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.

翻译：变化检测（Change Detection, CD）随着双时序数据集的出现而引起了广泛关注。然而，由于多时序图像采集和标注的巨大成本，现有变化检测数据集存在数量少、时序短、实用性低等问题。因此，亟需一个覆盖广泛时相的大规模实用导向数据集来推动该领域发展。为此，本文提出了专用于多时序变化检测的ChangeNet数据集，并引入"非对称变化检测"（Asymmetric Change Detection）这一新任务。具体而言，ChangeNet包含31,000对多时序图像、来自100个城市的多样化复杂场景以及6个像素级标注类别，其规模远超LEVIR-CD、WHU Building CD等现有变化检测数据集。此外，ChangeNet中同一区域不同时序图像包含大量真实场景视角畸变，有助于促进变化检测算法的实际应用。该数据集适用于二值变化检测（Binary Change Detection, BCD）和语义变化检测（Semantic Change Detection, SCD）两种任务。基于此，我们在六种BCD方法和两种SCD方法上对ChangeNet数据集进行了基准测试，大量实验证明了其挑战性与重要价值。数据集获取地址：https://github.com/jankyee/ChangeNet。