TreeAI: a global database for tree species annotations and high-resolution aerial imagery

Accurate and scalable tree species identification remains a critical challenge for global forest monitoring and management. Despite the increasing availability of remotely sensed data, the lack of standardized, high-quality ground truth datasets limits the potential of supervised machine learning models in capturing the tree diversity of forest ecosystems across different environmental and geographic contexts. Prior studies have highlighted the need for global-scale, high-resolution datasets to develop robust algorithms capable of capturing the diversity of forest ecosystems.

Towards a benchmark dataset for tree species identification in high-resolution aerial imagery. To address this critical gap, we introduce the TreeAI database, an open-access dataset designed to support advanced research in tree species identification and forest dynamics. The database comprises 53 datasets (47 publicly available) from 32 countries, representing 61,158 annotated trees across 5,000 ha of forest ecosystems, and it is still growing.

The TreeAI database provides annotations paired with high-resolution imagery (RGB and near-infrared bands at 1–10 cm spatial resolution, with an average of 3.5 cm). The database offers three key advancements. First, its global representation spans diverse ecosystems, climates, and species, enhancing its applicability across regions. Second, including centimetre-scale orthophotos ensures sufficient detail for identifying subtle differences between species. Finally, its community-driven design fosters ongoing contributions and ensures a dynamic dataset that evolves with the field's needs.

Preliminary tree species identification analysis using deep learning algorithms conducted for Switzerland, with very heterogeneous forest ecosystems and challenging topography, yielded promising results. The average F1-score for nine common species was 0.72, with Larix spp., Picea abies, and Tilia spp. exceeding 0.80. The mean average precision (mAP) across all the species was 0.76. These findings underscore the potential of the TreeAI. To further harness TreeAI’s potential, a scientific competition will be launched in 2025, challenging participants to develop deep-learning algorithms that maximize tree species identification accuracy across a broad range of forest ecosystems.

The impact of a global database for tree species annotations. The TreeAI database serves as a benchmark dataset for advancing artificial intelligence models, enabling automated forest inventory systems. This capability allows for the creation of high-resolution maps detailing tree species distributions, which can be used by researchers and practitioners for applications such as forest management, biodiversity monitoring, and ecosystem conservation. Moreover, the dataset complements existing National Forest Inventory (NFI) data, providing additional resources for point-based regional studies and enhancing ecological research at finer scales. Furthermore, the database promotes the refinement of AI models for practical forestry applications, fostering innovation in open science and collaborative research.

Further needs and collaboration potential: i.) expanding its geographic and tree species coverage, such as tropical forests, which remain inadequately sampled in existing datasets. ii.) integrating TreeAI with Earth observation platforms, such as Planet Scope, Sentinel-2, and GEDI. iii.) exploring methods to enhance data accessibility and interoperability, ensuring that the database meets the evolving needs of its users. Feedback from the broader forestry community will be instrumental in shaping these developments, emphasising addressing challenges related to data standardization, processing efficiency, and algorithm performance.

This contribution is based upon work from COST Action CA20118, supported by COST (European Cooperation in Science and Technology).