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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....

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Qiusheng Wu

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33,700 görüntüleme • 11 ay önce •via X (Twitter)

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Rituraj profil fotoğrafı
Rituraj11 ay önce

Massive update. SAM3-powered segmentation inside SamGeo is a game-changer for remote sensing workflows. Text-prompt + box-prompt segmentation removes hours of manual labeling and finally brings true interactive GeoAI to everyday users. Huge step forward for geospatial AI.

Chirag profil fotoğrafı
Chirag11 ay önce

amazing! what are the use cases here?

Qiusheng Wu profil fotoğrafı
Qiusheng Wu11 ay önce

Check out the use cases of SAM 3 here:

Chirag profil fotoğrafı
Chirag11 ay önce

thanks more so for SamGeo?

Qiusheng Wu profil fotoğrafı
Qiusheng Wu11 ay önce

Applying SAM on geospatial data, satellite imagery, aerial imagery, drone imagery, etc. Think about Google Maps and Apple Maps. Some of their data are derived from high-resolution satellite imagery, and SAM certainly can be useful for cases like this and so many more.

Qiusheng Wu profil fotoğrafı
Qiusheng Wu11 ay önce

@ChiragCX Yes. Links in the post.

遥望行止 profil fotoğrafı
遥望行止11 ay önce

准备学习一下

ken zhu profil fotoğrafı
ken zhu11 ay önce

it can be perfectly used in some demographical analysis, like some distributing biz facilities' market potential calculation...

sohan profil fotoğrafı
sohan11 ay önce

Makes complex analysis simple. Great update!

Benzer Videolar

🚀 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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🚀 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

33,059 görüntüleme • 11 ay önce

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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11,786 görüntüleme • 11 ay önce

🚀 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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11,738 görüntüleme • 10 ay önce

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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21,385 görüntüleme • 9 ay önce

🚀 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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🚀 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

Qiusheng Wu

72,345 görüntüleme • 2 yıl önce

🚀 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

12,908 görüntüleme • 1 yıl önce

🚀 I am very excited to release the SamGeo QGIS plugin for geospatial image segmentation, powered by Meta’s Segment Anything Model (SAM 3) In this full tutorial, I’ll walk you through how to install, configure, and start segmenting satellite imagery in QGIS without writing a single line of code! 👉 Download the plugin here: 💻 Full video tutorial: #QGIS #SegmentAnything #SAM #GeoAI #RemoteSensing
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🚀 I am very excited to release the SamGeo QGIS plugin for geospatial image segmentation, powered by Meta’s Segment Anything Model (SAM 3) In this full tutorial, I’ll walk you through how to install, configure, and start segmenting satellite imagery in QGIS without writing a single line of code! 👉 Download the plugin here: 💻 Full video tutorial: #QGIS #SegmentAnything #SAM #GeoAI #RemoteSensing

Qiusheng Wu

29,228 görüntüleme • 11 ay önce

🚀 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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🚀 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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12,364 görüntüleme • 1 yıl önce

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Segment-geospatial v0.9.0 is out📢It now supports segmenting remote sensing imagery with box prompts. You can create box prompts interactively or use existing vector data as box prompts 🌐 GitHub: Notebook: Video tutorial: #geospatial #segmentanything #deeplearning

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24,200 görüntüleme • 3 yıl önce

Interactive well pad identification in the Permian Basin using Meta's Segment Anything Model 3. Draw a box around one well pad using Mapbox imagery; SAM3 finds similar objects in the view. So many use-cases for AI in oil & gas, and I'm not just talking about ChatGPT.
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Interactive well pad identification in the Permian Basin using Meta's Segment Anything Model 3. Draw a box around one well pad using Mapbox imagery; SAM3 finds similar objects in the view. So many use-cases for AI in oil & gas, and I'm not just talking about ChatGPT.

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12,214 görüntüleme • 9 ay önce

Nearly 1500 swimming pools found across a 10 square kilometer suburban area, all from a single text prompt. Meta's SAM3 and Mapbox imagery combine for incredible zero-shot geospatial intelligence.
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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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17,073 görüntüleme • 1 yıl önce

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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

Qiusheng Wu

16,642 görüntüleme • 1 yıl önce

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🚀 Visualizing Planet’s Tanager Hyperspectral Data in 3D! 🌍🛰️ Create an interactive image cube to explore the internal structure of all 426 spectral bands captured by Planet’s Tanager mission — going far beyond the traditional RGB view of desktop GIS tools. This approach allows for a deeper comparison of spectral characteristics across different land cover types and supports more advanced geospatial analysis. 📓 Try the notebook example: 💻 Explore the code on GitHub: #Hyperspectral #Geospatial #Tanager #Python #DataScience #OpenSource

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11,367 görüntüleme • 1 yıl önce

🚀 GeoAI Tutorial 12: Wetland Mapping with Deep Learning and GeoAI! 🌍 Unlock the power of your geospatial data! Learn to train an wetland detection model and run inference on large datasets—all with just a few lines of code. 🎥 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
0:20

🚀 GeoAI Tutorial 12: Wetland Mapping with Deep Learning and GeoAI! 🌍 Unlock the power of your geospatial data! Learn to train an wetland detection model and run inference on large datasets—all with just a few lines of code. 🎥 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

Qiusheng Wu

16,644 görüntüleme • 1 yıl önce

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!)

Kyle Walker

44,030 görüntüleme • 11 ay önce

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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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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20,089 görüntüleme • 7 ay önce

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.
1:00

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.

Kyle Walker

50,572 görüntüleme • 7 ay önce

I am excited to introduce OpenGeoAgent, a powerful open-source multimodal AI agent for automated geospatial analysis and visualization! It supports QGIS, Jupyter notebook, and Python scripting. In this tutorial, you’ll learn how to automate GIS workflows using natural language, generate maps, analyze satellite data, and even run complex hydrological models. You can even interact with the agent using voice commands (no typing needed). Video: GitHub: QGIS Plugin: #geospatial #GeoAgent #OpenSource #AI
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I am excited to introduce OpenGeoAgent, a powerful open-source multimodal AI agent for automated geospatial analysis and visualization! It supports QGIS, Jupyter notebook, and Python scripting. In this tutorial, you’ll learn how to automate GIS workflows using natural language, generate maps, analyze satellite data, and even run complex hydrological models. You can even interact with the agent using voice commands (no typing needed). Video: GitHub: QGIS Plugin: #geospatial #GeoAgent #OpenSource #AI

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63,819 görüntüleme • 6 ay önce