Regression in EO: Are VLMs Up to the Challenge?
Xizhe Xue, Xiao Xiang Zhu
TL;DR
The paper investigates whether Vision Language Models can effectively address regression tasks in Earth Observation by contrasting EO with conventional CV data, identifying four key challenges (benchmark scarcity, discrete-continuous representation mismatch, cumulative error, and numerically-weak training objectives), and offering methodological insights to overcome them. It surveys EO regression domains, reviews EO-focused LLMs and VLMs (including contrastive and conversational approaches), and discusses problem settings that emphasize continuous numerical outputs and uncertainty. The proposed solutions include benchmark design, parallel representations with MoE, discrete guidance, dense attribute map generation, probabilistic modeling, masked autoregression, and targeted optimization, along with distillation and prompt engineering. The paper also highlights pitfalls such as information loss and scale variability, and outlines future directions like multi-sensor fusion, multi-temporal reasoning, multi-step regression, and uncertainty quantification. Overall, it lays a foundation for robust, domain-aware EO regression with VLMs, aiming to improve the precision and interpretability of environmental process modeling.
Abstract
Earth Observation (EO) data encompass a vast range of remotely sensed information, featuring multi-sensor and multi-temporal, playing an indispensable role in understanding our planet's dynamics. Recently, Vision Language Models (VLMs) have achieved remarkable success in perception and reasoning tasks, bringing new insights and opportunities to the EO field. However, the potential for EO applications, especially for scientific regression related applications remains largely unexplored. This paper bridges that gap by systematically examining the challenges and opportunities of adapting VLMs for EO regression tasks. The discussion first contrasts the distinctive properties of EO data with conventional computer vision datasets, then identifies four core obstacles in applying VLMs to EO regression: 1) the absence of dedicated benchmarks, 2) the discrete-versus-continuous representation mismatch, 3) cumulative error accumulation, and 4) the suboptimal nature of text-centric training objectives for numerical tasks. Next, a series of methodological insights and potential subtle pitfalls are explored. Lastly, we offer some promising future directions for designing robust, domain-aware solutions. Our findings highlight the promise of VLMs for scientific regression in EO, setting the stage for more precise and interpretable modeling of critical environmental processes.