2022 Fiscal Year Research-status Report
Post-disaster Recovery Monitoring based on Multi-Source Remote Sensing Imagery and Deep Learning
| Project/Area Number |
21K14261
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| Research Institution | The University of Tokyo |
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Principal Investigator |
郭 直霊 東京大学, 空間情報科学研究センター, 客員研究員 (40897716)
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| Project Period (FY) |
2021-04-01 – 2024-03-31
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| Keywords | Post-disaster Monitoring / Deep Learning / Remote Sensing / Computer Vision |
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| Outline of Annual Research Achievements |
First, multi-temporal post-disaster remote sensing imagery and the ground-truth of changing has been collected.
Then, with the help of Multi-task Urban Mapping, the land cover semantic segmentation, object detection, and DSM in multi-temporal can be generated.
After that, an end-to-end deep learning model for change detection has been trained by data fusing and ensemble learning. Meanwhile, to solve the slight misalignment of multi-temporal imagery as well as the imbalanced land cover ratio, a specific loss function will be proposed.
Finally, the trained change detection model is capable of detecting land cover changes, such as the destroyed, new vacant, under construction, completed, etc.
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| Current Status of Research Progress |
Current Status of Research Progress
2: Research has progressed on the whole more than it was originally planned.
Reason
Progressing rather smoothly.
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| Strategy for Future Research Activity |
Transfer Learning based Recovery Monitoring will be finished; in the next half year, some practical applications will be conducted.
For a large-scale and long-lasting post-disaster recovery monitoring, problems caused by multi-source remote sensing imagery such as the difference in land features, resolution, and spectrum, will be solved by transfer learning methods. Thus, a small amount of training datasets will be obtained for model transfer. After that, multi-temporal UAV aerial imagery will be put into the transferred multi-task urban mapping model for multi-temporal land feature extraction. Finally, the generated land features including semantic, object, and DEM will be utilized to achieve high-performance recovery monitoring via the transferred change detection model. In this year, the results of the aforementioned steps will be integrated, and the experimental post-disaster recovery monitoring will be deployed in cooperation with communication and companies. With the analysis of the experimental results, the rest of the time in FY2023 will be devoted to the conclusions of the theories, methods, and findings derived from this research. Additionally, the monitoring methods will be open for the society with practical applications, publications, and web service, which will lead to the future evolvement and application of post-disaster recovery monitoring in broader usages.
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| Causes of Carryover |
Paper submission fee.
Item fee.
Personnel fee.
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