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REO-VLM: Transforming VLM to Meet Regression Challenges in Earth Observation

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arxiv 2412.16583 v1 pith:QRU5OQVM submitted 2024-12-21 cs.CV

Xizhe Xue , Guoting Wei , Hao Chen , Haokui Zhang , Feng Lin , Chunhua Shen , Xiao Xiang Zhu This is my paper
classification cs.CV
keywords regressiondatasetimagereo-vlmscientifictasksapplicationscapabilities
verification ladder T0 review T1 audit T2 compute T3 formal
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The rapid evolution of Vision Language Models (VLMs) has catalyzed significant advancements in artificial intelligence, expanding research across various disciplines, including Earth Observation (EO). While VLMs have enhanced image understanding and data processing within EO, their applications have predominantly focused on image content description. This limited focus overlooks their potential in geographic and scientific regression tasks, which are essential for diverse EO applications. To bridge this gap, this paper introduces a novel benchmark dataset, called \textbf{REO-Instruct} to unify regression and generation tasks specifically for the EO domain. Comprising 1.6 million multimodal EO imagery and language pairs, this dataset is designed to support both biomass regression and image content interpretation tasks. Leveraging this dataset, we develop \textbf{REO-VLM}, a groundbreaking model that seamlessly integrates regression capabilities with traditional generative functions. By utilizing language-driven reasoning to incorporate scientific domain knowledge, REO-VLM goes beyond solely relying on EO imagery, enabling comprehensive interpretation of complex scientific attributes from EO data. This approach establishes new performance benchmarks and significantly enhances the capabilities of environmental monitoring and resource management.

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Cited by 4 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. RemoteShield: Enable Robust Multimodal Large Language Models for Earth Observation

    cs.CV 2026-04 unverdicted novelty 6.0 of 10

    RemoteShield improves robustness of Earth observation MLLMs by training on semantic equivalence clusters of clean and perturbed inputs via preference learning to maintain consistent reasoning under noise.

  2. OSMDA: OpenStreetMap-based Domain Adaptation for Remote Sensing VLMs

    cs.CV 2026-03 conditional novelty 6.0 of 10

    A remote-sensing VLM can be adapted by having it read rendered OpenStreetMap maps paired with satellite images, then fine-tuning it on satellite images alone.

  3. Robust Onion: Peeling Open Vocab Object Detectors Under Noise

    cs.CV 2026-06 unverdicted novelty 5.0 of 10

    Empirical study finds OV-OD robustness driven by vision backbone and image domain via layer-wise feature collapse analysis, validated with a low-parameter robustness improvement on real data.

  4. Robust Onion: Peeling Open Vocab Object Detectors Under Noise

    cs.CV 2026-06 unverdicted novelty 4.0 of 10

    Empirical analysis shows open vocabulary object detector robustness is driven mainly by vision backbone and image domain via similar feature collapse patterns, with a lightweight NN & TK0 method improving real-world p...

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