Forest monitoring for the Chocó forests of western Ecuador, a global biodiversity hotspot
ChocoForestWatch is an advanced forest monitoring system that monitors deforestation and forest cover changes across the Chocó bioregion of western Ecuador. Using high-resolution satellite imagery and machine learning, we provide researchers, conservationists, and policymakers with the tools needed to protect one of the world's most biodiverse and threatened regions.
🗺️ Interactive Mapping Interface - Explore forest cover changes with an intuitive web-based mapping platform
🛰️ Satellite Image Analysis - Monthly NICFI satellite imagery processed with advanced machine learning algorithms
🤖 ML-Powered Detection - XGBoost models trained on expert-annotated data to identify deforestation patterns
- Data Processing Workflow - Complete guide for processing NICFI satellite imagery and STAC integration
- ML Pipeline Workflow - End-to-end machine learning pipeline including feature engineering and model training
- Versioning System - Application and dataset versioning guidelines and implementation
- GFW Deforestation Alerts - Integration with Global Forest Watch deforestation monitoring
- Western Ecuador Stats Caching - Performance optimization for regional statistics
- Forest Flag Algorithms - Comprehensive comparison of temporal algorithms for forest classification
- Model Fitting Process - Hyperparameter tuning, cross-validation, and model optimization techniques
- Analytics Setup - Umami analytics integration for usage tracking
Frontend: Quasar (Vue.js) with OpenLayers mapping
Backend: Django REST API with PostGIS for geospatial data
Database: PostgreSQL with PostGIS and PGSTAC extensions
ML Pipeline: Python with XGBoost
Docker-based development and deployment workflow
TiTiler dynamic tile server for satellite imagery visualization
Data Storage: STAC-compliant object storage for satellite imagery
We welcome contributions from researchers, developers, and conservationists!
- Report Issues: Found a bug or have a feature request? Open an issue
- Documentation: Help improve our guides and documentation
This project is licensed under the MIT License. See LICENSE for details.