Fine urban change segmentation using multi-temporal remote sensing images is
essential for understanding human-environment interactions. Despite advances in
remote sensing data for urban monitoring, coarse-grained classification systems
and the lack of continuous temporal observations hinder the application of deep
learning to urban change analysis. To address this, we introduce FUSU, a
multi-source, multi-temporal change segmentation dataset for fine-grained urban
semantic understanding. FUSU features the most detailed land use classification
system to date, with 17 classes and 30 billion pixels of annotations. It
includes bi-temporal high-resolution satellite images with 20-50 cm ground
sample distance and monthly optical and radar satellite time series, covering
847 km2 across five urban areas in China. The fine-grained pixel-wise
annotations and high spatial-temporal resolution data provide a robust
foundation for deep learning models to understand urbanization and land use
changes. To fully leverage FUSU, we propose a unified time-series architecture
for both change detection and segmentation and benchmark FUSU on various
methods for several tasks. Dataset and code will be available at:
this https URL

利用多时相遥感图像进行精细城市变化分割对于理解人地互动非常重要。我们介绍了 FUSU，这是一个多源、多时序变化分割数据集，用于精细级城市语义理解。FUSU 拥有迄今为止最详细的土地利用分类系统，具有 17 类和 300 亿像素的注释。通过提供像素级细化注释和高空间 - 时间分辨率数据，FUSU 为深度学习模型理解城市化和土地利用变化提供了坚实的基础。我们提出了一个统一的时序架构来检测和分割变化，并在多项任务上对 FUSU 进行了基准测试。数据集和代码将在此 URL 上提供。