| Project/Area Number |
19J13500
|
|---|
| Research Category |
Grant-in-Aid for JSPS Fellows
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| Allocation Type | Single-year Grants |
|---|
| Section | 国内 |
|---|
| Review Section |
Basic Section 22050:Civil engineering plan and transportation engineering-related
|
|---|
| Research Institution | The University of Tokyo |
|---|
Principal Investigator |
郭 直霊 東京大学, 新領域創成科学研究科, 特別研究員(DC2)
|
|---|
| Project Period (FY) |
2019-04-25 – 2021-03-31
|
| Project Status |
Discontinued (Fiscal Year 2020)
|
| Budget Amount *help |
¥1,700,000 (Direct Cost: ¥1,700,000)
Fiscal Year 2020: ¥800,000 (Direct Cost: ¥800,000)
Fiscal Year 2019: ¥900,000 (Direct Cost: ¥900,000)
|
|---|
| Keywords | deep learning / remote sensing / library establishment / Deep Learning / Super Resolution / Remote Sensing |
|---|
| Outline of Research at the Start |
The proposed research aims to achieve automatic and real-time generalization of catastrophe maps with high accuracy and efficiency. State-of-the-art Methodologies in deep learning such as CNN and GAN will be developed and utilized to detect important land features such as buildings and roads.
|
| Outline of Annual Research Achievements |
The main research topic “System for Automatic and Real-time Generalization of Catastrophe Maps based on Deep Learning Methods” was splitted into different subtopics. For instance: segmentation and super-resolution library establishment, real-time map segmentation application, high accuracy building semantic based on deep learning, the pedestrian trajectory prediction and surveillance, super-resolution integrated method for accuracy pattern recognition accuracy enhancement, change detection, etc.
|
| Research Progress Status |
令和2年度が最終年度であるため、記入しない。
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| Strategy for Future Research Activity |
令和2年度が最終年度であるため、記入しない。
|
