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Aniruddha Chowdhury

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repliedto Tonic's post about 13 hours ago

🙋🏻‍♂️ Hey there folks , Turns out : if we predict 🌏 earth we can save a lot of time looking for interesting things and less time looking at things that we expect to see. Sentinel-2 imagery 🛰️basically takes a long time to download towards earth. so our "near real time" systems are quite far from that in practical terms. meanwhile , if we "predict" what we will see , based on what we do see , we can send down much less data in a timely way , and prioritize 📡earth-bound response . I'm talking about illegal fishing , logging , mining or building in nature reserves , the more of that we predict early the more we're able to stop it on time. At least that's the concept ! check out the blog : https://huggingface.co/blog/Tonic/save-patagonia-by-predicting-earth - Collection: https://huggingface.co/collections/NuTonic/earth-observation-with-temporal-and-general-understanding - Code: https://github.com/Josephrp/Nutonic - Dataset: https://huggingface.co/datasets/NuTonic/sat-vl-sft-training-ready-v1 - Model: https://huggingface.co/NuTonic/lspace - Training: https://huggingface.co/spaces/NuTonic/lspace-trackio - Evals: https://huggingface.co/datasets/NuTonic/Patagonia_Eval

reacted to Tonic's post with 🔥 about 13 hours ago

🙋🏻‍♂️ Hey there folks , Turns out : if we predict 🌏 earth we can save a lot of time looking for interesting things and less time looking at things that we expect to see. Sentinel-2 imagery 🛰️basically takes a long time to download towards earth. so our "near real time" systems are quite far from that in practical terms. meanwhile , if we "predict" what we will see , based on what we do see , we can send down much less data in a timely way , and prioritize 📡earth-bound response . I'm talking about illegal fishing , logging , mining or building in nature reserves , the more of that we predict early the more we're able to stop it on time. At least that's the concept ! check out the blog : https://huggingface.co/blog/Tonic/save-patagonia-by-predicting-earth - Collection: https://huggingface.co/collections/NuTonic/earth-observation-with-temporal-and-general-understanding - Code: https://github.com/Josephrp/Nutonic - Dataset: https://huggingface.co/datasets/NuTonic/sat-vl-sft-training-ready-v1 - Model: https://huggingface.co/NuTonic/lspace - Training: https://huggingface.co/spaces/NuTonic/lspace-trackio - Evals: https://huggingface.co/datasets/NuTonic/Patagonia_Eval

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