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Mapping swimming pools 🏊‍♀️ interactively with text prompts and the Segment Anything Model 🤩 Notebook: GitHub: #geospatial #segmentanything #deeplearning

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Qiusheng Wu

49,236 subscribers

30,498 görüntüleme • 3 yıl önce •via X (Twitter)

#geospatial#segmentanything#deeplearning
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JCLB 💸 profil fotoğrafı
JCLB 💸3 yıl önce

France paid 26 million USD to Capgemini for this....

Will Ward profil fotoğrafı
Will Ward3 yıl önce

Are you seeing this used for property tax assessments? Or identifying supply & water resources in the western US?

Qiusheng Wu profil fotoğrafı
Qiusheng Wu3 yıl önce

Probably. I only provide the tool. It is up to others how they want to use it.

Xin Zhang profil fotoğrafı
Xin Zhang3 yıl önce

Looks interesting! If I wanna segment something over the whole US, it seems time-cost ...

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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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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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🚀 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

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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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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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🚀 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

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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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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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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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Visualizing Google-Microsoft Open Building Datasets (165 GB) with #PMTiles and #leafmap with just a few lines of code 🗺️ App: Notebook: GitHub: #geospatial #opendata #dataviz

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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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🚀 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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🚀 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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🚀 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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Monarcha (Monarcha) is the AI-native mapping tool for planners, developers & environmental teams - turning prompts into geospatial analysis in seconds. Congrats on the launch, James Spokes and @EverettLee41320!

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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

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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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🚀 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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Detecting airplanes at DFW Airport from a single text prompt. Meta's Segment Anything Model 3 and the geosam R package make powerful image detection tools accessible.
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