Abstract
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 this https URL.
Abstract (translated)
随着遥感影像（RSI）分辨率的提高，大尺寸的遥感影像已成为地理对象高精度矢量化映射的重要数据来源。现有的方法通常局限于处理小图像块，这往往会导致上下文信息的丢失，并产生碎片化的矢量输出结果。为了解决这些问题，本文介绍了HoliTracer，这是首个设计用于从大尺寸RSI中全面提取向量化地理对象的框架。 在HoliTracer中，我们通过使用包含局部到全局注意力机制以捕捉上下文依赖关系的Context Attention Net（CAN），增强了对大尺寸遥感影像的分割。此外，我们利用了一个稳健的工作流程来实现整体矢量化，该工作流程采用Mask Contour Reformer（MCR）重构多边形，并通过Polygon Sequence Tracer（PST）追踪顶点。 在包括建筑物、水域和道路的大尺寸RSI数据集上进行的广泛实验表明，HoliTracer优于当前最先进的方法。我们的代码和数据可以在[此处](https://这个URL)获取。
URL
https://arxiv.org/abs/2507.16251
