HyBiomass: Global Hyperspectral Imagery Benchmark Dataset for Evaluating Geospatial Foundation Models in Forest Aboveground Biomass Estimation

TL;DR

This paper introduces a global hyperspectral imagery benchmark dataset for forest aboveground biomass estimation, enabling comprehensive evaluation of geospatial foundation models across diverse regions and tasks.

Contribution

It provides the first large-scale, pixel-wise regression benchmark dataset combining hyperspectral imagery and biomass estimates for global forest regions.

Findings

Geo-FMs can match or outperform U-Net with fine-tuning

Performance varies with dataset size and token patch size

The dataset facilitates research on geographic bias and model generalization

Abstract

Comprehensive evaluation of geospatial foundation models (Geo-FMs) requires benchmarking across diverse tasks, sensors, and geographic regions. However, most existing benchmark datasets are limited to segmentation or classification tasks, and focus on specific geographic areas. To address this gap, we introduce a globally distributed dataset for forest aboveground biomass (AGB) estimation, a pixel-wise regression task. This benchmark dataset combines co-located hyperspectral imagery (HSI) from the Environmental Mapping and Analysis Program (EnMAP) satellite and predictions of AGB density estimates derived from the Global Ecosystem Dynamics Investigation lidars, covering seven continental regions. Our experimental results on this dataset demonstrate that the evaluated Geo-FMs can match or, in some cases, surpass the performance of a baseline U-Net, especially when fine-tuning the encoder. We also find that the performance difference between the U-Net and Geo-FMs depends on the dataset size for each region and highlight the importance of the token patch size in the Vision Transformer backbone for accurate predictions in pixel-wise regression tasks. By releasing this globally distributed hyperspectral benchmark dataset, we aim to facilitate the development and evaluation of Geo-FMs for HSI applications. Leveraging this dataset additionally enables research into geographic bias and generalization capacity of Geo-FMs. The dataset and source code will be made publicly available.