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ارزیابی آسیب سازه های عمرانی پس از بلایای طبیعی با استفاده از آموزش عمیق و تصاویر ماهواره ای
Damage Assessment of Civil Structures after Natural Disasters Using Deep Learning and Satellite Imagery
Since 1980, millions of people have been harmed by natural disasters that have cost communities across the world over three trillion dollars. After a natural disaster has occurred, the creation of maps that identify the damage to buildings and infrastructure is imperative. Currently, many organizations perform this task manually, using pre- and post-disaster images and well-trained professionals to determine the degree and extent of damage. This manual task can take days to complete. I propose to do this task automatically using post-disaster satellite imagery. I use a pre-trained neural network, SegNet, and replaced its last layer with a simple damage classification scheme. This final layer of the network is re-trained using cropped segments of the satellite image of the disaster.
The data were obtained from a publicly accessible source, the Copernicus EMS system. They provided three channel (RGB) reference and damage grading maps that were used to create the images of the ground truth and the damaged terrain. I then retrained the final layer of the network to identify civil structures that had been damaged. The resulting network was 85% accurate at labelling the pixels in an image of the disaster from typhoon Haiyan. The test results show that it is possible to create these maps quickly and efficiently.
یادگیری عمیق برای ارزیابی خسارت پس از بلایای طبیعی
Deep Learning for Damage Assessment after Natural Disasters
In recent decades, due to global warming and climate change, we have witnessed ever-growing occurrences of natural disasters such as flooding, tornadoes, earthquakes, and wildfires. As such, it is more important than ever to provide emergency response personnel with accurate and timely information for their effective responses to crises. Among the variety of information which is needed for emergency responses and managements, it is vital that response personnel get informed in a timely manner as to where and how severely a building is damaged so that rescue efforts can be the most effective. However, challenges remain despite a great deal of effort in the field of image classification for disaster response. In this work, a promising approach based on deep learning is proposed to detect damaged buildings on high-resolution satellite imagery. By utilizing generic data augmentation, the proposed approach overcame the problem of limited training data popular in many remote sensing applications. A fine-tuning strategy is proposed to use transfer learning with a pretrained model for the task of interest. The experiments with imagery of Port-au-Prince, Haiti demonstrated the proposed method is effective when training data is limited. The Convolutional Neural Network (CNN) model with augmented training data can achieve 83% accuracy in detecting damaged buildings, greatly improved from 53% with the original training data. Future work would be focused on exploring automated methods to acquire larger training datasets and model generalization by investigating more robust data augmentation techniques.