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MapBiomas Argentina Fire

Annual burned area mapping for Argentina using Landsat satellite imagery, produced by the MapBiomas Argentina team.


Collections

Collection Folder Status Coverage Model
Collection 0 collection-00/ Complete Patagonia Logistic regression (GEE JS + R)
Collection 1 collection-01/ In development All regions Regularized logistic regression (GEE Python API + R glmnet)

See each collection's README for what is where:

Before delving into the repo structure, we suggest reading the Algorithm Theoretical Basis Document of the latest collection (Collection 1).


Remotes

This repo has two git remotes:

Remote URL Role
origin barberaivan/mapbiomas-argentina-fire source of truth — every commit and push lands here, at whatever frequency work happens
mapbiomas mapbiomas/argentina-fire the official, public-facing mirror. Updated only explicitly and at low frequency (e.g. at milestones), never as a side effect of a normal commit/push

To publish a snapshot to the official repo: git push mapbiomas main.


Getting started (first-time setup)

This repository holds code only. The heavy data — training samples, fitted models, CV metrics, prediction plots — is not in git. It lives in a separate folder called mapbiomas-arg-fire-store (the "store"), and a small script links it into the repo.

Why? Code belongs in git/GitHub (which versions and backs it up); large data files do not. Keeping them apart avoids a bloated, slow git history and the sync conflicts that arise when a cloud-sync tool and git fight over the same files.

Setup is three steps:

1. Get the code

git clone <this-repo-url>
cd mapbiomas-arg-fire

2. Get the data store

Download it once and remember where you put it.

Google Drive (anyone): open the store folder → https://drive.google.com/drive/folders/1hmWZWkX1iHcGAGvqtv-5JSynvLnPx7wX → download it. Your browser saves it as a .zip; unzip it somewhere stable (e.g. your home folder). You should end up with a folder named mapbiomas-arg-fire-store containing collection-00/ and collection-01/ inside.

3. Link the store into the repo

From the repo root, run setup.sh once, giving it the path to the store you just got and — optionally — the path to your GEE Python interpreter:

# store only (Python falls back to system `python3`)
./setup.sh /full/path/to/mapbiomas-arg-fire-store

# store + the GEE venv you run pipeline scripts with (recommended)
./setup.sh /full/path/to/mapbiomas-arg-fire-store /full/path/to/venv/bin/python

That's it. The script creates the symlinks and remembers both paths (in a local, gitignored .local-paths file), so any later re-run is just ./setup.sh with no argument.

Why give it the Python path? Each machine's venv lives somewhere different, so the docs never hardcode it — they say "run with $PYTHON". setup.sh records your interpreter as $PYTHON in .local-paths (for your shell — source .local-paths) and in .claude/settings.local.json (so Claude Code's Bash uses the same one). Both files are gitignored, so two machines can each point $PYTHON at their own venv with nothing committed.

To confirm it worked:

ls collection-01/data          # should list training_observations_*.csv etc.
ls collection-01/models-store  # should list class_*_fit.rds etc.

Heads-up for contributors: because the data is outside git, uncommitted code is backed up nowhere — get in the habit of git commit && git push often. Work on one machine at a time, and git pull before you start. Collaborators without write access to the store upload any exported files to it manually.

Symlinks require Linux or macOS (or Windows with WSL / Developer Mode enabled).

Once set up, collection-01/docs/00-overview.md is where to start reading: the method in one page, and which step is which. Each step's own doc carries the commands to run it.


Regions

Code Region
BA Bosque Atlántico
CHACO Chaco
PAMPA Pampas
CUYO Monte, Puna y Altos Andes
PAT Patagonia

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MapBiomas Argentina — Fire.

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