  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.
