Name
Abstract | ||
|---|---|---|
Analysis of satellite images plays an increasingly vital role in environment and climate monitoring, especially in detecting and managing natural disaster. In this paper, we proposed an automatic disaster detection system by implementing one of the advance deep learning techniques, convolutional neural network (CNN), to analysis satellite images. The neural network consists of 3 convolutional layers, followed by max-pooling layers after each convolutional layer, and 2 fully connected layers. We created our own disaster detection training data patches, which is currently focusing on 2 main disasters in Japan and Thailand: landslide and flood. Each disaster's training data set consists of 30000 similar to 40000 patches and all patches are trained automatically in CNN to extract region where disaster occurred instantaneously. The results reveal accuracy of 80%similar to 90% for both disaster detection. The results presented here may facilitate improvements in detecting natural disaster efficiently by establishing automatic disaster detection system. |
Year | Venue | Keywords |
|---|---|---|
2016 | 2016 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM (IGARSS) | convolutional neural network, disaster detection, difference extraction, satellite images |
Field | DocType | ISSN |
Satellite,Computer science,Convolutional neural network,Remote sensing,Feature extraction,Natural disaster,Artificial intelligence,Landslide,Deep learning,Artificial neural network,Flood myth | Conference | 2153-6996 |
Citations | PageRank | References |
3 | 0.39 | 2 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Siti Nor Khuzaimah Binti Amit | 1 | 3 | 0.72 |
| Soma Shiraishi | 2 | 3 | 1.40 |
| Tetsuo Inoshita | 3 | 3 | 0.39 |
| Yoshimitsu Aoki | 4 | 80 | 23.65 |