favyen

3 ms·

1. 1.▲
   by favyen 3y ago

   Yes, I think this is part of it -- when the model sees a new low-res image, it compares it to patterns that it has already seen to estimate what that location might look like at high-res. The other important part is that the model inputs ma
2. 2.▲
   by favyen 3y ago

   We plan to eventually add some real paid high-res imagery to the map just as a comparison, but for now you would need to look at the map at https://satlas.allen.ai/map (select Super Resolution) and compare it to a source of
3. 3.▲
   by favyen 3y ago

   The marine infrastructure (offshore platform and offshore wind turbine) and super-resolution models only use RGB bands (B04, B03, B02), while the solar farm, onshore wind turbine, and tree cover models use 9 Sentinel-2 bands (add B05, B06,
4. 4.▲
   by favyen 3y ago

   Thanks for pointing this out! We will look into fixing it.
5. 5.▲
   by favyen 3y ago

   I fully agree that errors in extracted data can lead to making incorrect decisions/policies. Even for applications where accuracy is paramount, though, I think error-prone models still have their uses: - For applications that only need
6. 6.▲
   by favyen 3y ago

   The models we use to extract the geospatial data (like solar farm and offshore platform positions) from Sentinel-2 imagery are currently separate from the Sentinel-2 upscaling model, which is a more exploratory project. We report the accura