EO中的回归：VLM能否应对挑战？
Regression in EO: Are VLMs Up to the Challenge?
AI总结：
本文系统分析VLM用于EO科学回归任务的数据特性、四大核心障碍、方法陷阱与未来方向，论证其潜力。
摘要：
地球观测（Earth Observation, EO）数据涵盖范围广泛的遥感信息，具有多传感器和多时相特征，在理解地球动态方面发挥着不可或缺的作用。近年来，视觉语言模型（Vision Language Models, VLMs）在感知和推理任务中取得了显著成功，为EO领域带来了新的见解和机遇。然而，其在EO应用中的潜力，尤其是与科学回归相关的应用，仍基本未得到探索。本文通过系统考察将VLM适配于EO回归任务所面临的挑战与机遇来弥补这一空白。讨论首先对比EO数据与传统计算机视觉数据集的独特属性，随后识别将VLM应用于EO回归的四个核心障碍：1）缺乏专用基准；2）离散与连续表示之间的错配；3）累积误差；以及4）以文本为中心的训练目标对数值任务而言并非最优。接下来，本文探讨了一系列方法层面的见解以及潜在的细微陷阱。最后，我们为设计稳健、具备领域感知能力的解决方案提出了若干有前景的未来方向。研究结果凸显了VLM在EO科学回归中的前景，为对关键环境过程进行更精确、更可解释的建模奠定基础。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
