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

M. C. Hansen et al., “High-resolution global maps of 21st-century forest cover change,” Science

2013

Earlier work this paper cites.

L. Guanter et al., “The EnMAP spaceborne imaging spectroscopy mission for earth observation,” Remote Sensing

2015

Earlier work this paper cites.

O. Ronneberger, P. Fischer, T. Brox, “U-net: Convolutional networks for biomedical image segmentation,” International Conference on Medical image computing and computer-assisted intervention

2015

Earlier work this paper cites.

J. M. B. Carreiras et al., ”Coverage of high biomass forests by the ESA BIOMASS mission under defense restrictions,” Remote Sensing of Environment

2017

Earlier work this paper cites.

T. Xiao, Y. Liu, B. Zhou, Y. Jiang, and J. Sun, “Unified perceptual parsing for scene understanding,” Proc. Eur. Conf. Comput. Vis. (ECCV)

2018

Earlier work this paper cites.

M. Réjou‑Méchain et al., “Upscaling forest biomass from field to satellite measurements: Sources of errors and ways to reduce them,” Surveys in Geophysics

2019

Earlier work this paper cites.

R. Dubayah et al., “The global ecosystem dynamics investigation: High-resolution laser ranging of the Earth’s forests and topography,” Science of Remote Sensing

2020

Earlier work this paper cites.

K. He, H. Fan, Y. Wu, S. Xie, R. Girshick, “Momentum contrast for unsupervised visual representation learning,” Proc. IEEE/CVF Conf. Comp. Vis. Pattern Recognit. (CVPR)

2020

Cited alongside, same era.

Y. Cong et al., “SatMAE: Pre-training transformers for temporal and multi-spectral satellite imagery,” Proc. NeurIPS

2022

Cited alongside, same era.

K. Lahssini, N. Baghdadi, G. le Maire, I. Fayad, “Influence of GEDI acquisition and processing parameters on canopy height estimates over tropical forests,” Remote Sensing

2022

Cited alongside, same era.

K. He, X. Chen, S. Xie, Y. Li, P. Dollár, R. Girshick, “Masked autoencoders are scalable vision learners,” Proc. IEEE/CVF Conf. Comp. Vis. Pattern Recognit. (CVPR)

2022

Cited alongside, same era.

M. H. P. Fuchs, B. Demir, “Hyspecnet-11k: A large-scale hyperspectral dataset for benchmarking learning-based hyperspectral image compression methods,” arXiv preprint

N. Ait Ali Braham, C. M. Albrecht, J. Mairal, J. Chanussot, Y. Wang, X. X. Zhu, “SpectralEarth: Training hyperspectral foundation models at scale,” arXiv preprint

Original

2024

Later among the works it cites.

Z. Xiong et al., “Neural plasticity-inspired multimodal foundation model for earth observation,” arXiv preprint

Original

2024

Later among the works it cites.

P. Burns, C. R. Hakkenberg, S. J. Goetz, “Multi-resolution gridded maps of vegetation structure from GEDI,” Scientific Data

2024

Later among the works it cites.

V. Marsocci et al., “PANGAEA: A global and inclusive benchmark for geospatial foundation models,” arXiv preprint

Original

2024

Later among the works it cites.

M. Muszynski et al., “Fine-tuning of geospatial foundation models for aboveground biomass estimation,” arXiv preprint