| 研究実績の概要 |
The research achievements can be summarized as follows:
1. We have made several damage responses with Group Earth Observation (GEO), such as 2020 Chinese Summer Flood, 2020 Beirut explosion, 2021 Indonesia Floods and landslides. In the disaster responses, we have applied the image preprocessing (i.e., noise filtering), transfer learning, Siamese convolutional neural networks (CNN), noisy label learning and post-processing to provide the change detection and building damage mapping.
2. We have constructed very high-resolution (1m) Gaofen-3 Synthetic Aperture Radar(SAR) datasets for building semantic segmentation. For the datasets, we compare the performance of difference baselines, and give the guidelines and roadmap for the future studeis. The datasets will be extend for the use of damage mapping.
3. We have constructed the multimodal (1 m high-resolution optical and SAR) datasets (more than 10 events) for damage mapping and proposed a general framework of learning from multimodal and multitemporal earth observation data for building damage mapping.
4. We have developed the ensemble of diverse Siamese CNN, such as the Unet with different encoders and attention mechanism, for building damage mapping.
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