Show Me What and Where has Changed? Question Answering and Grounding for Remote Sensing Change Detection

• URL: http://arxiv.org/abs/2410.23828v2
• Date: Wed, 13 Nov 2024 09:06:18 GMT
• Title: Show Me What and Where has Changed? Question Answering and Grounding for Remote Sensing Change Detection
• Authors: Ke Li, Fuyu Dong, Di Wang, Shaofeng Li, Quan Wang, Xinbo Gao, Tat-Seng Chua,
• Abstract summary: We introduce a new task named Change Detection Question Answering and Grounding (CDQAG) CDQAG extends the traditional change detection task by providing interpretable textual answers and intuitive visual evidence. We construct the first CDQAG benchmark dataset, termed QAG-360K, comprising over 360K triplets of questions, textual answers, and corresponding high-quality visual masks.
• Score: 82.65760006883248
• License:
• Abstract: Remote sensing change detection aims to perceive changes occurring on the Earth's surface from remote sensing data in different periods, and feed these changes back to humans. However, most existing methods only focus on detecting change regions, lacking the capability to interact with users to identify changes that the users expect. In this paper, we introduce a new task named Change Detection Question Answering and Grounding (CDQAG), which extends the traditional change detection task by providing interpretable textual answers and intuitive visual evidence. To this end, we construct the first CDQAG benchmark dataset, termed QAG-360K, comprising over 360K triplets of questions, textual answers, and corresponding high-quality visual masks. It encompasses 10 essential land-cover categories and 8 comprehensive question types, which provides a valuable and diverse dataset for remote sensing applications. Furthermore, we present VisTA, a simple yet effective baseline method that unifies the tasks of question answering and grounding by delivering both visual and textual answers. Our method achieves state-of-the-art results on both the classic change detection-based visual question answering (CDVQA) and the proposed CDQAG datasets. Extensive qualitative and quantitative experimental results provide useful insights for developing better CDQAG models, and we hope that our work can inspire further research in this important yet underexplored research field. The proposed benchmark dataset and method are available at https://github.com/like413/VisTA.

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