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Lurker

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Lurker last won the day on March 17

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  • Birthday 02/13/1983

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    GIS and Remote Sensing

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  1. Satellite images analyzed by AI are emerging as a new tool in finding unmapped roads that bring environmental destruction to wilderness areas. James Cook University's Distinguished Professor Bill Laurance was co-author of a study analyzing the reliability of an automated approach to large-scale road mapping, using convolutional neural networks trained on road data, using satellite images. He said the Earth is experiencing an unprecedented wave of road building, with some 25 million kilometers of new paved roads expected by mid-century. "Roughly 90% of all road construction is occurring in developing nations including many tropical and subtropical regions of exceptional biodiversity. "By sharply increasing access to formerly remote natural areas, poorly regulated road development triggers dramatic increases in environmental disruption due to activities such as logging, mining and land clearing," said Professor Laurance. He said many roads in such regions, both legal and illegal, are unmapped, with road-mapping studies in the Brazilian Amazon, Asia-Pacific and elsewhere regularly finding up to 13 times more road length than reported in government or road databases. "Traditionally, road mapping meant tracing road features by hand, using satellite imagery. This is incredibly slow, making it almost impossible to stay on top of the global road tsunami," said Professor Laurance. The researchers trained three machine-learning models to automatically map road features from high-resolution satellite imagery covering rural, generally remote and often forested areas of Papua New Guinea, Indonesia and Malaysia. "This study shows the remarkable potential of AI for large-scale tasks like global road-mapping. We're not there yet, but we're making good progress," said Professor Laurance. "Proliferating roads are probably the most important direct threat to tropical forests globally. In a few more years, AI might give us the means to map and monitor roads across the world's most environmentally critical areas." journal: https://www.mdpi.com/2072-4292/16/5/839
  2. 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.”
  3. Leica Geosystems, part of Hexagon, introduces the Leica TerrainMapper-3 airborne LiDAR sensor, featuring new scan pattern configurability to support the widest variety of applications and requirements in a single system. Building upon Leica Geosystems’ legacy of LiDAR efficiency, the TerrainMapper-3 provides three scan patterns for superior productivity and to customise the sensor’s performance to specific applications. Circle scan patterns enhance 3D modelling of urban areas or steep terrains, while ellipse scan patterns optimise data capture for more traditional mapping applications. Skew ellipse scan patterns improve point density for infrastructures and corridor mapping applications. The sensor’s higher scan speed rate allows customers to fly the aircraft faster while maintaining the highest data quality, and the 60-degrees adjustable field of view maximises data collection with fewer flight lines. The TerrainMapper-3 is further complemented by the Leica MFC150 4-band camera, operating with the same 60-degree field of view coverage as the LiDAR for exact data consistency. Thanks to reduced beam divergence, the TerrainMapper-3 provides improved planimetric accuracy, while new MPiA (Multiple Pulses in Air) handling guarantees more consistent data acquisition, even in steep terrain, providing users with unparalleled reliability and precision. The new system introduces possibilities for real-time full waveform recording at maximum pulse rate, opening new opportunities for advanced and automated point classification. The TerrainMapper-3 seamlessly integrates with Leica HxMap end-to-end processing workflow, supporting users from mission planning to product generation to extract the greatest value from the collected data.
  4. 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
  5. The Association for Geographic Information (AGI) and the Government Geography Profession (GGP) have agreed to work together to combine their experience, expertise and outreach to further the impact of geospatial data and technology within the public sector. By working together, they will help grow the geospatial community, and will build on recent activities such as the AGI’s Skills Roundtable. “The UK is at the forefront of geospatial. Now more than ever, geographers are combining increasing quantities of geospatial information with advances in technology, such as AI and ML, to drive new insights on our place in the world,” commented David Wood, Head of the Government Geography Profession. “The profession is leading the way in government and the public sector, recognising and encouraging the use of geography and geographical sciences within and across government. By working with the AGI, we can increase awareness and therefore engagement with geographers across government and align our ambitions and activities with the wider geospatial community.” “Many of government’s greatest challenges are time and place related and therefore the data and technology that will help address and resolve them must also have location at its heart,” added Adam Burke, Past Chair of the Association for Geographic Information. “By partnering with GGP, we can help ensure the geospatial ecosystem continues to grow sustainably, both within government and beyond, and is utilised across diverse industry sectors and across multiple applications to impact positive outputs.” AGI is the UK’s geospatial membership organisation; leading, connecting and developing a community of members who use and benefit from geographic information. An independent and impartial organisation, the AGI works with members and the wider community alongside government policy makers, delivers professional development and provides a lead for best practice across the industry. Its mission is to nurture, create and support a thriving community, actively supporting a sustainable future, and it aims to achieve this by nurturing and connecting active GI communities, supporting career and skills development and providing thought leadership to inspire future generations. The GGP, established in 2018, is made up of around 1,500 professional geographers in roles across the public sector. The profession is working ‘to create and grow a high-profile, proud and effective geography profession that attracts fresh talent and has a secure place at the heart of decision making’. This is being achieved by creating the environment for geographers to have maximum impact, professionalising and progressing the use applications of geography and growing a diverse and inclusive community within government and the wider public sector. page: https://www.directionsmag.com/pressrelease/12860
  6. Copernicus Open Access Hub is closing at the end of October 2023. Copernicus Sentinel data are now fully available in the Copernicus Data Space Ecosystem As previously announced in January the Copernicus Open Access Hub service continued its full operations until the end of June 2023, followed up by a gradual ramp-down phase until September 2023. The Copernicus Open Access Hub will be exceptionally extended for another month and will cease operations at the end of October 2023. To continue accessing Copernicus Sentinel data users will need to self-register on the new Copernicus Data Space Ecosystem. A guide for migration is available here. The new service offers access to a wide range of Earth observation data and services as well as new tools, GUI and APIs to help users explore and analyse satellite imagery. Discover more about the Copernicus Data Space Ecosystem at https://dataspace.copernicus.eu . A system of platforms to access EO data The Copernicus Data Space Ecosystem will be the main distribution platform for data from the EU Copernicus missions. Instant access to full and always up-to-date Earth observation data archives is supported by a new, more intuitive browser interface, the Copernicus Browser. Since 2015, the Copernicus Open Access Hub supports direct download of Sentinel satellite data for a wide range of operational applications by hundreds of thousands of users through the last decade. However, technology has moved on and the Copernicus Data Space Ecosystem was recently launched as a new system of platforms for accessing Sentinel data. As part of this process, the current access point will be gradually wound down from July 2023 and will no longer operate from end of October 2023. This post demonstrates how to migrate your workflow from accessing the data through the Copernicus Open Access Hub to using APIs via the Copernicus Data Space Ecosystem. In this post, we will show you how to: setup your credentials use OData to search the Catalog and download Sentinel-2 L2A Granules in .SAFE Format. search, discover and download gridded Sentinel-2 L2A data using the Process API Increase in data quality, quantity and accessibility With the glut of free and open data in recent years, the increases in revisit times and higher spatial and temporal resolutions, applications using earth observation data have blossomed. For example, before 2013, you would likely have used Landsat 8 data for land cover mapping with a revisit time of 16 days at 30m spatial resolution. In 2023 though, we now have access to Sentinel-2 with a revisit time of 3-5 days at 10m resolution enabling you not just to map land cover but monitor changes at higher spatial and temporal resolutions. Therefore, while it was feasible to download, process and analyse individual acquisitions in the past, this approach is no longer effective today and it makes more sense to process data in the cloud. This is where the new APIs provided by the Copernicus Data Space Ecosystem come in. official page: https://dataspace.copernicus.eu/
  7. this is indonesian language sub forum, and you may notice this topic is 5 years old
  8. any rough guess based on your experience?
  9. anyone knows the exact price for original license ENVI? how much in USD?
  10. 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
  11. Are you a post-doctoral researcher looking for an exciting opportunity in advanced Earth Observation (EO) for Earth Science? The ESA is offering a two-year research fellowship in the Directorate of Earth Observation Programmes. The fellowship will cover a wide range of innovative topics from the development and validation of novel methods, algorithms and EO products to innovative Earth system and climate research. The successful candidate will be responsible for undertaking advanced research addressing major observational gaps and scientific priorities in EO and Earth system science. The fellowship is open to all qualified candidates irrespective of gender, sexual orientation, ethnicity, beliefs, age, disability or other characteristics. Applications from women are encouraged. Apply by October 3, 2023 1.For more information please visit: http://geospatialsight.com/post-doctoral-research-fellowship-in-advanced-eo-for-earth-science/
  12. no, geomatics and engineer could do that, there are many software that already have deep learning function... for example, qgis already have plugin for deep learning, you can search on that in google, there are many paid software that can do that
  13. 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
  14. 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.
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