Awesome Forests: Curated Ground-Truth Data for Machine Learning

This collection of ground-truth forest datasets, curated by blutjens as of August 2025, targets machine learning applications in forestry and climate change. It organizes external resources by domain, including carbon sequestration, biodiversity, and ecosystem health.

Use Cases

• Training deep learning models for forest cover classification using linked ground-truth imagery
• Analyzing carbon sequestration patterns across different ecosystems via biodiversity datasets
• Benchmarking climate change impact models using curated forestry records

Strengths

• Curated specifically for machine learning and deep learning applications
• Includes ground-truth labels for forestry and ecosystem monitoring
• Released under a CC0-1.0 public domain license for unrestricted use

Limitations

• Acts as a directory of external links rather than a unified data repository
• Lacks a standardized schema or unified file format across the linked sources
• Metadata for individual datasets like row counts or specific columns is not centralized

Provenance

Source

GitHub repository by blutjens

Collection Method

curated list

Freshness

Updated August 2025

Geography

global