Sentinel-1 & Sentinel-2 data cubes at large scale (bigger-than-memory) on any machine with integrated cloud detection, snow masking, harmonization, merging, and temporal composites.
When using Sentle to generate a cube in my test case, the georeferencing seems to be not super stable. This can be seen as a "shaky" animation when putting the cube into Lexcube and animating through time. The area in this case is in the intersection area of two Sentinel tiles, which could be the only reason or just an amplification of an underlying problem.
Cube Generation:
from sentle import sentle
from rasterio.crs import CRS
if __name__ == "__main__":
da = sentle.process(
target_crs=CRS.from_string("EPSG:8857"),
bound_left=961600,
bound_bottom=6122140,
bound_right=963070,
bound_top=6123050,
datetime="2016-06-17/2024-06-17",
target_resolution=10,
S2_mask_snow=True,
S2_cloud_classification=True,
S2_cloud_classification_device="cuda",
S1_assets=None,
S2_apply_snow_mask=True,
S2_apply_cloud_mask=True,
time_composite_freq=None,
num_workers=30,
)
sentle.save_as_zarr(da, "test_cube.zarr")
When using Sentle to generate a cube in my test case, the georeferencing seems to be not super stable. This can be seen as a "shaky" animation when putting the cube into Lexcube and animating through time. The area in this case is in the intersection area of two Sentinel tiles, which could be the only reason or just an amplification of an underlying problem.
Cube Generation:
Visualization:
Result:
https://github.com/user-attachments/assets/a72cc67e-d9fd-4ff4-90cc-624375770196