2021 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 / Change Detection / Deep Learning / Remote Sensing |
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| Outline of Annual Research Achievements |
First, multi-source remote sensing imagery with related ground truth, including semantic segmentation annotation, object bounding box, and DEM are collected.
Then, weak supervision based multi-task deep learning methods are formulated to train the urban mapping model, which can conduct semantic segmentation, object detection, and DEM generalization simultaneously even with the inexact and inadequate training dataset.
Meanwhile, a learnable weighting method for multi-loss functions combination is proposed to achieve automatic fine-tuning.
The multi-task urban mapping will serve the following steps with a high-performance multi-task mapping model, and provide additional land cover mapping results as well.
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| Current Status of Research Progress |
Current Status of Research Progress
3: Progress in research has been slightly delayed.
Reason
The quality of DEM dataset is not good enough.
The learnable weighting method for multi-loss functions is still under development.
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| Strategy for Future Research Activity |
Post-disaster Change Detection will be the finished in FY2022.
First, multi-temporal post-disaster remote sensing imagery and the ground-truth of changing will be collected. Then, with the help of (A) 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 will be 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 will be capable of detecting land cover changes, such as the destroyed, new vacant, under construction, completed, etc.
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| Causes of Carryover |
The research need to prepare UAV as well as GPU server; 1.Unmanned Aerial Vehicle (UAV)(DJI, Phantom 4 Pro V2.0); 2.NVIDIA GeForce RTX 3090 24GB GDDR6X.
Join the international conference for sharing research results.
Hire part-time students for the experiment.
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