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Isaac Corley

@isaaccorley_ • 1,215 subscribers

head of ai/ml research @TaylorGeoAI | torchgeo maintainer | prev @MSFTResearch @BlackSky_Inc @ORNL

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If you enjoyed the TerraBit app (1), then you'll enjoy knowing that I'm open sourcing it! (2) Feel free to contribute, optimize or build on top of it! If you like the aesthetic of the app, I've also created a `geospatial-frontend` SKILL.md by scraping my convos with Claude! You can install it from the geospatial-skills repo! (3) (1) (2) (3)

If you enjoyed the TerraBit app (1), then you'll enjoy knowing that I'm open sourcing it! (2) Feel free to contribute, optimize or build on top of it! If you like the aesthetic of the app, I've also created a `geospatial-frontend` SKILL.md by scraping my convos with Claude! You can install it from the geospatial-skills repo! (3) (1) (2) (3)

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𝗙𝗼𝗿 𝘁𝗵𝗲 𝗳𝗶𝗿𝘀𝘁 𝘁𝗶𝗺𝗲 𝗶𝗻 𝗵𝗶𝘀𝘁𝗼𝗿𝘆, 𝗮𝗴𝗿𝗶𝗰𝘂𝗹𝘁𝘂𝗿𝗮𝗹 𝗳𝗶𝗲𝗹𝗱 𝗯𝗼𝘂𝗻𝗱𝗮𝗿𝗶𝗲𝘀 𝗵𝗮𝘃𝗲 𝗯𝗲𝗲𝗻 𝗺𝗮𝗽𝗽𝗲𝗱 𝗮𝘁 𝗴𝗹𝗼𝗯𝗮𝗹 𝘀𝗰𝗮𝗹𝗲. I've been collaborating with the Fields of the World (FTW) organization for over a year through Taylor Geospatial and it's finally released publicly in cloud storage for anyone to use, at no cost.

𝗙𝗼𝗿 𝘁𝗵𝗲 𝗳𝗶𝗿𝘀𝘁 𝘁𝗶𝗺𝗲 𝗶𝗻 𝗵𝗶𝘀𝘁𝗼𝗿𝘆, 𝗮𝗴𝗿𝗶𝗰𝘂𝗹𝘁𝘂𝗿𝗮𝗹 𝗳𝗶𝗲𝗹𝗱 𝗯𝗼𝘂𝗻𝗱𝗮𝗿𝗶𝗲𝘀 𝗵𝗮𝘃𝗲 𝗯𝗲𝗲𝗻 𝗺𝗮𝗽𝗽𝗲𝗱 𝗮𝘁 𝗴𝗹𝗼𝗯𝗮𝗹 𝘀𝗰𝗮𝗹𝗲. I've been collaborating with the Fields of the World (FTW) organization for over a year through Taylor Geospatial and it's finally released publicly in cloud storage for anyone to use, at no cost.

107,209 просмотров • 4 месяцев назад

What if you could search 50M satellite image embeddings with no server, no database, and no API? Part 2 of Caleb Robinson and my series on Compressing Earth Embeddings is live at We binarized the global Clay v1.5 Sentinel-2 embeddings from 183GB → 7GB and built TerraBit — a retrieval demo that runs with zero backend. DuckDB-WASM, cleverly partitioned GeoParquet on S3, and brute-force Hamming in an in-memory index makes the experience feel too good to be true. If you also wondered how Google's TurboQuant fares on compressing earth embeddings -- don't worry, we got you covered in the blog -- hint: TurboQuant int4 is a solid choice

What if you could search 50M satellite image embeddings with no server, no database, and no API? Part 2 of Caleb Robinson and my series on Compressing Earth Embeddings is live at We binarized the global Clay v1.5 Sentinel-2 embeddings from 183GB → 7GB and built TerraBit — a retrieval demo that runs with zero backend. DuckDB-WASM, cleverly partitioned GeoParquet on S3, and brute-force Hamming in an in-memory index makes the experience feel too good to be true. If you also wondered how Google's TurboQuant fares on compressing earth embeddings -- don't worry, we got you covered in the blog -- hint: TurboQuant int4 is a solid choice

43,250 просмотров • 5 месяцев назад

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