Hi-UCD: A Large-scale Dataset for Urban Semantic Change Detection in Remote Sensing Imagery

Understand

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.