File size: 2,458 Bytes
d574382 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 | # Preparing S1 + AEF + Dynamic World data
This repository intentionally does not distribute imagery, AlphaEarth embeddings, or labels. Use this guide to create compatible GeoTIFF triplets.
## Required rasters per training tile
| Raster | Bands | Data type | Meaning |
| --- | ---: | --- | --- |
| S1 input | 3 | float32 preferred | `VV`, `VH`, `angle`, in that order |
| AEF input | 64 | raw embedding values | Annual AlphaEarth Foundation embedding bands, in source order |
| DW target | 1 | integer or float | Binary mask: water=`1`, other=`0` |
The training code reads all values as float32, converts non-finite values to zero, and binarizes labels at `> 0.5`. Therefore, encode water as one and non-water as zero. Do not encode Dynamic World class IDs directly as the target: class ID zero means water, but would be interpreted as non-water by the trainer.
## Alignment checklist
For each S1/AEF/label triplet, ensure:
- exactly the same CRS, affine transform, width, and height;
- exactly one pixel grid, resolution, and area of interest;
- S1 is exactly three bands and AEF is exactly 64 bands;
- no duplicate filename exists anywhere under the AEF directory;
- file names match exactly when using directory-based loading.
The command below is a quick manual inspection for one file. Repeat it for its S1, AEF, and label partners and compare the reported grid fields.
```bash
gdalinfo data/s1/tile_0001.tif
gdalinfo data/aef/tile_0001.tif
gdalinfo data/labels/tile_0001.tif
```
## Example directory layout
```text
data/
s1/
tile_0001.tif
tile_0002.tif
aef/
tile_0001.tif
tile_0002.tif
labels/
tile_0001.tif
tile_0002.tif
```
Or create a manifest from `training_manifest.csv` and train only a selected `sample_id`:
```bash
python src/train.py \
--triplet_manifest examples/training_manifest.csv \
--triplet_sample_id demo_region \
--artifact_dir runs/demo_region \
--n_s1_bands 3 --n_aef_bands 64 --n_proj_bands 16 \
--loss ce_dice --batch_size 4 --crop_size 512
```
## Preparing inference data
Inference needs no Dynamic World target. Provide a directory of three-band S1 GeoTIFFs named `s1_YYYY-MM-DD.tif` and one 64-band annual AEF GeoTIFF. The inference code reprojects AEF windows to each S1 scene grid, but the S1 files in a single run must share the same CRS, transform, width, and height.
```text
data/inference/
s1/
s1_2025-01-15.tif
s1_2025-01-27.tif
alphaearth_2025.tif
```
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