Inference container:
This module enables users to create an inference pipeline that take a Sentinel-2 STAC Item from the Planetary Computer, and generates a binary mask TIFF image using a pre-trained CNN model. For details on how the model was trained, refer to the training container documentation.
Make Inference Module:
Inputs:
- input_reference
: The reference to a Sentinel-2 product on planetary computer. The application will give you an accurate result if the sentinel-2 product has no/low cloud-cover.
Outputs:
{STAC_ITEM_ID}_classified.tif
: A binary.tif
image inCOG
format classifies:
Class ID | Class Name |
---|---|
0 | AnnualCrop |
1 | Forest |
2 | HerbaceousVegetation |
3 | Highway |
4 | Industrial |
5 | Pasture |
6 | PermanentCrop |
7 | Residential |
8 | River |
9 | SeaLake |
10 | No Data |
overview_{STAC_ITEM_ID}_classified.tif
: A binary.tif
image inCOG
format classifies:
Class ID | Class Name |
---|---|
0 | AnnualCrop |
1 | Forest |
2 | HerbaceousVegetation |
3 | Highway |
4 | Industrial |
5 | Pasture |
6 | PermanentCrop |
7 | Residential |
8 | River |
9 | SeaLake |
10 | No Data |
STAC objects
: STAC objects related to the provided masks, including STAC catalog and STAC Item.
How the Application Works
The application begins by reading a Sentinel-2 STAC Item from the Planetary Computer. It then filters and selects 12 specific asset references in the order expected by the machine learning model. These assets correspond to common Sentinel-2 bands, as shown below:
Index | Asset Key | Asset Common Name |
---|---|---|
1 | B01 | Coastal |
2 | B02 | Blue |
3 | B03 | Green |
4 | B04 | Red |
5 | B05 | Red Edge |
6 | B06 | Red Edge |
7 | B07 | Red Edge |
8 | B08 | NIR |
9 | B8A | Narrow NIR |
10 | B09 | Water Vapor |
11 | B11 | SWIR 1 (16) |
12 | B12 | SWIR 2 (22) |
As a preprocessing step, all selected assets are resampled to a uniform resolution of 10 meters.
The pipeline then proceeds with a sliding window approach: it reads and stacks small image chips from the selected bands in the order listed above. These chips are fed into a trained CNN model, which predicts the corresponding class for each chip.
Finally, the application generates: - The classification prediction map (as a GeoTIFF mask) - A visual overview image - An updated STAC item containing metadata and references to the output files