论文标题
HI-UCD：遥感图像中用于城市语义变化检测的大型数据集
Hi-UCD: A Large-scale Dataset for Urban Semantic Change Detection in Remote Sensing Imagery
论文作者
论文摘要
随着城市扩张的加速，城市变化检测（UCD）作为一种重要而有效的方法可以提供有关地理空间对象的变更信息进行动力学分析。但是，现有数据集遭受三个瓶颈的影响：（1）缺乏高空间分辨率图像； （2）缺乏语义注释； （3）缺乏远程多阶梯图像。在本文中，我们提出了一个大型基准数据集，称为HI-UCD。该数据集使用的空间图像具有由爱沙尼亚土地板提供的0.1 m的空间分辨率，包括三个期间阶段，并用九类土地覆盖物进行注释，以获得地面物体的方向变化。它可用于检测和分析精致的城市变化。我们在二进制和多类更改检测中使用一些经典方法对数据集进行了基准测试。实验结果表明，HI-UCD具有挑战性但有用。我们希望HI-UCD能够成为强大的基准加速未来的研究。
With the acceleration of the urban expansion, urban change detection (UCD), as a significant and effective approach, can provide the change information with respect to geospatial objects for dynamical urban analysis. However, existing datasets suffer from three bottlenecks: (1) lack of high spatial resolution images; (2) lack of semantic annotation; (3) lack of long-range multi-temporal images. In this paper, we propose a large scale benchmark dataset, termed Hi-UCD. This dataset uses aerial images with a spatial resolution of 0.1 m provided by the Estonia Land Board, including three-time phases, and semantically annotated with nine classes of land cover to obtain the direction of ground objects change. It can be used for detecting and analyzing refined urban changes. We benchmark our dataset using some classic methods in binary and multi-class change detection. Experimental results show that Hi-UCD is challenging yet useful. We hope the Hi-UCD can become a strong benchmark accelerating future research.
