XeMap: Contextual Referring in Large-Scale Remote Sensing Environments

• URL: http://arxiv.org/abs/2505.00738v1
• Date: Wed, 30 Apr 2025 02:14:39 GMT
• Title: XeMap: Contextual Referring in Large-Scale Remote Sensing Environments
• Authors: Yuxi Li, Lu Si, Yujie Hou, Chengaung Liu, Bin Li, Hongjian Fang, Jun Zhang,
• Abstract summary: XeMap task focuses on contextual, fine-grained localization of text-referred regions in large-scale RS scenes.<n>XeMap-Network handles the complexities of pixel-level cross-modal contextual referring mapping in RS.<n>HMSA module aligns multiscale visual features with the text semantic vector, enabling precise multimodal matching.
• Score: 13.162347922111056
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Advancements in remote sensing (RS) imagery have provided high-resolution detail and vast coverage, yet existing methods, such as image-level captioning/retrieval and object-level detection/segmentation, often fail to capture mid-scale semantic entities essential for interpreting large-scale scenes. To address this, we propose the conteXtual referring Map (XeMap) task, which focuses on contextual, fine-grained localization of text-referred regions in large-scale RS scenes. Unlike traditional approaches, XeMap enables precise mapping of mid-scale semantic entities that are often overlooked in image-level or object-level methods. To achieve this, we introduce XeMap-Network, a novel architecture designed to handle the complexities of pixel-level cross-modal contextual referring mapping in RS. The network includes a fusion layer that applies self- and cross-attention mechanisms to enhance the interaction between text and image embeddings. Furthermore, we propose a Hierarchical Multi-Scale Semantic Alignment (HMSA) module that aligns multiscale visual features with the text semantic vector, enabling precise multimodal matching across large-scale RS imagery. To support XeMap task, we provide a novel, annotated dataset, XeMap-set, specifically tailored for this task, overcoming the lack of XeMap datasets in RS imagery. XeMap-Network is evaluated in a zero-shot setting against state-of-the-art methods, demonstrating superior performance. This highlights its effectiveness in accurately mapping referring regions and providing valuable insights for interpreting large-scale RS environments.

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