Spatio SDS - Spatial Data Systems

Working with LiDAR technology has been an exciting experience, as it significantly enhances efficiency in many industries like hashtag #forestry, #mining, and #construction. From topographic mapping to #3Dmodeling and volume measurement, LiDAR is a game-changer. Used this sample dataset from Kaggle acquired from backpack LiDAR #Ligriph120 and integrating the data with hashtag #LiDAR360 forestry software allows for even greater advancements. LiDAR can be used for forest asset mapping, identifying land boundaries, conservation areas, and tree counts. It also enables tree volume estimation, providing detailed measurements like tree height, diameter at breast height (DBH), crown diameter, and crown volume. Moreover, it helps with monitoring tree growth over time by analyzing multiple LiDAR scans. These are just a few examples of how LiDAR is revolutionizing forestry. #LiDAR #LiDAR360 #GreenValleyInternational #Forestry #TreeSegmentation #Geodetic #3DModel #3DMapping #KompasNavigasi #ForestManagement

?? GIS Database for Disaster Risk, Emergency, and Resilience ?? We are excited to share our legacy project: “GIS Database for Disaster Risk, Emergency, and Resilience,” authored by Philippa Burgess, Aranza Yee, and Jer-Yu Jeng. This comprehensive repository offers environmental scientists, geospatial practitioners, emergency responders, and resilience planners over 250 GIS data sources focused on natural hazards, environment, population, infrastructure, agriculture, and climate at global, national, and select regional levels. ?? Discover the resource here: https://lnkd.in/eDffgNUQ Let’s harness the power of geospatial data to build a safer, more resilient world! #Database #DisasterRisk #MentorshipMonday

Happy World L?DAR Day!!! #WorldLidarDay #L?DARHappy

Comparing LiDAR Technologies: ALS vs. MLS vs. TLS - Explore the differences in resolution and area coverage speed. Maximize your potential with our advanced technology – let's bring innovation and excellence together with ASPAC Geo Survey. Visit our website www.aspacgeosurvey.com for the best LiDAR data acquisition solutions, precise LiDAR data extraction, and the creation of outstanding videos from LiDAR data. #ASPCGeoSurvey #pointcloud #extraction #lidar #bim #digitaltwin #LiDAR #extractions #CAD #mapping #surveying #3DModeling #3DMapping #digitaltwins #engineering #infrastructure #Topography #3D #BIM #mining #TIN #DTM #topodot

Battling a massive and fast-spreading wildfire from the air is an incredibly complex logistical challenge. Using flight data from FlightAware and wildfire data from NASA and the ArcGIS Living Atlas, we visualized a 24-hour period of aerial firefighting over the Palisades Fire. Between January 9-10, more than 35 aircraft, operated by various state, local, and commercial agencies, covered over 15,000 flight miles to contain the blaze. The data was processed in ArcGIS Pro and brought to life through animation in Blender. Credits: Peter Atwood #ArcGIS #Blender3D #Wildfire #DataVisualization #Cart

Researchers at Hong Kong University MaRS Lab have just published another jaw dropping paper featuring their safety-assured high-speed aerial robot path planning system dubbed "SUPER". With a single MID360 lidar sensor they repeatedly achieved autonomous one-shot navigation at speeds exceeding 20m/s in obstacle rich environments. Since it only requires a single lidar these vehicles can be built with a small footprint and navigate completely independent of light, GPS and radio link. This is not just #SLAM on a #drone, in fact the SUPER system continuously computes two trajectories in each re-planning cycle—a high-speed exploratory trajectory and a conservative backup trajectory. The exploratory trajectory is designed to maximize speed by considering both known free spaces and unknown areas, allowing the drone to fly aggressively and efficiently toward its goal. In contrast, the backup trajectory is entirely confined within the known free spaces identified by the point-cloud map, ensuring that if unforeseen obstacles are encountered or if the system’s perception becomes uncertain, the system can safely switch to a precomputed, collision-free path. The direct use of LIDAR point clouds for mapping eliminates the need for time-consuming occupancy grid updates and complex data fusion algorithms. Combined with an efficient dual-trajectory planning framework, this leads to significant reductions in computation time—often an order of magnitude faster than comparable SLAM-based systems—allowing the MAV to operate at higher speeds without sacrificing safety. This two-pronged planning strategy is particularly innovative because it directly addresses the classic speed-safety trade-off in autonomous navigation. By planning an exploratory trajectory that pushes the speed envelope and a backup trajectory that guarantees safety, SUPER can achieve high-speed flight (demonstrated speeds exceeding 20 meters per second) without compromising on collision avoidance. If you've been tracking the progress of autonomy in aerial robotics and matching it to the winning strategies emerging in Ukraine, it's clear we're likely to experience another ChatGPT moment in this domain, very soon. #LiDAR scanners will continue to get smaller and cheaper, solid state VSCEL based sensors are rapidly improving and it is conceivable that vehicles with this capability can be built and deployed with a bill of materials below $1000. Link to the paper in the comments below.

ChatGPT for geospatial analysis is free and open-source! In the past, you needed a GIS specialist to perform basic geospatial analysis. Now, all that is needed is a prompt. Chat2 Geo delivers a ChatGPT-like experience tailored for remote-sensing-based geospatial analysis. Its mission is to democratize geospatial insights at scale by harnessing cutting-edge Al technologies, making advanced analysis accessible to everyone. Wonderful project by Shahab Jozdani, PhD. Thanks for making it open-source! Link to project: https://Inkd.in/ec-_Z6A?

The Segment Anything Model (SAM) and Its Impact on Geospatial Analysis The Segment Anything Model (SAM), introduced by Meta AI, represents a groundbreaking advancement in geospatial technology. By providing precise and flexible segmentation across a variety of datasets, SAM has revolutionized workflows in GeoAI, tackling challenges like object detection, land classification, and temporal change analysis. This innovative tool has enhanced the efficiency of extracting essential spatial features, reduced reliance on labor-intensive processes, and paved the way for more accurate analysis of complex geospatial phenomena. How SAM Enhances GeoAI Applications 1. Object Detection in Remote Sensing Technical Aspect: Identifying features such as roads, buildings, and water bodies in aerial or satellite imagery. SAM's Contribution: Facilitates instant segmentation of these features from high-resolution geospatial data, drastically reducing the time required for manual annotations. 2. Land Cover Classification (LCC) Objective: Classify regions into categories such as forests, urban areas, wetlands, and farmlands. SAM's Contribution: Streamlines the segmentation of varied land cover types by leveraging its pretrained algorithms, even in challenging geographies. 3. Change Detection Objective: Monitor changes in land use, deforestation, or urban development over time. SAM's Contribution: Enables automated change detection by accurately segmenting objects in sequential imagery datasets. 4. Spatiotemporal Analysis Objective: Examine dynamic events like glacier movement or flooding patterns. SAM's Contribution: Delivers precise segmentation in time-series imagery, allowing for deeper insights into spatial and temporal trends. 5. Automated Map Updates Objective: Maintain updated geospatial datasets using the latest satellite imagery. SAM's Contribution: Extracts features like new roads and buildings, ensuring maps remain accurate and current. Follow: Gensre Engineering & Research #GeoAI #ArtificialIntelligence #MachineLearning #SegmentAnythingModel #RemoteSensing #GeospatialAnalysis #GIS #Automation #InnovationInAI #LandCoverMapping #ChangeDetection Software: Esri ArcGIS Pro Video Credit: Esri Canada

The paper 'Pix2Poly: A Sequence Prediction Method for End-to-end Polygonal Building Footprint Extraction from Remote Sensing Imagery' by Yeshwanth KumarA. Charalambos Poullis, and Melinos Averkiou has been accepted for publication in IEEE/CVF WACV 2025. This work introduces Pix2Poly, an attention-based, end-to-end trainable, and differentiable deep learning model that generates explicit, high-quality building footprint polygons in ring graph format from aerial images. The method outperforms existing state-of-the-art approaches in accuracy and efficiency without requiring raster losses or complex pipelines, as demonstrated on multiple challenging datasets. Research paper & Supplementary material: https:/Inkd. in/g5ezHGjy Source code: https://lnkd.in/gZV6N7GW

Reality Mapping + 3D GIS?? Replicating and modeling reality is just the start of the Digital Twin journey. Often referred to as a "visual twin," when combined with GIS, BIM, and surveying data in a robust system, it can revolutionize your organization. Expect to: 1. Drive impactful transformation. 2. Optimize operations, resource usage, and reduce waste. 3. Anticipate potential issues before they arise, enhancing resilience and risk management. What are your expectations from Digital Twins? Data provided by: PORTCOAST CONSULTANT CORPORATION #3dgis #digitaltwin #digitaltwins