README.md

metadata

title: EuroSAT RGB Land Cover Classifier
sdk: streamlit
app_file: app/streamlit-app.py

EuroSAT Land Cover Classification

CNN-based land cover classification on EuroSAT, comparing RGB imagery with 13-band Sentinel-2 multispectral input.

Streamlit RGB Demo

app/app.py is a Hugging Face Spaces-ready Streamlit demo for the EuroSAT-RGB ResNet-50 classifier. It shows an Esri World Imagery map centered on Bergen, Norway, lets a user draw a rectangle, fetches the corresponding RGB map tiles, and displays the predicted EuroSAT land cover class plus the top-3 class probabilities.

The RGB model was trained on EuroSAT-RGB tiles, which are about 64x64 pixels and roughly 640m on a side. Predictions on arbitrary map regions are illustrative; for best results, draw a rectangle of roughly 500m-1km on a side over land.

Classes: Annual Crop, Forest, Herbaceous Vegetation, Highway, Industrial Buildings, Pasture, Permanent Crop, Residential Buildings, River, SeaLake.

Validation accuracy on EuroSAT-RGB: 96.8%.

Main GitHub repo: davidlsan/EuroSAT-Land-Cover-Classification

Run Locally

Place the trained RGB checkpoint at:

weights/rgb_e15_best.pt

Install dependencies with uv and start the app:

uv sync
uv run streamlit run app/app.py

For Hugging Face Spaces, use the Streamlit SDK and include app/app.py, app/model_utils.py, app/tile_utils.py, requirements.txt, and the checkpoint at weights/rgb_e15_best.pt.

Notebooks

• [notebooks/01_data_exploration.ipynb](notebooks/01_data_exploration.ipynb) - RGB EDA (class balance, sample grid). Run from the repository root so PNGs land in [figures/](figures/).
• [notebooks/02_data_exploration_multispectral.ipynb](notebooks/02_data_exploration_multispectral.ipynb) - 13-band MSI EDA (class balance, composites, per-band stats, class-mean spectra, band correlation, RGB-MSI alignment).

Training

Run from the repository root:

python3 main.py --modality rgb --epochs 15 --batch-size 32 --num-workers 2 --lr 1e-3

CLI Flags

• --modality: values rgb or msi. RGB loads the blanchon/EuroSAT_RGB dataset, while MSI loads blanchon/EuroSAT_MSI.
• --epochs: number of full passes over the training split. Defaults to 15.
• --batch-size: number of samples per batch. Defaults to 32.
• --num-workers: DataLoader worker processes. Defaults to 2, but 0 is safer for debugging.
• --lr: learning rate for Adam. Defaults to 1e-3.
• --seed: seeds Python, NumPy, and PyTorch for best-effort reproducibility.