ChangeNet: Multi-Temporal Asymmetric Change Detection Dataset
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
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2023
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| _version_ | 1866929311831818240 |
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| author | Ji, Deyi Gao, Siqi Tao, Mingyuan Lu, Hongtao Zhao, Feng |
| author_facet | Ji, Deyi Gao, Siqi Tao, Mingyuan Lu, Hongtao Zhao, Feng |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2312_17428 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | ChangeNet: Multi-Temporal Asymmetric Change Detection Dataset Ji, Deyi Gao, Siqi Tao, Mingyuan Lu, Hongtao Zhao, Feng Computer Vision and Pattern Recognition 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. |
| title | ChangeNet: Multi-Temporal Asymmetric Change Detection Dataset |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2312.17428 |