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Idea that came up during our regular meetings, on generalizing the cloud-cover percentage patch-level info (i.e. extending #168) to other bands/channels, so that someone could apply other filters based on certain columns with some statistics (mean, count, min/max, percentage, etc) derived from the input images. This would enable pre-filtering based on attributes when performing Similiarity Search.
Example:
embedding
cloud_cover_percentage
mean_elevation
max_temperature
bbox
[0.1, 0.4, ... x768]
20%
100m
25°C
POLYGON(...)
[0.2, 0.5, ... x768]
30%
300m
20°C
POLYGON(...)
[0.3, 0.6, ... x768]
40%
500m
15°C
POLYGON(...)
This would involve generalizing the inference part of the code somehow, specifically the predict_step function here:
Good idea, but not sure if this is still relevant. We won't always have a way to assess cloud cover in this predict step. @srmsoumya is this code still operational?
We don't have a predict_step in v1, we should add a script instead, that takes maybe a tile as input & creates embedding for chip size defined by the user.
This can be scalable & we could add AWS batch scripts for these.
Idea that came up during our regular meetings, on generalizing the cloud-cover percentage patch-level info (i.e. extending #168) to other bands/channels, so that someone could apply other filters based on certain columns with some statistics (mean, count, min/max, percentage, etc) derived from the input images. This would enable pre-filtering based on attributes when performing Similiarity Search.
Example:
This would involve generalizing the inference part of the code somehow, specifically the
predict_step
function here:model/src/model_clay.py
Lines 855 to 921 in 0145e55
Some changes might also need to happen on the DataLoader side, so that these statistical measures are passed through. Parking this as an idea for now.
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