首次实现大尺度遥感影像整体矢量化，提升地理对象提取精度
HoliTracer: Holistic Vectorization of Geographic Objects from Large-Size Remote Sensing Imagery
- 采用局部到全局注意力机制捕捉大图上下文关系
- 在建筑物、水体等多类目标上显著优于现有方法
- 适合需要高精度矢量地图的遥感应用开发者
随着遥感影像（RSI）分辨率提升，大尺度遥感影像已成为高精度地理对象矢量化的重要数据源。现有方法多局限于小图像块处理，常导致上下文信息丢失和矢量结果碎片化。为此，本文提出HoliTracer，首个面向大尺度遥感影像的整体矢量化框架。通过引入上下文注意力网络（CAN），利用局部到全局注意力机制捕捉长距离依赖关系，增强大图分割效果；并构建鲁棒的矢量化流程，结合掩码轮廓重构器（MCR）重建多边形，以及多边形顶点追踪器（PST）精确追踪顶点。在包含建筑物、水体和道路的大尺度遥感影像数据集上进行的大量实验表明，HoliTracer显著优于当前最优方法。代码与数据已开源：https://github.com/vvangfaye/HoliTracer。
原文摘要 · Abstract (English)
With the increasing resolution of remote sensing imagery (RSI), large-size RSI has emerged as a vital data source for high-precision vector mapping of geographic objects. Existing methods are typically constrained to processing small image patches, which often leads to the loss of contextual information and produces fragmented vector outputs. To address these, this paper introduces HoliTracer, the first framework designed to holistically extract vectorized geographic objects from large-size RSI. In HoliTracer, we enhance segmentation of large-size RSI using the Context Attention Net (CAN), which employs a local-to-global attention mechanism to capture contextual dependencies. Furthermore, we achieve holistic vectorization through a robust pipeline that leverages the Mask Contour Reformer (MCR) to reconstruct polygons and the Polygon Sequence Tracer (PST) to trace vertices. Extensive experiments on large-size RSI datasets, including buildings, water bodies, and roads, demonstrate that HoliTracer outperforms state-of-the-art methods. Our code and data are available in https://github.com/vvangfaye/HoliTracer.
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