awesome-forests

Awesome-forests is a curated list of ground-truth, validation, and in-situ forest datasets specifically designed for the machine learning and forestry communities. This valuable resource addresses the significant challenge of finding organized datasets for forest analysis, thereby simplifying the process for users to embark on building robust machine learning models. Crucially, the list focuses on providing access to raw, foundational data, intentionally excluding algorithm-generated data products or generalized global maps.

The datasets are meticulously categorized to support a diverse array of analytical needs within forestry and environmental science. Key applications include tree species classification, accurate tree detection, and the detailed assessment of tree traits, damage, and health. Additionally, the repository features data relevant to biodiversity flora, precise tree crown segmentation, and the crucial quantification of both aboveground and belowground carbon.

Further categories encompass forest type and land cover classification, alongside essential tools for change detection and deforestation monitoring. This comprehensive and continually updated resource aims to significantly facilitate data-driven research and practical applications in vital areas such as biodiversity conservation, carbon sequestration strategies, wildfire prediction, and the broader analysis of ecosystem services within forest environments worldwide.