Semantic segmentation of Retrogressive Thaw Slumps (RTS) in Arctic satellite imagery for pan-arctic mapping.
This project trains a deep learning model to detect RTS from PlanetScope basemap imagery (up to 74N) and deploys it for pan-arctic inference to produce an RTS survey map.
| Document | Description |
|---|---|
| Data Specification | Data sources, labeling rules, split strategy |
| Training Guide | Model architecture, loss, metrics, hyperparameters |
| Inference Pipeline | Deployment workflow, tiling, post-processing |
| Post-Inference | Post-processing, map-making, visualisation, Quality control, failure mode analysis, threshold tuning |
- Training: 2024 PlanetScope Quarterly Basemap (RGB 3m)
- Inference: 2025 PlanetScope Quarterly Basemap
- Labels: Refined from ARTS dataset on 2024 imagery(~2–3k positive, ~20–25k negative tiles)
- Auxiliary (optional): Sentinel-2 NDVI/NIR, ArcticDEM derivatives
Google Cloud Platform VM via PDG: https://docs.google.com/document/d/1BFwFRtXIYNjjQ7ovyEp6O1v31oTO8dSn8IDPotUBxhM/edit?pli=1&tab=t.0#heading=h.w9hi6k63xnp9
Dockerization