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    Convert CRS from ESRI:102673 to EPSG:4326

    Zach
    By Zach,
    Hi readers, I have a dataset of ~60,000,000 rows of lidar point data. All spatial reference points are projected using ESRI:102673 (full WKT here: https://epsg.io/102673). I need to convert this to latitude and longitude, EPSG:4326, so that I can overlay other layers and have them match up in the same spacial reference. I am using Python, so any transformation I can do using numpy or pandas will work, or any other free software that gets the job done would be great. This tool (https://

    Field calculation ?

    adamekcerv
    By adamekcerv,
    Hi, I have two layers of polygons (gaps in the city) with unique IDs and smaller "lots". These gaps are dissolved from these smaller "lots" polygons (in some cases one lot is equal to one gap). These smaller polygons have the same ID (all ID1 lots - creates one ID1 gap). I have three types of these smaller lots polygons, let's say a, b, c. How to calculate the percentage share (or spatial share ha) of these individual (a,b,c) types of lots in the gap ? Ex: ID1 gap: 30% - type a 50% - type b 20

    Add Feature Point by Coordinate (Long,Lat) in QGIS From Postgis Layer

    Reyalino
    By Reyalino,
    Basically, I want to add a feature point by a coordinate in QGIS where the feature itself is loaded from Postgres database. I know there is a plugin called LatLon Tools which provides the tool but sadly it cannot be done when the layer is loaded from Postgres database. anyone knows how to do it?

    Emlid launches Reach RS2 multi-band RTK receiver

    Lurker
    By Lurker,
    Emlid has debuted the Reach RS2, a fully-featured multi-band RTK receiver. All of its features are available out of the box, along with a survey app for iOS and Android. The Reach RS2 tracks L1/L2 bands on GPS, GLONASS and BeiDou, and L1/L5 on Galileo, and acquires a fixed solution in seconds. It achieves centimeter-level precision for surveying, mapping and navigation and maintains robust performance even in challenging conditions. Centimeter accuracy can be achieved on distances up to 60 

    Mapping Carbon Dioxide Emissions from Soil Respiration

    Lurker
    By Lurker,
    Until recently, there have been no clear assessment of how much  CO2  and the role that soils contribute in emissions relative to other greenhouse gases. Now a global scale map using statistical models and satellite imagery, along with other work by scientists, is beginning to indicate how much land use and soil change can affect our planet’s climate. In a recent study, a series of machine learning models using multiple nonlinear regression (MNLR), random forest regression (RFR), support ve

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    • 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.
    • 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.
    • 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
    • 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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