EarthVQA (A multi-modal multi-task VQA dataset for remote sensing)
Introduced by Wang et al. in EarthVQA: Towards Queryable Earth via Relational Reasoning-Based Remote Sensing Visual Question Answering
Earth vision research typically focuses on extracting geospatial object locations and categories but neglects the exploration of relations between objects and comprehensive reasoning. Based on city planning needs, we develop a multi-modal multi-task VQA dataset (EarthVQA) to advance relational reasoning-based judging, counting, and comprehensive analysis. The EarthVQA dataset contains 6000 images, corresponding semantic masks, and 208,593 QA pairs with urban and rural governance requirements embedded.
Characteristics:
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Multi-level annotations: The paired image-mask-QA pairs assisst for relational reasoning-based remote sensing visual question answering.
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Applicable QA pairs: All QA pairs are designed based on the actual city planning needs.
Papers
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