Leaderboard

The open-source model will serve as the basis for future forest, crop and climate change-monitoring AI. NASA estimates that its Earth science missions will generate around a quarter million terabytes of data in 2024 alone. In order for climate scientists and the research community efficiently dig through these reams of raw satellite data, IBM, HuggingFace and NASA have collaborated to build an open-source geospatial foundation model that will serve as the basis for a new class of climate and Earth science AIs that can track deforestation, predict crop yields and rack greenhouse gas emissions. For this project, IBM leveraged its recently-released Watsonx.ai to serve as the foundational model using a year’s worth of NASA’s Harmonized Landsat Sentinel-2 satellite data (HLS). That data is collected by the ESA’s pair of Sentinel-2 satellites, which are built to acquire high resolution optical imagery over land and coastal regions in 13 spectral bands. For it’s part, HuggingFace is hosting the model on its open-source AI platform. According to IBM, by fine-tuning the model on “labeled data for flood and burn scar mapping,” the team was able to improve the model's performance 15 percent over the current state of the art using half as much data. "The essential role of open-source technologies to accelerate critical areas of discovery such as climate change has never been clearer,” Sriram Raghavan, VP of IBM Research AI, said in a press release. “By combining IBM’s foundation model efforts aimed at creating flexible, reusable AI systems with NASA’s repository of Earth-satellite data, and making it available on the leading open-source AI platform, Hugging Face, we can leverage the power of collaboration to implement faster and more impactful solutions that will improve our planet.” source: engadget

A new machine learning system can create height maps of urban environments from a single synthetic aperture radar (SAR) image, potentially accelerating disaster planning and response. Aerospace engineers at the University of the Bundeswehr in Munich claim their SAR2Height framework is the first to provide complete—if not perfect—three-dimensional city maps from a single SAR satellite. When an earthquake devastates a city, information can be in short supply. With basic services disrupted, it can difficult to assess how much damage occurred or where the need for humanitarian aid is greatest. Aerial surveys using laser ranging lidar systems provide the gold standard for 3D mapping, but such systems are expensive to buy and operate, even without the added logistical difficulties of a major disaster. Remote sensing is another option but optical satellite images are next to useless if the area is obscured by clouds or smoke. Synthetic aperture radar, on the other hand, works day or night, whatever the weather. SAR is an active sensor that uses the reflections of signals beamed from a satellite towards the Earth’s surface—the “synthetic aperture” part comes from the radar using the satellite’s own motion to mimic a larger antenna, to capture reflected signals with relatively long wavelengths. There are dozens of governmental and commercial SAR satellites orbiting the planet, and many can be tasked to image new locations in a matter of hours. However, SAR imagery is still inherently two-dimensional, and can be even trickier to interpret than photographs. This is partly due to an effect called radar layover where undamaged buildings appear to be toppling towards the sensor. “Height is a super complex topic in itself,” says Michael Schmitt, a professor at the University of the Bundeswehr. “There are a million definitions of what height is, and turning a satellite image into a meaningful height in a meaningful world geometry is a very complicated endeavor.” Schmitt and his colleague Michael Reclastarted by sourcing SAR images for 51 cities from the TerraSAR-X satellite, a partnership between the public German Aerospace Center and the private contractor Airbus Defence and Space. The researchers then obtained high quality height maps for the same cities, mostly generated by lidar surveys but some by planes or drones carrying stereo cameras. The next step was to make a one-to-one, pixel-to-pixel mapping between the height maps and the SAR images on which they could train a deep neural network. The results were amazing, says Schmitt. “We trained our model purely on TerraSAR-X imagery but out of the box, it works quite well on imagery from other commercial satellites.” He says the model, which takes only minutes to run, can predict the height of buildings in SAR images with an accuracy of around three meters—the height of a single story in a typical building. That means the system should be able to spot almost every building across a city that has suffered significant damage. Pietro Milillo, a professor of geosensing systems engineering at the University of Houston, hopes to use Schmitt and Recla’s model in an ongoing NASA-funded project on earthquake recovery. “We can go from a map of building heights to a map of probability of collapse of buildings,” he says. Later this month, Milillo intends to validate his application by visiting the site of an earthquake in Morocco last year that killed over 2,900 people. But the AI model is still far from perfect, warns Schmitt. It struggles to accurately predict the height of skyscrapers and is biased towards North American and European cities. This is because many cities in developing nations did not have regular lidar mapping flights to provide representational training data. The longer the gap between the lidar flight and the SAR images, the more buildings would have been built or replaced, and the less reliable the model’s predictions. Even in richer countries, “we’re really dependent on the slow revisit cycles of governments flying lidar missions and making the data publicly available,” says Carl Pucci, founder of EO59, a Virginia Beach, Va.-based company specializing in SAR software. “It just sucks. Being able to produce 3D from SAR alone would be really be a revolution.” Schmitt says the SAR2Height model now incorporates data from 177 cities and is getting better all time. “We are very close to reconstructing actual building models from single SAR images,” he says. “But you have to keep in mind that our method will never be as accurate as classic stereo or lidar. It will always remain a form of best guess instead of high-precision measurement.” source: ieee

Qualcomm Technologies and Xiaomi have verified meter-level positioning in the Xiaomi 12T Pro powered by the Snapdragon 8+ Gen 1 mobile platform, in Germany. Accuracy verification tests, including driving tests, were conducted by Qualcomm Technologies, Xiaomi, and Trimble in various scenarios such as open-sky rural roads and urban highways. The companies’ solutions demonstrated meter-level positioning variance at a 95% confidence level. This level of accuracy in a commercial smartphone is enabled through Qualcomm meter-level positioning for mobile in combination with Trimble RTX correction services. When integrated with Snapdragon mobile platforms, Trimble RTX enhances the phone’s positioning capabilities. Meter-level positioning accuracy can improve smartphone user experience in several scenarios, including mapping, driving, and other mobile applications. It enables greater accuracy when using ridesharing applications to identify pick-up locations for both driver and rider, fitness applications to track users’ movements, and in-vehicle real-time navigation applications for increased lane-level accuracy with greater map details and more accurate directions.

The European Space Agency (ESA) has greenlit the development of the NanoMagSat constellation, marking a significant advancement in the use of small satellites for scientific missions. NanoMagSat, a flagship mission spearheaded by Open Cosmos together with IPGP (Université Paris Cité, Institut de physique du globe de Paris, CNRS) and CEA-Léti, aims to revolutionise our understanding of Earth's magnetic field and ionospheric environment. As a follow on from ESA's successful Earth Explorer Swarm mission, NanoMagSat will use a constellation of three 16U satellites equipped with state-of-the-art instruments to monitor magnetic fields and ionospheric phenomena. This mission is joining the Scout family, a programme from ESA to deliver scientific small satellite missions within a budget of less than €35 million. The decision to proceed with NanoMagSat follows the successful completion of Risk Retirement Activities including the development of a 3m-long deployable boom and a satellite platform with exceptional magnetic cleanliness, key to ensuring state-of-the art magnetic accuracy. ESA’s Director of Earth Observation Programmes, Simonetta Cheli, said of this news: “We are very pleased to add two new Scouts to our Earth observation mission portfolio. These small science missions perfectly complement our more traditional existing and future Earth Explorer missions, and will bring exciting benefits to Earth.”

The 9.0 release add several new features, including a Google Maps source (finally!), improved WebGL line rendering, and a new symbol and text decluttering implementation. We also improved and broadened flat styles support for both WebGL and Canvas 2D renderers. For better developer experience, we made more types generic and fixed some issues with types. Backwards incompatible changes Improved render order of decluttered items Decluttered items in Vector and VectorTile layers now maintain the render order of the layers and within a layer. They do not get lifted to a higher place in the stack any more. For most use cases, this is the desired behavior. If, however, you've been relying on the previous behavior, you now have to create separate layers above the layer stack, with just the styles for the declutter items. Removal of Map#flushDeclutterItems() It is no longer necessary to call this function to put layers above decluttered symbols and text, because decluttering no longer lifts elements above the layer stack. To upgrade, simply remove the code where you use the flushDeclutterItems() method. Changes in ol/style Removed the ol/style/RegularShape's radius1 property. Use radius for regular polygons or radius and radius2 for stars. Removed the shape-radius1 property from ol/style/flat~FlatShape. Use shape-radius instead. GeometryCollection constructor ol/geom/GeometryCollection can no longer be created without providing a Geometry array. Empty arrays are still valid. ol/interaction/Draw The finishDrawing() method now returns the drawn feature or null if no drawing could be finished. Previously it returned undefined. page: https://github.com/openlayers/openlayers/releases/tag/v9.0.0

Added support for data types: GRUS L1C, L2A - Axelspace micro-earth observation satellite ISIS3 - USGS Astrogeology ISIS Cube, Version 3 PDS4 -NASA Planetary Data System, Version 4 New Spectral Hourglass Workflow and N-Dimensional Visualizer New Target Detection Workflow The Target Detection Workflow has been added to this release. Use the Target Detection Workflow to locate objects within hyperspectral or multispectral images that match the signatures of in-scene regions. The targets may be a material or mineral of interest, or man-made objects. New Dynamic Band Selection tool New Material Identification tool Updated and improved Endmember Collection tool New and updated ENVI Toolbox tools The following tools have been updated to use new ENVI Tasks: Adaptive Coherence Estimator Classification: A classification method derived from the Generalized Likelihood Ratio (GLR) approach. The ACE is invariant to relative scaling of input spectra and has a Constant False Alarm Rate (CFAR) with respect to such scaling. Constrained Energy Minimization Classification: A classification method that uses a specific constraint, CEM uses a finite impulse response (FIR) filter to pass through the desired target while minimizing its output energy resulting from a background other than the desired targets. Classification Smoothing: Removes speckling noise from a classification image. It uses majority analysis to change spurious pixels within a large single class to that class. Forward Minimum Noise Fraction: Performs a minimum noise fraction (MNF) transform to determine the inherent dimensionality of image data, to segregate noise in the data, and to reduce the computational requirements for subsequent processing. Inverse Minimum Noise Fraction: Transforms the bands from a previous Forward Minimum Noise Fraction to their original data space. Orthogonal Subspace Projection Classification: This classification method first designs an orthogonal subspace projector to eliminate the response of non-targets, then Matched Filter is applied to match the desired target from the data. Parallelepiped Classification: Performs a parallelepiped supervised classification which uses a simple decision rule to classify multispectral data. Spectral Information Divergence Classification: A spectral classification method that uses a divergence measure to match pixels to reference spectra. New and updated ENVI Tasks You can use these new ENVI Tasks to perform data-processing operations in your own ENVI+IDL programs: ConstrainedEnergyMinimization: Performs the Constrained Energy Minimization (CEM) target analysis. InverseMNFTransform: Transforms the bands from a previous Forward Minimum Noise Fraction to their original data space. MixtureTunedRuleRasterClassification: Applies threshold and infeasibility values and performs classification on mixture tuned rule raster. MixtureTunedTargetConstrainedInterferenceMinimizedFilter: Performs the Mixture Tuned Target-Constrained Interference-Minimized Filter (MTTCIMF) target analysis. NormalizedEuclideanDistanceClassification: Performs a Normalized Euclidean Distance (NED) supervised classification. OrthogonalSubspaceProjection: Performs the Orthogonal Subspace Projection (OSP) target analysis. ParallelepipedClassification: This task performs a parallelepiped supervised classification which uses a simple decision rule to classify multispectral data. RuleRasterClassification: Creates a classification raster by thresholding on each band of the raster. SpectralInformationDivergenceClassification: Performs the Spectral Information Divergence (SID) classification. SpectralSimilarityMapperClassification: Performs a Spectral Similarity Mapper (SSM) supervised classification. TargetConstrainedInterferenceMinimizedFilter: Performs the Target-Constrained Interference-Minimized Filter (TCIMF) target analysis. ENVI performance improvements NITF updates Merged ENVI Crop Science Module into ENVI Enhanced support for ENVI Connect also you may check this presentation: https://www.nv5geospatialsoftware.com/Portals/0/pdfs/envi-6.0-idl-9.0-redefining-image-analysis-webinar.pdf

Hello friends! I have plant symbols and would like to use them in QGIS. The problem is that these symbols don't allow you to edit the background colors. https://drive.google.com/file/d/15n7TvSRx8Ha2Igm_rs13A8KVggsSghJs/view?usp=sharing I followed jbrocha's solution posted on the GIS Stack Exchange forum: https://gis.stackexchange.com/questions/45180/how-to-create-svg-symbols-that-have-modifiable-fill-color-stroke-color-and-stro However, I would like to know if there is a more practical method to modify the colors of an SVG file imported into QGIS. Thanks.

Hi, a simple solution : in the Layer Properties, add your SVG a symbol, then add a new Simple Marker (like a circle or a Square), put the SVG Marker on top of the Simple Marker. The size of the SVG and the Simple Marker has to be similar or the Simple Marker Square just a little bigger. It worked with your symbol as svg and a Square Marker.

With the onset of the this years User Conference, Esri unveiled various updates to its existing services and apps, one of which is the Sentinel-2 Land Cover Explorer announced in last February. You can also find this in the Living Atlas. Apart of the the visual updates, the biggest change this time is that the changes over the year are now more accurate. A fundamental aspect of global LULC maps is the ability to detect and assess land cover changes over time. Improving on an already accurate set of annual maps, Impact Observatory has incorporated new features and methodologies in their proprietary deep learning classification model, resulting in better temporal consistency across the entire time series. Change between two LULC maps can potentially signify an important and developing change in an area of interest. However, in some cases, classification results may vary from one annual cycle to the next due to modeling insufficiencies, variability in seasonal observations, and/or class ambiguity at 10-meter resolution. Such cases can lead to false or spurious change results when conducting temporal change analysis. With the improvements to the temporal consistency, users assessing temporal change across the time series can be confident that what they are seeing represents the natural world. Link - https://livingatlas.arcgis.com/landcoverexplorer/ Source https://www.esri.com/arcgis-blog/products/arcgis-living-atlas/imagery/global-land-cover-updates/

Several digital elevation model (DEM) sources are used in the processing of Landsat Collection 2 Level-1 products. These sources are based on specific geographic regions and contribute to improved vertical accuracy in Collection 2 when compared to data processed in the past. Together, these sources are all known as the Landsat Collection 2 DEM. These DEM sources have been modified for use in Collection 2 processing; void filling techniques were used where persistent gaps were found in the elevation data, and improvements to the vertical accuracy were realized by differencing accuracies of other elevation datasets to the newer Collection 2 DEM. The following DEM sources are now available for download from EarthExplorer, listed under the Collection 2 section on the Data Set tab: Global Land Survey (GLS) — Various specific elevation inputs collectively make up the Global Land Surveys (GLS) DEM. Each input is based on the spatial region for which it is appropriate. Radarsat Antarctic Mapping Project (RAMP) — The Radarsat Antarctic Mapping Project (RAMP) is a high-resolution DEM that combines topographic data from a variety of sources to provide consistent coverage of all of Antarctica. Gravity for Earth, Ocean and Ice Dynamics (GEOID) Model — While not an actual elevation dataset, the geoid model provides necessary offsets to adjust the elevations of the GLS DEM from its original Earth Gravitational Model 96 (EGM96) to the World Geodetic System 84 (WGS84) ellipsoid. This is necessary so the GLS DEM can be correctly used by the Landsat processing systems software and algorithms. Please visit Landsat Collection 2 Digital Elevation Model to learn more about these DEM source products, and contact USGS Customer Services with any questions. links: Landsat Collection 2 DEM Source Products Available | U.S. Geological Survey (usgs.gov)

Flash floods caused by heavy rains have killed at least 14 people, displaced thousands more, and destroyed properties and homes in several areas of Somalia signalling an early start of the Gu rainy season, which usually runs from April to June. Those who have lost their homes are now living in makeshift shelters on higher ground, which are severely overcrowded and lack water and sanitation facilities. The Somali Disaster Management Agency (SoDMA) said it is the worst flooding in almost a decade. Heavy rain in the Ethiopian Highlands has also made its way downstream, increasing river levels in Somalia. You can see the satelite detection on below links: Flooding in Somalia and Ethiopia - Activations - International Disasters Charter

QField’s main new feature of this 2.5 release cycle is its brand new elevation profiling functionality which has been added to the measuring tool. Users are now able to dynamically build and analyze elevation profiles wherever they are – in the field or on their desktop – by simply drawing paths onto their maps and projects. This is a great example of QField’s capability at bringing the power of QGIS through a UI that keeps things simple and avoids being in your way until you need it. Oh and while we’re speaking of the measuring tool, check out the new azimuth measurement! This new version also brings multi-column support to feature forms. QField now respects the number of columns set by users in the attributes’ drag and drop designer while building and tweaking projects in QGIS. The implementation will take into account the screen availability and on narrow devices will revert to a one-column setup. Pro tip: try to change the background color of your individual groups to ease understanding of the overall feature form. Another highlight of this release is a brand new screen lock action that can be triggered through QField’s main menu found in the side dashboard or in the map canvas menu shown when long pressing on the map itself. Once activated, QField will become unresponsive to touch and mouse events while keeping the display turned on. When locked, QField also hides tool buttons which results in a more complete view of the map extent. Stability improvements As with every release, our ninjas have been spending time hunting nasty bugs and improving stability and QField 2.5 is no exception. In particular, the feature form should feel more reliable and even more polished. sites: QField - Efficient field work built for QGIS

Good news because I've been waiting for it since a long time. I have already tried it but I was really disappointed. "Input" from merginmap is really far more efficient than QField. I've been using this tool for 2 years now and it's really powerful.

The Mapzen open-source mapping platform has a hard history. On the one hand, Mapzen, which is based on OpenStreetMap, is used by over 70,000 developers and it's the backbone of such mapping services as , Remix and Carto. But, as a business, Mapzen failed in 2018. Mapzen's code and service lived on as a Linux Foundation Project. Now, it's moved on to the Urban Computing Foundation (UCF), another Linux Foundation group with more resources. UCF is devoted to helping create smarter cities, multimodal transportation, and autonomous vehicles. In the UCF, Mapzen will have the support of such members like Facebook, Google, and Uber. There, its developers can collaborate on and build a common set of open-source tools connecting cities, autonomous vehicles, and smart infrastructure. It joins the existing UCF project Kepler.gl, an open-source geospatial analysis tool for Big Data. Mapzen is made up of several MIT-licensed projects. These include real-time search, rendering, navigation, and data. These include: Pelias: Distributed full-text geographic search engine Tangram: Libraries for rendering 2D and 3D maps with WebGL/OpenGL ES and vector tiles Tilezen: Libraries to generate vector tiles for global map display Transitland: Community-edited data service aggregating transit networks across metropolitan and rural areas around the world Valhalla: Global, multi-modal routing engine for turn-by-turn navigation services Who's on First: Gazetteer or a big list of places, each with a stable identifier and descriptive properties "We are extremely excited to welcome the Mapzen family of projects to the Urban Computing Foundation," said Travis Gorkin, Uber's engineering manager of data visualization. This move "represents a great step forward in expanding the ecosystem of open-source urban computing software and tools," he added. It's also good news for all the many mapping services, which has relied on Mapzen, and for all the future transportation and mobility applications that will lean on it in the future. source: https://www.zdnet.com/article/mapzen-open-source-mapping-project-revived-under-the-urban-computing-foundation/

Applications of Geographical Information System (GIS) in Civil Engineering Objectives Relevant Blog websites References Introduction GIS applications in civil engineering Landslide hazard zonation Site selection for solid waste disposal Site selection for groundwater potential zones Hurricane wind Analysis Transportation analysis Relevant Blog websites http://www.gisblog.com http://www.gis-blog.com http://gisfolio.blogspot.com http://opensourcegisblog.blogspot.com http://www.birdseyeviewgis.com http://canadiangis.com http://www.gisarea.com/ Introduction Geographic Information System (GIS) is the new area with diversified applications in Civil Engineering, Geosciences, Forestry, Disaster mitigation, Environment and Ecology, Infrastructure planning, Utility mapping, Business, Mobile mapping, Information Technology and in many more fields. Since nearly 80% of the real world data are spatial in nature, GIS technology has been popularized overwhelmingly. It is not overestimation that GIS will be used in day to day life within next decade. Already developed countries use GIS widely and it will be used in day to day activities in developing countries also in near future. CIVIL ENGINEERING APPLICATIONS Ø Landslide hazard zonation Ø Site selection for solid waste disposal Ø Site selection for groundwater potential zones Ø Earthquake probability Analysis Ø Hurricane wind Analysis Ø Transportation analysis Ø Natural Hazard Assessment Ø Target Site Selection Ø Landfill Site Selection Ø Mineral mapping Ø Pollution Monitoring Ø Urban Development Ø Watershed Analysis Ø The following Figure shows the zones for groundwater exploration The following Figure shows the classified map for various crops of parts of PAP project. Site selection for solid waste disposal Site selection for solid waste disposal for Coimbatore was carried out in GIS and shown in following Figure. Urban development has brought forth several maladies and suffering to human kind, besides bringing economic and cultural development in its fold. Due to pressure of urbanization most of the cities are growing and some times they develop beyond the planned limits. Generally the unplanned area of the city contains a quarter of the total population where the spatial information is missing. At present no efficient system is available for solid waste management in Coimbatore Corporation, though the corporation administration now taking a few steps to manage the solid wastes. In this direction, an attempt has been made to optimize the route and also finding a favorable site for disposal of solid wastes using GIS & GPS for Coimbatore Corporation. LANDSLIDE HAZARD ZONATION Several different themes like land use, drainage, soil and outcrop, lineament, slope, etc. were obtained and weightages were assigned to each theme. Based on weightages assigned, the entire study area is divided into 5 classes [Very High, High, Moderate, Low and very Low]. From the map obtained by weighted overlay and the arithmetic overlay map, the Landslide Hazard Zonation (LHZ) is thus created. The road network map is overlaid with slope and land use map to identify sites, where mitigation works to be taken on priority basis. Conclusion: After studying the applications of GIS in civil engineering the conclusion was that GIS software is very helpful in civil engineering. Without GIS it will be very difficult and time taking process for us to select a site for any task. E.g. selecting a vehicles routing with the help of GIS software is easier and less time taking process as compare to doing it manually. Similarly in watersheds it has many applications i.e. determining the floods zones, the probability of low-flow events and estimating the magnitude of high-flow events. In short GIS software has an important role in civil engineering and without this it will be difficult for a civil engineering to do the tasks like finding the probability of hurricanes. References www.esri.com/library/brochures/pdfs/gis-sols-for-civil-engineering.pdf https://en.wikipedia.org/wiki/Civil_engineering www.slideshare.net/amalcvarghese/gis-application-in-civil-engineering https://www.scribd.com/doc/99493218/Application-of-Remote-Sensing-and-Gis-in-Civil-Engineering http://gis.stackexchange.com/questions/56825/using-gis-in-civil-engineering-construction-field https://en.wikiversity.org/wiki/Basics_of_civil_engineering www.slideshare.net/rpundlik111/gis-and-gps-applications-in-civil-engg ftp://urban.gis.unlv.edu/haroon/classes/...668_Intro2GIS/.../Lecture11.pdf www.engr.uky.edu/.../GIS%20applications%20in%20CE%20-%20Eric.ppt http://www.powershow.com/view/6cedcMTc5Z/GIS_Applications_in_Civil_Engineering_Carolyn_J_Merry_Dept_of_Civil_powerpoint_ppt_presentation http://ajolma.net/files/Lectures/Geospatial%20computing%20in%20civil%20engineering.ppt https://www.ndsu.edu/ce/civil_engineering/ce_subfields www.ncsu.edu/midlink/gis/joseph.htm https://en.wikipedia.org/wiki/Geomatics_engineering www.civil.iitb.ac.in/...%20GIS%204%20WRM.../GIS%20Lecture-1_GIS http://gis.stackexchange.com/questions/18526/gis-applications-used-in-civil-engineering-sources-for-acquiring-more-gis-know http://cenews.com/article/6147/q&a__opportunities_for_gis_in_civil_engineering www.slideshare.net/amalcvarghese/gis-application-in-civil-engineering http://www.aboutcivil.org/answers/1094/what-are-the-requirements-of-gis https://www.gis.fhwa.dot.gov/documents/climate_change_report_aug2011.pdf

What's the point using PowerPoint for GIS presentations? What about another approach?

Hi, adnan0001 If you go for icons, another resource can be found here https://mapicons.mapsmarker.com/ You can change the color of icons online and export those icons to use in arcmap or other softwares.