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  2. 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.
  3. How Upskilling in GIS Aids Educational Policy Research Understanding the distribution of student demographics is crucial for making informed decisions. This is where Geographic Information Systems (GIS) and remote sensing technologies play a vital role. By upskilling in GIS, researchers can harness the power of spatial analysis and mapping to gain valuable insights into student populations and educational disparities. GIS is a powerful tool that allows researchers to visualize, analyze, and interpret data in a spatial context. By integrating demographic data with geographic information, researchers can create detailed maps that highlight patterns and trends in student populations. Mapping student demographics enables policymakers and educators to identify areas with high concentrations of specific demographic groups, such as low-income students, English language learners, or students with disabilities. This information can inform targeted interventions and resource allocation to address educational inequities. Remote sensing, on the other hand, involves the collection of data from a distance, typically using satellite imagery or aerial photography. This technology provides researchers with a wealth of information about the physical characteristics of an area, such as land cover, vegetation density, and infrastructure. By combining remote sensing data with demographic information, researchers can gain insights into the relationship between the physical environment and educational outcomes. For example, they can examine how proximity to green spaces or access to transportation infrastructure affects student performance and attendance. Furthermore, GIS and remote sensing can help researchers analyze the spatial distribution of educational resources and facilities. By mapping school locations, transportation routes, and student residences, researchers can identify areas that lack access to quality education or suffer from transportation barriers. This information can guide the development of policies that promote educational equity and improve school planning. To effectively utilize GIS and remote sensing in educational policy research, upskilling is essential. Researchers should acquire proficiency in GIS software, such as ArcGIS or QGIS, to manipulate and analyze spatial data. They should also learn how to integrate remote sensing data into their analyses, using tools like Google Earth Engine or ENVI. Additionally, understanding spatial statistics and geospatial modeling techniques can enhance the depth and accuracy of research findings. In conclusion, upskilling in GIS and remote sensing offers significant benefits to educational policy research, particularly in mapping student demographics. By leveraging these technologies, researchers can gain valuable insights into the spatial distribution of student populations, educational disparities, and the impact of the physical environment on educational outcomes. With this information, policymakers and educators can make evidence-based decisions to promote educational equity and improve the quality of education for all students.
  4. Due to their ability to collect tree phenotypic trait data in large quantities, unmanned aerial vehicles, or UAVs, have completely changed the forestry industry. Even with the progress made in object detection and remote sensing, precise identification and extraction of spectral data for individual trees continue to be major obstacles, frequently necessitating tedious manual annotation. For better tree detection, current research focuses on developing segmentation algorithms and convolutional neural networks; however, the requirement for precise manual labeling prevents these technologies from being widely adopted. This emphasizes how critical it is to create a higher-throughput, more effective technique for automatically extracting spectral information for individual trees. The open-source tool ExtSpecR, which offers an intuitive interactive web application, is presented in this paper as a means of achieving single tree spectral extraction in forestry using UAV-based imagery. It optimizes the process of spectral and spatial feature extraction by speeding up the identification and annotation of individual trees. Users can calculate vegetation indices and view outputs as false-color and VI-specific images by uploading TIFF-formatted spectral images through the ExtSpecR user interface. Users upload point cloud data and multispectral images to the interactive dashboard, which then defines the region of interest (ROI) for tree identification and segmentation, enabling the system's core phenotyping capabilities. This procedure produces 3D visualizations of the segmented trees by utilizing the lidR package's "locate_trees" function. Evaluation of ExtSpecR's performance in comparison to ground truth in tree plantations with different canopy densities shows that it can detect individual trees with accuracy ranging from 91% to 97%. By comparing ExtSpecR's functionality to that of other tools, its distinct approach of fusing point cloud data and multispectral imagery with already-existing algorithms for optimal user experience and thorough tree analysis is highlighted. For better outcomes, recommendations include segmenting point cloud data and defining specific target areas, even though it faces difficulties with large input data sizes and complex environments with overlapping canopies. Further improvements, according to the paper, ought to focus on raising cloud quality and assessing effectiveness using hyperspectral imagery and LiDAR point clouds. page: GitHub - Yanjie-Li/ExtSpecR: Tree detection, segementation and spectral extraction
  5. 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
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  7. 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
  8. As technology advances and AI becomes more sophisticated, there is a growing concern that GIS analysts might be replaced by AI algorithms. What are your thoughts on this potential shift? Will AI be able to match the expertise and intuition of human analysts in the field of Geographic Information Science?
  9. 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/
  10. this is indonesian language sub forum, and you may notice this topic is 5 years old
  11. Can you please tell me what kind of bands you're making, what materials you're using, and what specific colors you're trying to achieve? With more information, I can provide more specific and helpful instructions. In the meantime, here are a few general tips for making natural-colored bands: Use natural materials like wool, hemp, or cotton. Dye the materials with natural dyes like plants, berries, or minerals. Use different shades of the same color to create a more natural look. Blend different colors together to create a more subtle effect. I hope this helps!
  12. any rough guess based on your experience?
  13. Interstingly, ENVI has been wildly successful for many people. After its release in 1994, it was acquired by ITT Corp in 2011, then handed to Exelis Inc, then Harris Corp, then L3 Technologies as L3Harris, and finally NV5 Global. The core is IDL which remained unchanged, but everything has refined and fine-tuned all the way. This depends on the vendor and license type.
  14. anyone knows the exact price for original license ENVI? how much in USD?
  15. Interesting update! I see they introduced IDL for VSCode as well, also an 'IDL Notebook'. I will give these new tools some time to mature before a proper test drive.
  16. Medicine of Envi 6 please!!!! 😇
  17. 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
  18. some of file hosting known to use some kind of client software mandatory to download their file, like for example pan baidu or terrabox and many more, I tend to avoid those type of file hosting btw, there are already topic related to this software, here: I will close this topic
  19. Likely spam, use a different download platform/site if this is genuine. While trying to download, it tried to install an unknown software and had to abort and cancel it.😡
  20. TerraExplorer 8.0 Installer Download Link🙂:https://pan.baidu.com/s/1Y33PRnVk5SwqLEenVaaG6g?pwd=qq7m
  21. 你好,TerraExplorer 8.0更新了,近期会有许可更新吗?

    TerraExplorer 8.0 安装包

    链接:https://pan.baidu.com/s/1Y33PRnVk5SwqLEenVaaG6g?pwd=qq7m 
     

    1. tsingkong

      tsingkong

      有没有官网的下载地址

      我抽空搞一下

  22. Is there a way to calibrate multispectral imagery without using a reflectance panel? I have two sets of data that need to be calibrated but they were flown without using a reflectance panel. The sensor is a Micasense RedEgde-MX. Both sets are taken in the same area.
  23. Correlator3D

     

    1. tsingkong

      tsingkong

      It looks like I have none of that.

  24. its been awhile since I update the forum, so here you go, sorry for the down time, but I need to update to the recent version to make sure the forum stay secure and the functions keep running, if you have any bugs please let me know
  25. 👋Hello! It's been a minute or two since I was last here. Keen to see what I have missed.
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