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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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Qiusheng Wu

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17,073 views • 1 year ago •via X (Twitter)

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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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🚀 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

Qiusheng Wu

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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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🚀 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

Qiusheng Wu

32,950 views • 9 months ago

🚀 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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Qiusheng Wu

11,738 views • 9 months ago

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Qiusheng Wu

16,623 views • 1 year ago

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10,346 views • 10 months ago

🚀 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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An AI assistant for Google Earth Engine is now available in QGIS! You can explore and visualize any dataset from the 80+ PB Earth Engine Data Catalog using natural language, and run scalable geospatial analysis on demand without writing code. Every interaction automatically generates reusable code, making it easy to reproduce results, customize workflows, and transition from no-code to full scripting when needed. Upgrade the plugin to get started and bring AI-powered geospatial analytics directly into your QGIS workflow. GitHub: QGIS plugin: GeoAgent: #EarthEngine #QGIS #OpenSource #GeoAI #Geospatial
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26,043 views • 1 year ago

Daphne Koller says although biology is 5-7 years behind language models, the explosion in biological data means that AI will soon be able to make causal inferences about disease pathways
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Daphne Koller says although biology is 5-7 years behind language models, the explosion in biological data means that AI will soon be able to make causal inferences about disease pathways

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