Regression in EO: Are VLMs Up to the Challenge?

TL;DR

This paper explores the potential and challenges of adapting Vision Language Models for Earth Observation regression tasks, highlighting obstacles and proposing future research directions for improved environmental modeling.

Contribution

It systematically analyzes the unique challenges of applying VLMs to EO regression and offers insights and future directions for domain-specific solutions.

Findings

VLMs show promise for EO scientific regression tasks.

Identified key obstacles like data representation mismatch and lack of benchmarks.

Proposed future research directions for robust EO regression models.

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.