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GeoLangBind: Unifying Earth Observation with Agglomerative Vision-Language Foundation Models by Xiong, Zhitong; Wang, Yi; Yu, Weikang; Stewart, Adam J; Zhao, Jie; Lehmann, Nils; Dujardin, Thomas; Yuan, Zhenghang; Ghamisi, Pedram; Zhu, Xiao Xiang is a scholarly article available to read on EtoBox.

What is GeoLangBind: Unifying Earth Observation with Agglomerative Vision-Language Foundation Models about?

Earth observation (EO) data, collected from diverse sensors with varying imaging principles, present significant challenges in creating unified analytical frameworks. We present GeoLangBind, a novel agglomerative vision--language foundation model that bridges the gap between heterogeneous EO data modalities using language as a unifying medium. Our approach aligns different EO data types into a shared language embedding space, enabling seamless integration and complementary feature learning from diverse sensor data. To achieve this, we construct a large-scale multimodal image--text dataset, GeoLangBind-2M, encompassing six data modalities. GeoLangBind leverages this dataset to develop a zero-shot foundation model capable of processing arbitrary numbers of EO data channels as input. Through our designed Modality-aware Knowledge Agglomeration (MaKA) module and progressive multimodal weight merging strategy, we create a powerful agglomerative foundation model that excels in both zero-shot vision--language comprehension and fine-grained visual understanding. Extensive evaluation across 23 datasets covering multiple tasks demonstrates GeoLangBind's superior performance and versatility in

Author

Xiong, Zhitong; Wang, Yi; Yu, Weikang; Stewart, Adam J; Zhao, Jie; Lehmann, Nils; Dujardin, Thomas; Yuan, Zhenghang; Ghamisi, Pedram; Zhu, Xiao Xiang

Published

2025

Language

EN