RTS Segmentation Model v2

Semantic segmentation of Retrogressive Thaw Slumps (RTS) in Arctic satellite imagery for pan-arctic mapping.

Overview

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.

This README is the map of the repo — every canonical document is linked below, and the Source of truth table says where each kind of fact lives. Specs are the source of truth: always read the relevant doc before implementing (see CLAUDE.md §Rule 1).

Data

• 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

Document map

Project & process

Document	Purpose
CLAUDE.md	How to work in this repo: rules, structure, technical constraints, code style
current_working_status.md	Diary & roadmap — live status, key decisions, budget plan, next steps

Data

Document	Purpose
data/data.md	Data pipeline spec — sources, labels, splits, normalization, disk layout (§9 = EXTRA bands)
data/data_format.md	Format standards for all data (CRS, tile size, label values, dtypes)
data/datacheck.md	Data-validation checks at each lifecycle stage

Training

Document	Purpose
training/training.md	Model, loss, metrics, training loop, train–inference consistency contract
training/experiments.md	The phased experimentation plan (sequential elimination + multi-seed lock)
docs/baseline_unetpp_effb5.md	Living experiment record for the UNet++/EfficientNet-B5 baseline

Inference & post-inference

Document	Purpose
inference/inference.md	Deployment workflow — tiling, overlap aggregation, merging, vectorization
post-inference/post-inference.md	Post-processing, QC, evaluation, threshold tuning (spec in progress)

Computing

Document	Purpose
computing/infrastructure.md	Infra SSoT — GCP projects, buckets, VM inventory, regions, compute budget, data storage map
computing/vm_instruction.md	Daily VM/SSH how-to — start/stop, config, Python env, file transfer
computing/docker_training.md	Docker build/run how-to — image, mounts, GCS auth

Domain

Document	Purpose
domain/inference_domain.md	Inference domain and circumpolar subregions (H. Rodenhizer)
domain/training_data_distribution.md	Geographic/ecological distribution of the training data (H. Rodenhizer)

Tests

Document	Purpose
tests/tests.md	Test-suite living doc — per-test inventory, strictness, coverage gaps

Source of truth

This repo follows a single-source-of-truth standard. Where each kind of fact lives:

Concern	Source of truth
Config values — hyperparameters, paths, thresholds	configs/*.yaml (configs/baseline.yaml is primary)
MLflow tracking URI	configs/baseline.yaml:mlflow.tracking_uri
Core constants — CRS, tile size, label values, seed	CLAUDE.md §Technical Constraints
Data disk layout & EXTRA bands	data/data.md (§9 for bands)
Status, roadmap, decisions	current_working_status.md
Test inventory	tests/tests.md
Infra facts — projects, buckets, VMs, regions, budget	computing/infrastructure.md

Todos

1. training in multi-scale
2. explore GEE satellite embedding as input feature
3. 2025 micro set to test temporal domain shift