GlobalGeoTree: A Multi-Granular Vision-Language Dataset for Global Tree Species Classification

TL;DR

GlobalGeoTree is a large-scale, multi-modal dataset for global tree species classification that combines remote sensing imagery, environmental data, and taxonomic labels to improve zero- and few-shot ecological modeling.

Contribution

This paper introduces GlobalGeoTree, the first extensive hierarchical dataset combining remote sensing and environmental data for global tree species classification, along with a baseline vision-language model, GeoTreeCLIP.

Findings

GeoTreeCLIP outperforms existing models in zero-shot classification.

The dataset enables improved biodiversity and ecological research.

Baseline results demonstrate the dataset's utility for ecological modeling.

Abstract

Global tree species mapping using remote sensing data is vital for biodiversity monitoring, forest management, and ecological research. However, progress in this field has been constrained by the scarcity of large-scale, labeled datasets. To address this, we introduce GlobalGeoTree, a comprehensive global dataset for tree species classification. GlobalGeoTree comprises 6.3 million geolocated tree occurrences, spanning 275 families, 2,734 genera, and 21,001 species across the hierarchical taxonomic levels. Each sample is paired with Sentinel-2 image time series and 27 auxiliary environmental variables, encompassing bioclimatic, geographic, and soil data. The dataset is partitioned into GlobalGeoTree-6M for model pretraining and curated evaluation subsets, primarily GlobalGeoTree-10kEval for zero-shot and few-shot benchmarking. To demonstrate the utility of the dataset, we introduce a baseline model, GeoTreeCLIP, which leverages paired remote sensing data and taxonomic text labels within a vision-language framework pretrained on GlobalGeoTree-6M. Experimental results show that GeoTreeCLIP achieves substantial improvements in zero- and few-shot classification on GlobalGeoTree-10kEval over existing advanced models. By making the dataset, models, and code publicly available, we aim to establish a benchmark to advance tree species classification and foster innovation in biodiversity research and ecological applications.