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Lurker

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Lurker last won the day on February 21

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About Lurker

  • Birthday 02/13/1983

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  1. 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
  2. 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/
  3. this is indonesian language sub forum, and you may notice this topic is 5 years old
  4. any rough guess based on your experience?
  5. anyone knows the exact price for original license ENVI? how much in USD?
  6. 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
  7. 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/
  8. 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
  9. 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
  10. 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.
  11. do you mean the filter function in drone2map? (densified point cloud filter?)
  12. The Aeolus mission is coming to a close on 30 April 2023 with a series of end life activities after achieving many significant accomplishments. Launched in 2018, the mission’s main goal was to measure the Earth’s wind patterns and improve our understanding of how they affect the planet’s climate. That ESA’s wind mission had outlived its predicted lifetime of three years by over 18 months. The best course of action to wind down Aeolus was to carefully re-enter the satellite back to Earth. The finishing touches to the end-of-life schedule will be made in a span of numerous weeks. Innovating Wind Measurement across Earth Using state-of-the-art laser technology, Aeolus was able to measure the wind speeds and direction from space with incredible precision. These measurements were used to create detailed maps of global wind patterns and improve weather forecasting models. In addition to its primary mission, Aeolus also made important contributions to other areas of Earth observation. For example, it provided valuable data on air pollution and dust transport across the globe. As the Aeolus mission winds down, scientists are already looking ahead to future missions that can build on its successes. This includes plans for new Earth observation missions that will focus on other key environmental factors such as ocean currents, land use, and the carbon cycle. Aeolus Mission Manager, Tommaso Parrinello was grateful to all the ESA and industry colleagues who developed and operated the mission. Improvement in Weather Forecasts Aeolus carried ALADIN, an instrument that is Europe’s most sophisticated Doppler wind lidar flown in space. A laser inbuilt the instrument fires pulses of ultraviolet light towards Earth’s atmosphere, which is received by a light detecting receiver that scatters it back from air and water molecules like aerosols and dust. With that measurement one can check the speed of the wind. Over its extended lifetime, ALADIN has beamed down over seven billion laser pulses orbiting Earth 16 times a day and covering the entire globe once a week. Aeolus data are used by major weather forecasting services worldwide, including the European Centre for Medium-Range Weather Forecasts (ECMWF), Météo-France, the UK Met Office, Germany’s Deutscher Wetterdienst (DWD), and India’s National Centre for Medium Range Weather Forecasting (NCMRWF). Since ECMWF started assimilating Aeolus data in 2020 the satellite has become the highest impact-per-observation instruments in existence. It is mainly due to Aeolus’ capacity to measure winds where data is scarce. When planes were grounded during the lockdowns imposed due to the COVID pandemic, Aeolus was able to contribute missing data to plug the gap in weather forecasts. ESA’s Director of Earth Observation Programme, Simonetta Cheli said that the Aeolus mission has been a triumph of European innovation, collaboration and technical excellence, and is an example of how ESA’s Earth Explorers perform beyond expectations, and is a shining light for our Future EO Programme. Its impacts will live long beyond its lifetime in space, paving the way for future operational missions such as Aeolus-2. Despite the mission’s impending end, the data collected by Aeolus will continue to be used by scientists around the world for years ahead in the future. This legacy is a testament to the mission’s trailblazing spirit and its important role in advancing our understanding of the Earth’s climate. First Mission to Measure Earth’s Wind Patterns Powers Down (geospatialworld.net)
  13. The WMO State of the Global Climate report 2022 focuses on key climate indicators – greenhouse gases, temperatures, sea level rise, ocean heat and acidification, sea ice and glaciers. It also highlights the impacts of climate change and extreme weather. Drought, floods and heatwaves affect large parts of the world and the costs are rising Global mean temperatures for the past 8 years have been the highest on record Sea level and ocean heat are at record levels – and this trend will continue for many centuries Antarctic sea ice falls to lowest extent on record Europe shatters records for glacier melt From mountain peaks to ocean depths, climate change continued its advance in 2022. Droughts, floods and heatwaves affected communities on every continent and cost many billions of dollars. Antarctic sea ice fell to its lowest extent on record and the melting of some European glaciers was, literally, off the charts. The State of the Global Climate 2022 shows the planetary scale changes on land, in the ocean and in the atmosphere caused by record levels of heat-trapping greenhouse gases. For global temperature, 2015-2022 were the eight warmest on record despite the cooling impact of a La Niña event for the past three years. Melting of glaciers and sea level rise - which again reached record levels in 2022 - will continue for up to thousands of years. links: State of the Global Climate in 2022 | World Meteorological Organization (wmo.int)
  14. 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)
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