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🚀 A brand-new feature is coming to the SAMGeo Python package: interactive remote-sensing image segmentation with text prompts powered by SAM3. This update makes geospatial segmentation even more intuitive: just describe what you want to detect, and let GeoAI do the rest. ✨ GitHub PR: #GeoAI #SAM3 #OpenSource #Python

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Qiusheng Wu

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32,869 просмотров • 6 месяцев назад •via X (Twitter)

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🚀 SamGeo v0.14.0 is here! This release brings interactive segmentation for remote sensing imagery powered by Meta’s SAM3. 🟦 Draw a bounding box to automatically detect all similar objects across the image. 📝 Or just type a text prompt to segment objects instantly. No complex setup. No manual labeling. Just intuitive, AI-driven geospatial analysis. 🔗 GitHub: 🧪 Notebook: #SAM3 #GeoAI #Geospatial #OpenSource #Python #DataScience

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🚀 The GeoAI QGIS Plugin is here 🔥 You can run Moondream vision-language models, object detection, image segmentation (SAM 3), and even train your own geospatial segmentation model end-to-end. Website: GitHub: Short demo: Full video tutorial: #QGIS #GeoAI #SAM3 #Geospatial #DeepLearning #ComputerVision #OpenSource #Python
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🚀 The GeoAI QGIS Plugin is here 🔥 You can run Moondream vision-language models, object detection, image segmentation (SAM 3), and even train your own geospatial segmentation model end-to-end. Website: GitHub: Short demo: Full video tutorial: #QGIS #GeoAI #SAM3 #Geospatial #DeepLearning #ComputerVision #OpenSource #Python

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The GeoAI Python package now supports interactive segmentation of remote sensing imagery with the Moondream AI Vision Language Models (VLMs), which provides powerful and efficient visual reasoning. A great alternative to Meta's SAM 3. GitHub: Notebook example: #GeoAI #geospatial #AI #opensource
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The GeoAI Python package now supports interactive segmentation of remote sensing imagery with the Moondream AI Vision Language Models (VLMs), which provides powerful and efficient visual reasoning. A great alternative to Meta's SAM 3. GitHub: Notebook example: #GeoAI #geospatial #AI #opensource

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QGIS GeoAI Plugin v0.5.0 is out! I just released v0.5.0 of the QGIS GeoAI Plugin, bringing interactive image segmentation powered by DeepForest ( to your desktop GIS workflow. With this update, you can load pretrained DeepForest models to segment real-world objects such as trees, birds, livestock, dead trees, and more, directly inside QGIS. Try it out: Documentation: GitHub: #deepforest #geoai #QGIS #geospatial #OpenSource
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QGIS GeoAI Plugin v0.5.0 is out! I just released v0.5.0 of the QGIS GeoAI Plugin, bringing interactive image segmentation powered by DeepForest ( to your desktop GIS workflow. With this update, you can load pretrained DeepForest models to segment real-world objects such as trees, birds, livestock, dead trees, and more, directly inside QGIS. Try it out: Documentation: GitHub: #deepforest #geoai #QGIS #geospatial #OpenSource

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🚀 GeoAI Tutorial 19: Train a Semantic Segmentation Model for Objection Detection from Remote Sensing Imagery 🌍 Leverage any PyTorch segmentation model from — with hundreds of image encoders & pretrained weights available. 🎥 Watch the full tutorial here: 📓 Explore the notebook: 🛠️ Check out the GitHub repository: 📚 Dive into the documentation: 📺 Check out the entire YouTube playlist: #GeoAI #geospatial #AI #Python #DeepLearning
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🚀 Exciting update! The GeoAI Python package is getting even smarter — soon it will support AI agents. That means you’ll be able to use natural language to effortlessly download, analyze, and visualize geospatial data without coding 🌍🤖 Stay tuned for the upcoming release! 🔗 GitHub: #geospatial #GeoAI #Python #AI
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🚀 Exciting news! The GeoAI Python package just got a major upgrade. It now supports interactive geospatial data visualization powered by AI Agents. 🌍✨ GitHub: Full tutorial: 🌍 In this tutorial, you’ll learn how to build interactive maps powered by AI agents that respond to natural language queries. Watch how you can fly to any city, add raster or vector layers, visualize 3D buildings, and explore geospatial data—all without writing code. Perfect for developers, researchers, and anyone curious about AI, GIS, and interactive mapping. #geospatial #mapping #python #GeoAI #AI
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🚀 GeoAI Tutorial 23: Building Detection from Aerial Imagery and LiDAR Data 🏙️🛰️ In this tutorial, I demonstrate how to train a deep learning segmentation model for building detection by combining aerial/satellite imagery with LiDAR data. You'll learn how to fuse multi-source remote sensing data to improve model accuracy and robustness. 🔗 Tutorial video: 📓 Notebook example: 💻 GitHub repo: 📚 Documentation: 🎥 Full playlist: #GeoAI #geospatial #AI #Python #DeepLearning
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Agricultural Field Boundary Delineation (Instance Segmentation) with GeoAI Learn an end-to-end GeoAI workflow for agricultural field boundary delineation using instance segmentation and the Fields of the World dataset ( This workflow can be adapted to detect other object types (not just field boundaries) as long as training data is available. Video tutorial: Notebook: Web app: #geoai #geospatial #deeplearing
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1,890 swimming pools found across a 5 square mile suburban neighborhood with zero-shot image segmentation. Lower the confidence threshold to find even more! Use the interactive explorer in the {geosam} R package to try out Meta's SAM3 for satellite imagery analysis yourselves.
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Just received a proof copy of my new GeoAI book from Amazon! The printing quality is very good. It is now the #1 best-seller in GIS, Remote Sensing, and Computer Programming Languages. Grab your packpaer copy here: PDF edition available in English, Spanish, Chinese, French, and German: #geospatial #geoai #opensource
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Just received a proof copy of my new GeoAI book from Amazon! The printing quality is very good. It is now the #1 best-seller in GIS, Remote Sensing, and Computer Programming Languages. Grab your packpaer copy here: PDF edition available in English, Spanish, Chinese, French, and German: #geospatial #geoai #opensource

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35,749 просмотров • 2 месяцев назад

The new SAM3 model from Meta is blowing my mind Shown here: detecting putting greens, pools, and cars in Scottsdale from simple text prompts via Mapbox imagery R, Shiny, mapgl for the UI; Python backend via Qiusheng Wu's segment-geospatial package (thanks Qiusheng!)
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The new SAM3 model from Meta is blowing my mind Shown here: detecting putting greens, pools, and cars in Scottsdale from simple text prompts via Mapbox imagery R, Shiny, mapgl for the UI; Python backend via Qiusheng Wu's segment-geospatial package (thanks Qiusheng!)

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Found the perfect sport to stress test Meta's SAM3 person segmentation. Dense crowds, extreme motion, zero structure. This is as tough as it gets, and SAM3 nailed it.
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Found the perfect sport to stress test Meta's SAM3 person segmentation. Dense crowds, extreme motion, zero structure. This is as tough as it gets, and SAM3 nailed it.

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🚀 Sneak Peek: Interactive Visualization of the Planet Tanager Hyperspectral Data ( Using the HyperCoast Python package Stay tuned for notebook examples and video tutorials. GitHub: #geospatial #Tanager #Python #DataScience
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🚀 Sneak Peek: Interactive Visualization of the Planet Tanager Hyperspectral Data ( Using the HyperCoast Python package Stay tuned for notebook examples and video tutorials. GitHub: #geospatial #Tanager #Python #DataScience

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16,562 просмотров • 10 месяцев назад

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🚀 Excited to share a new tutorial on how to detect buildings 🏘️using the Segment Anything Model 2 (SAM 2) with Segment-Geospatial! You can choose interactive segmentation or utilize a GeoJSON file with building centroids or bounding boxes to do automated building detection. 📹 Check out the video tutorial: 📓 Explore the notebook: #Geospatial #OpenSource #AI #MachineLearning
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🚀 New Tutorial Alert! I just published a step-by-step guide on building an interactive dashboard with Python and Leafmap. 🌍📊 Explore the Jupyter Notebook: Watch the full video tutorial: #leafmap #geospatial #python #dataviz
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