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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 次观看 • 3 年前 •via X (Twitter)

#geospatial#segmentanything#deeplearning
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4 条评论

JCLB 💸 的头像
JCLB 💸3 年前

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

Will Ward 的头像
Will Ward3 年前

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

Qiusheng Wu 的头像
Qiusheng Wu3 年前

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

Xin Zhang 的头像
Xin Zhang3 年前

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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