GIS Mapping Software

Revolutionizing Geospatial Data: The Evolution of Aerial Photogrammetry Over the past 25 years, aerial photogrammetry has transformed into a fully digital technology, providing highly precise spatial data essential for creating digital twins and making informed decisions in urbanization, climate change, and energy production. 🔍 Key Developments: 🔹 Digital Transformation: The 1990s saw the digitization of analog aerial images using high-precision scanners, leading to the first digital photogrammetric workstations. 🔹 Introduction of Laser Scanning: The late 1990s brought laser scanning technology, enabling direct capture of elevation data over large areas. 🔹 Advancements in GPS Technology: Integrating GPS allowed near real-time positioning and direct orientation of aerial images, enhancing spatial data precision. 🔹 First Digital Aerial Cameras: In 2000, Leica and Zeiss-Intergraph introduced the first digital aerial cameras, replacing traditional film with digital sensors. 🔹 Drones and Computer Vision: The 2010s democratized aerial photogrammetry with affordable drones and advancements in computer vision algorithms, enabling efficient data capture for smaller areas. 🔹 Semi-Global Matching (SGM): Introduced in 2005, SGM revolutionized 3D point cloud generation from image data, achieving near-laser scanning quality for surface models. 🔹 Hybrid Sensor Systems: The development of hybrid sensors combining imaging and laser scanning technologies in 2016. 🚀 Trends Shaping the Future: 🔹 Higher Resolutions: Achieving resolutions of 10 cm or better for large areas and 5 cm for urban regions. 🔹 Frequent Updates: Annual or bi-annual flights for cities and large-scale areas to ensure up-to-date data. 🔹 Larger Project Areas: Expanding project sizes to cover entire countries efficiently. 🔹 Multisensor Integration: Simultaneous capture of complementary image and LiDAR data, providing comprehensive geospatial information. 🔹 Artificial Intelligence: Enhanced data analysis, flight planning, and quality control through AI, leading to more efficient and accurate results. 🔹 End-to-End Solutions: Providing complete solutions from data capture to final presentation, meeting the growing demand for ready-to-use information. 🌟 Impact on Industries: Aerial photogrammetry is crucial for creating spatial digital twins, foundational for urban planning, environmental monitoring, and disaster management. AI and hybrid sensors enhance geospatial data accuracy and usability, driving innovation across sectors. 📈 Looking Ahead: The future of aerial photogrammetry lies in sensor advancements, increased automation, and AI integration. These developments will lead to higher quality data, faster processing times, and more comprehensive solutions, making geospatial data more accessible and valuable than ever before. 💡 Comment | Like | Share 👉 Follow me (Dr. Uwe Bacher) for more geospatial insights #Photogrammetry #DigitalTwins #AerialMapping

Drone surveying plays a key role in the Mamre Road Upgrade project in Sydney’s western suburbs. The 3.8km upgrade from two to four lanes is benefiting from digital surveying and cloud collaboration using 12d Synergy software. To learn more, I spoke with Lorcan Broderick, Survey Manager at contractor Seymour Whyte. Lorcan: “The ability the drones provide is phenomenal. The advances in photogrammetry over the last few years have been huge.” Lorcan: “We fly photogrammetry twice a month and video once a month. The output is hugely advantageous for the project team. “The drone data allows us to calculate material volumes, understand where material has moved from and to, and identify what additional material is required on site. “Traditionally, surveyors would have been out across the site collecting that data manually. The drone can capture the entire site in a matter of hours. “This reduces the exposure of people working around large machinery and gives us a very accurate snapshot of the site at that exact moment. “All of the information is then shared through 12d Synergy, which is a common data environment that approved stakeholders can access. So everyone can have visibility across the project. “The aerial imagery is particularly useful for programme planning and keeping everybody informed. People are visual learners, so they like to see progress. “Our client also uses the imagery regularly, and the feedback has been very positive.” #Construction #DigitalConstruction #Infrastructure #Surveying #constructiontechnology

🌏 🛰️ Supervised Land Use Classification Using Remote Sensing and Machine Learning 🛰️ 🌏 Land use classification is critical in understanding how human activities shape the environment and guiding sustainable development. In this learning experience, I explored how satellite imagery, field data, and machine learning techniques can be combined to generate detailed land use maps for effective spatial analysis and decision-making. 📊 Methodology ✔️ Field Data Collection- Collected 60 ground truth points across five land use classes (Built-up, Vegetation, Paddy, Bare Land, and Water Bodies) using QField linked with QGIS. ✔️ Satellite Data Processing- Processed Landsat 8 imagery in Google Earth Engine (GEE) to extract spectral signatures for each land use class. ✔️ Classification Model- Applied the Support Vector Machine (SVM) algorithm in Google Colab for supervised classification. ✔️ Accuracy Assessment- Validated the classification using a confusion matrix, user/producer accuracy metrics, and the kappa coefficient from both manually and Google Colab. 📈 Results ▪️ The model achieved an overall classification accuracy of 65% with moderate agreement (Kappa = 0.55). ▪️ Built-up area showed higher classification accuracy (70.59 % producer accuracy, 66.67% user accuracy), while Bare Land and Paddy classes had relatively lower accuracy, highlighting areas for future improvement. 🔎 Why This Matters This study demonstrates how integrating ground truth data, satellite imagery, and open-source tools enables practical, cost-effective land use classification, especially valuable for resource and data-limited areas. These insights can guide urban planning, agriculture management, environmental protection, and disaster risk planning. By applying machine learning in geospatial analysis, this study helps connect data to real-world solutions, making it a valuable approach for building more sustainable and resilient communities. #GIS #RemoteSensing #LandUseClassification #MachineLearning #GoogleEarthEngine #QField #QGIS #SpatialData #GeospatialAnalysis #UrbanPlanning #SustainableDevelopment #DataDrivenPlanning

Following the publication of my paper here https://lnkd.in/eP4AfWty, I’m ready to share my Random Forest-based machine learning scripts for Land Use Land Cover (LULC) classification in Gaborone, Botswana. The model was enhanced using the following indices: NDVI (Vegetation), NDBI (Built-up Index), NDWI (Water Index), BSI (Bare Soil Index). These indices improve class separability, making the classification process more accurate. The scripts, especially for the 2005 classification, also include a scan line error correction function for Landsat 7 images and a pan-sharpening function to enhance radiometric resolution by merging high-resolution panchromatic data with multispectral bands. Access the scripts here: 🔗 https://lnkd.in/eYbQs3Dy #GIS #remotesensing #machinelearning #lulc #geospatialanalysis #gee

🌍 Predicting Future Land Use and Land Cover (LULC) with - MOLUSCE plugin I’ve just published a new YouTube tutorial where I demonstrate how to simulate and validate future Land Use & Land Cover using the MOLUSCE plugin in QGIS. In this video, I walk through the complete workflow: ✅ Data preparation (LULC, DEM, distance from rivers & roads, slope map) ✅ Correlation evaluation (Pearson’s, Cramér’s V, Joint Information Uncertainty) ✅ Area change analysis ✅ Transition potential modeling (Logistic Regression, Multi-layer Perceptron, Weights of Evidence) ✅ Running the simulation ✅ Validation 🔗 Watch the full tutorial on predicting future LULC here: 👉 https://lnkd.in/gR7UykpS 🔗 Link to my YouTube channel: 👉 https://lnkd.in/gttuvzD2 📂 Tutorials to download data from different sources 🔗 How to download DEM from USGS: 👉 https://lnkd.in/gzgbgYZW 🔗 How to download river, road, LULC, and DEM from Diva GIS 👉 https://lnkd.in/gqm5fAWG 🔗 How to download Landsat LULC from USGS: 👉 https://lnkd.in/g6dQ42QW 🔗 LULC Unsupervised classification: 👉 https://lnkd.in/gpyUq7gu 🔗 Use a shapefile to download any topography map from USGS: 👉 https://lnkd.in/gm5HQ2Sg ✨ Whether you’re a student, researcher, or GIS professional, this tutorial will help you understand how to predict and validate land cover. 📢 If you find this useful, please share or repost — it might help someone in your network! 🌟 Let’s learn, grow, and model better together! #QGIS #GIS #RemoteSensing #LULC #Geospatial #MOLUSCE #QGISTutorial #SpatialAnalysis #MachineLearning #WatershedManagement #ArcGIS #Hydrology #WaterResources #FreeCourse #BeramaAcademy #ClimateChange #HydrologicalModeling #Watershed #ANN

Bringing Reality into Autodesk Civil 3D In many infrastructure projects, visual context can make all the difference. That’s why I share a quick technical guide on how to drape an aerial image directly onto a Civil 3D surface, combining georeferenced terrain data with real-world imagery. This workflow allows you to: - Overlay aerial photos onto existing ground models - Enhance terrain visualization for design presentations and client reviews - Quickly extract and align Google Earth images when no georeferenced photo is available - Ensure full coordinate consistency using MAPCASSIGN and ALIGN tools By integrating imagery directly into your Civil 3D environment, your surfaces become much more intuitive, both visually and spatially. I’m sharing this document openly with the community to support those who want to explore the full potential of Civil 3D visualization workflows. Feel free to download, test it, and share your feedback. 👤 Ricardo Pombo #Autodesk #Civil3D #InfrastructureDesign #GIS #Visualization #EngineeringWorkflows #Intecsa

🌍 New Paper Alert: LandSegmenter 🛰️ How can we build a #FoundationModel when high-quality annotations are scarce, but noisy labels are available at global scale? I'm excited to share our latest work, led by my PhD student Chenying Liu at the Technical University of Munich: #LandSegmenter – Towards a Flexible Foundation Model for LULC Mapping. To address the data bottleneck, she first built #LAS (LAnd Segment), a large-scale dataset with: 🌐 ~150k globally distributed sample locations 🛰️ Multi-modal imagery from RGB, Planet, Sentinel-2, and Landsat 🏷️ A combination of precise annotations and large-scale weak labels Building on LAS, she developed LandSegmenter, a foundation model that: ✅ Supports diverse remote sensing modalities ✅ Uses language prompts for flexible LULC mapping ✅ Enables zero-shot segmentation on unseen datasets and taxonomies ✅ Leverages weak supervision to scale training beyond costly manual annotation Across six benchmark datasets, LandSegmenter demonstrates strong transferability and achieves state-of-the-art zero-shot performance for LULC mapping. This work highlights the potential of combining weak supervision, multimodal Earth observation data, and vision-language learning to build more scalable geospatial foundation models. 📄 Paper: https://lnkd.in/e5H77WtE 💻 Code & Dataset: https://lnkd.in/d9gKG5d3 Congratulations to Chenying Liu and @Wei Huang on this exciting work. This work is funded by Munich Center for Machine Learning. #RemoteSensing #FoundationModels #LandCover #LandUse #EarthObservation #GeoAI #AIforEarth #SemanticSegmentation #OpenScience #LandSegmenter

🌊 𝗛𝗼𝘄 𝗗𝗿𝗼𝗻𝗲𝘀 𝗛𝗲𝗹𝗽𝗲𝗱 𝗩𝗲𝗿𝗶𝗳𝘆 𝗥𝗶𝘃𝗲𝗿 𝗗𝗲𝘀𝗶𝗹𝘁𝗶𝗻𝗴 𝗮𝗻𝗱 𝗥𝗲𝗱𝘂𝗰𝗲 𝗙𝗹𝗼𝗼𝗱 𝗥𝗶𝘀𝗸 🚁 Can a drone audit hundreds of kilometers of river desilting more accurately than manual inspections? Recently, we completed a drone-based monitoring project for a river desilting program where the contractor was required to maintain: ✅ 10 m minimum channel width ✅ 2.5 m minimum desilting depth ✅ Continuous flow from rivers to canals Traditionally, auditing the entire route manually is time-consuming and challenging for Public Works Departments. Some sections may not meet the required width and depth specifications, making complete verification difficult. To solve this challenge, we used drones to capture high-resolution aerial data and generated a detailed 3D model of the entire desilted corridor. 🔍 Our workflow: 📌 Created a centerline along the river channel 📌 Applied a 5 m buffer on each side to verify the required 10 m width 📌 Generated cross-sections at regular intervals 📌 Measured the elevation difference between the highest and lowest riverbed points 📌 Verified whether the minimum 2.5 m desilting depth was achieved throughout the route 📊 This digital audit enabled quick identification of non-compliant sections and provided objective evidence for project verification. 🌧️ Benefits: ✅ Ensures proper water flow from rivers to canals ✅ Reduces flood and inundation risks during heavy rainfall ✅ Improves transparency and contractor accountability ✅ Saves significant time compared to manual inspections ✅ Creates a permanent digital record for future monitoring Drone mapping is not just about creating maps—it is about delivering measurable insights that support better infrastructure management and river safety. Have you used drone-generated 3D models for river desilt monitoring? Share your experience in the comments. 👇 🚀 Follow me https://lnkd.in/g2rMzfjh for more insights on Drone Surveying, Photogrammetry, GIS, AI, and Geospatial Digital Transformation. #DroneSurvey #DroneMapping #Photogrammetry #GIS #RiverManagement #FloodPrevention #InfrastructureMonitoring #DigitalTwin #UAV #Geospatial #3DModeling #PublicWorks #CivilEngineering #RemoteSensing #DigitalTransformation

Leveraging Drones for Enhanced Data Collection in Geospatial Fields 🚁 Transform Your Geospatial Projects with Drone Technology! As the geospatial industry continues to innovate, the use of drones is becoming a game-changer in data collection across various sectors. From environmental monitoring to urban planning even inspections, drones provide unparalleled access to data that was once difficult to obtain. As a geospatial professional, integrating drone technology into your projects can lead to more precise analyses and efficient workflows. Why Drones? 1. Accessibility and Precision: Drones can access remote or hazardous areas with ease, capturing high-resolution imagery and data that would be challenging to gather otherwise. This precision allows for more accurate spatial analysis and decision-making. 2. Cost-Effective Solutions: Utilizing drones can significantly reduce the time and cost associated with traditional data collection methods. With rapid deployment and real-time data transmission, drones streamline operations and enhance productivity. 3. Versatile Applications: Drones are being used in a multitude of geospatial applications. For instance, in agriculture, they monitor crop health and optimize yields. In urban planning, drones aid in mapping and assessing infrastructure development. QGIS and Drone Data Integration For those using QGIS, incorporating drone data has never been easier. QGIS supports various plugins and tools that allow you to process and analyze drone-captured data effectively. Here are some practical uses: *Orthomosaic Creation: Generate detailed maps that provide a comprehensive view of large areas. *Digital Elevation Models (DEM): Develop precise elevation models for terrain analysis. *3D Modeling: Create 3D visualizations of landscapes and structures to enhance understanding and presentation. Get Started Today! Embrace the power of drones and elevate your geospatial projects to new heights. Whether you’re looking to improve data accuracy, reduce costs, or explore new applications, drones offer limitless possibilities. Connect with fellow professionals to share insights and experiences, and stay ahead in this rapidly evolving field. 🔍 Are you ready to integrate drone technology into your geospatial work? Let’s connect and explore how drones can transform your projects! #DronesInGeospatial #QGIS #Innovation Feel free to reach out for more information or guidance on incorporating drones into your geospatial initiatives. Together, we can harness these cutting-edge tools to drive innovation and success in your career.