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Change Detection (CD) has been attracting extensive interests with theavailability of bi-temporal datasets. However, due to the huge cost ofmulti-temporal images acquisition and labeling, existing change detectiondatasets are small in quantity, short in temporal, and low in practicability.Therefore, a large-scale practical-oriented dataset covering wide temporalphases is urgently needed to facilitate the community. To this end, theChangeNet 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 ofcomplex scenes from 100 cities, and 6 pixel-level annotated categories, whichis far superior to all the existing change detection datasets includingLEVIR-CD, WHU Building CD, etc.. In addition, ChangeNet contains amounts ofreal-world perspective distortions in different temporal phases on the sameareas, which is able to promote the practical application of change detectionalgorithms. The ChangeNet dataset is suitable for both binary change detection(BCD) and semantic change detection (SCD) tasks. Accordingly, we benchmark theChangeNet dataset on six BCD methods and two SCD methods, and extensiveexperiments demonstrate its challenges and great significance. The dataset isavailable at https://github.com/jankyee/ChangeNet.

ChangeNet: Multi-Temporal Asymmetric Change Detection Dataset