Environmental Monitor

A geospatial ML pipeline for monitoring and forecasting spectral indices derived from Sentinel-2 satellite imagery. The time series of NDVI, BSI, NDMI, and NBR are computed and stored as parquet files in S3. A web app hosted on Hugging Face Spaces visualizes the time series for registered areas of interest (AOIs), with XGBoost forecasts and model metrics. Please follow this link to view the dashboard.

Architecture

GitHub Actions (every 2 weeks)
    └── pipeline.py
            ├── data_download.py  →  S3: {country}/{aoi}/ts/*.parquet
            ├── forecast_ts.py    →  S3: {country}/{aoi}/ml/model_*.pkl
            │                        S3: {country}/{aoi}/ml/metrics_*.json
            │                        S3: {country}/{aoi}/ml/forecast_*.parquet
            └── aois.json         →  S3: aois.json

Hugging Face Spaces (always on)
    └── app.py  ←  reads S3 on page load via DataReader

Indices

Index	Description
NDVI	Normalized Difference Vegetation Index
BSI	Bare Soil Index
NDMI	Normalized Difference Moisture Index
NBR	Normalized Burn Ratio

Adding a new AOI

from scripts.pipeline import Pipeline

p = Pipeline(country="syria", aoi_name="Aleppo", bbox=[...])
p.run(lat=36.2021, lon=37.1343, rad=1000)

The AOI is registered in s3://environment-monitor/aois.json and will appear in the dashboard dropdown on next page load. Future pipeline runs will include it automatically.

Environment variables (set as Space secrets)

Variable	Description
AWS_ACCESS_KEY_ID	IAM user access key
AWS_SECRET_ACCESS_KEY	Corresponding secret
AWS_DEFAULT_REGION	e.g. us-east-1
BUCKET_NAME	S3 bucket name

Local development

cp .env.example .env          # fill in your credentials
pip install -r requirements-dashboard.txt
python app.py                 # opens on http://localhost:7860

Pipeline (separate from dashboard)

pip install -r requirements.txt
python run_pipeline.py                  # runs all AOIs
python run_pipeline.py --aoi Damascus  # runs one AOI