean (SubT) Challenge. Georeferenced data – geographic coordinates tied to a map or image – could significantly improve the speed and accuracy of warfighters in time-sensitive active combat operations and disaster-related missions in the subterranean domain. Today, the majority of the underground environments are uncharted or inadequately mapped, including human-made tunnels, underground infrastructure, and natural cave networks. Through the Request for Information, DARPA is looking for innovative technologies to collect highly accurate and reproducible ground-truth data for subterranean environments, which would potentially disrupt and positively leverage the subterranean domain without prohibitive cost and with less risk to human lives. These innovative technologies will allow for exploring and exploiting these dark and dirty environments that are too dangerous to deploy humans. “What makes subterranean areas challenging for precision mapping and surveying – such as lack of GPS, constrained passages, dark or dust-filled air – is similar to what inhibits safe and speedy underground operations for our warfighters,” said Timothy Chung, program manager in DARPA’s Tactical Technology Office (TTO). “Building an accurate three-dimensional picture is a key enabler to rapidly and remotely exploring and searching subterranean spaces.” DARPA is looking for commercial products, software, and services available to enable high-fidelity, 3D mapping and surveying of underground environments. Of interest are available technologies that offer high accuracy and high resolution, with the ability to provide precise and reproducible survey points without reliance on substantial infrastructure (e.g., access to global fixes underground). Additionally, relevant software should also allow for generated data products to be easily manipulated, annotated, and rendered into 3D mesh objects for importing into simulation and game engine environments. DARPA may select proposers to demonstrate their technologies or methods to determine feasibility of capabilities for potential use in the SubT Challenge in generating and sharing 3D datasets of underground environments. Such accurately georeferenced data may aid in scoring the SubT competitors’ performance in identifying and reporting the location of artifacts placed within the course. In addition, renderings from these data may provide DARPA with additional visualization assets to showcase competition activities in real-time and/or post-production. source : https://www.darpa.mil/news-events/2019-03-07
  26. 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
  27. 1 point
    cool, but costly. i would better recommend https://apps.sentinel-hub.com/eo-browser/?
  28. 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/
  29. 1 point
    I think it is more like asset management and navigation for indoor. This is helpful for managing personnel or assets within roofed infrastructure (ie. a large factory, multistorey building or underground construction site who cannot use GPS) by utilizing IoT devices and GIS software. I heard about similar tech when I was at Esri Singapore for few days.
  30. 1 point
    Taking picture of a stranger has become easier though (oh I was just using the map, lady !!). 😉
  31. 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
  32. 1 point
    I need to work on spatial interpolation using multivariate regression of observed AWS precipitation data by introducing variables such as slope, aspect etc. Is there any option in ARCGIS?
  33. 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.
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