I have 200 geo-referenced avalanche samples already validated on Sentinel imagery. Within the next two days I need a basic, from-scratch machine-learning workflow that can locate additional avalanches in Sentinel data from 2016 through 2025 and export each detection as a GeoTIFF layer. Scope • Train a simple but effective model on the 200 labelled samples. • Run the model across the specified years of Sentinel scenes (same Area of Interest and spectral bands I used for the samples). • Save every positive detection as a single-band GeoTIFF with the original projection preserved. Key points • No pre-trained weights are available, so please build the model pipeline yourself. • Accuracy is important, yet I understand this is an initial, low-budget pass: focus on a clean, reproducible codebase (Python, Rasterio, GDAL, NumPy, scikit-learn or a lightweight deep-learning library) and solid avalanche masks rather than exhaustive hyper-parameter tuning. • Deliver within 48 hours. Deliverables 1. Python code/notebook with clear instructions. 2. Trained model file. 3. GeoTIFFs of detected avalanches (2016-2025). A short demo GIF or screenshot proving the detections load correctly in QGIS would be a plus.