Landslide Geohazard Assessment with Convolutional Neural Networks Using Sentinel-2 Imagery Data
| Autor: | Alessandro Sebastianelli, Stefania Sica, F. P iccirillo, M. S. Langenkamp, Tuomas P. Oikarinen, Silvia Liberata Ullo, M. P. DelRosso |
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| Rok vydání: | 2019 |
| Předmět: |
010504 meteorology & atmospheric sciences
business.industry Computer science Deep learning Landslide Image processing 02 engineering and technology computer.software_genre 01 natural sciences Convolutional neural network 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Satellite imagery Artificial intelligence Data mining Geohazard business Baseline (configuration management) computer Risk management 0105 earth and related environmental sciences |
| Zdroj: | IGARSS |
| DOI: | 10.1109/igarss.2019.8898632 |
| Popis: | In this paper, the authors aim to combine the latest state of the art models in image recognition with the best publicly available satellite images to create a system for landslide risk mitigation. We focus first on landslide detection and further propose a similar system to be used for prediction. Such models are valuable as they could easily be scaled up to provide data for hazard evaluation, as satellite imagery becomes increasingly available. The goal is to use satellite images and correlated data to enrich the public repository of data and guide disaster relief efforts for locating precise areas where landslides have occurred. Different image augmentation methods are used to increase diversity in the chosen dataset and create more robust classification. The resulting outputs are then fed into variants of 3-D convolutional neural networks. A review of the current literature indicates there is no research using CNNs (Convolutional Neural Networks) and freely available satellite imagery for classifying landslide risk. The model has shown to be ultimately able to achieve a significantly better than baseline accuracy. |
| Databáze: | OpenAIRE |
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