Change Detection (CD) has been attracting extensive interests with the availability of bi-temporal datasets. However, due to the huge cost of multi-temporal images acquisition and labeling, existing change detection datasets are small in quantity, short in temporal, and low in practicability. Therefore, a large-scale practical-oriented dataset covering wide temporal phases is urgently needed to facilitate the community. To this end, the ChangeNet dataset is presented especially for multi-temporal change detection, along with the new task of ``Asymmetric Change Detection". Specifically, ChangeNet consists of 31,000 multi-temporal images pairs, a wide range of complex scenes from 100 cities, and 6 pixel-level annotated categories, which is far superior to all the existing change detection datasets including LEVIR-CD, WHU Building CD, etc.. In addition, ChangeNet contains amounts of real-world perspective distortions in different temporal phases on the same areas, which is able to promote the practical application of change detection algorithms. The ChangeNet dataset is suitable for both binary change detection (BCD) and semantic change detection (SCD) tasks. Accordingly, we benchmark the ChangeNet dataset on six BCD methods and two SCD methods, and extensive experiments demonstrate its challenges and great significance. The dataset is available at https://github.com/jankyee/ChangeNet.

由于多时相图像数据获取与标注成本巨大，导致现有变化检测数据集在数量、时间范围和实际可行性方面存在不足。因此，迫切需要一个大规模的实用数据集来促进该领域的发展。为此，提出了ChangeNet数据集，用于多时相变化检测，并引入了“非对称变化检测”的新任务。ChangeNet包含31000对多时相图像，来自100个城市的各种复杂场景，并且包含了6个像素级注释的类别，远远超过了所有已有的变化检测数据集，包括LEVIR-CD、WHU Building CD等。此外，ChangeNet包含了同一区域在不同时间相的大量真实视角扭曲，能够促进变化检测算法的实际应用。ChangeNet适用于二值化变化检测（BCD）和语义变化检测（SCD）任务，我们对ChangeNet数据集进行了六个BCD方法和两个SCD方法的基准测试，广泛的实验证明了其挑战性和重要性。该数据集可在此 https URL 获取。

ChangeNet：多时相非对称变化检测数据集