Natural Disaster Application on Big Data and Machine Learning: A Review

Natural disasters are events that are difficult to avoid. There are several ways of reducing the risks of natural disasters. One of them is implementing disaster reduction programs. There are already several developed countries that apply the concept of disaster reduction. In addition to disaster reduction programs, there are several ways to predict or reducing the risks using artificial intelligence technology. One of them is big data, machine learning, and deep learning. By utilizing this method at the moment, it facilitates tasks in visualizing, analyzing, and predicting natural disaster. This research will focus on conducting a review process and understanding the purpose of machine learning and big data in the area of disaster management and natural disaster. The result of this paper is providing insight and the use of big data, machine learning, and deep learning in 6 disaster management area. This 6-disaster management area includes early warning damage, damage assessment, monitoring and detection, forecasting and predicting, and post-disaster coordination, and response, and long-term risk assessment and reduction

Detecting demolished buildings after a natural hazard using high resolution RGB satellite imagery and modified U-Net convolutional neural networks

Collapsed buildings are usually linked with the highest number of human casualties reported after a natural disaster; therefore, quickly finding collapsed buildings can expedite rescue operations and save human lives. Recently, many researchers and agencies have tried to integrate satellite imagery into rapid response. The U.S. Defense Innovation Unit Experimental (DIUx) and National Geospatial Intelligence Agency (NGA) have recently released a ready-to-use dataset known as xView that contains thousands of labeled VHR RGB satellite imagery scenes with 30-cm spatial and 8-bit radiometric resolutions, respectively. Two of the labeled classes represent demolished buildings with 1067 instances and intact buildings with more than 300,000 instances, and both classes are associated with building footprints. In this study, we are using the xView imagery, with building labels (demolished and intact) to create a deep learning framework for classifying buildings as demolished or intact after a natural hazard event. We have used a modified U-Net style fully convolutional neural network (CNN). The results show that the proposed framework has 78% and 95% sensitivity in detecting the demolished and intact buildings, respectively, within the xView dataset. We have also tested the transferability and performance of the trained network on an independent dataset from the 19 September 2017 M 7.1 Pueblo earthquake in central Mexico using Google Earth imagery. To this end, we tested the network on 97 buildings including 10 demolished ones by feeding imagery and building footprints into the trained algorithm. The sensitivity for intact and demolished buildings was 89% and 60%, respectively.Published versio

HYBRID SVM AND SVSA METHOD FOR CLASSIFICATION OF REMOTE SENSING IMAGES

A linear support vector machine (LSVM) is based on determining an optimum hyperplane that separates the data into two classes with the maximum margin. The LSVM typically has high classification accuracy for linearly separable data. However, for nonlinearly separable data, it usually has poor performance. For this type of data, the Support Vector Selection and Adaptation (SVSA) method was developed, but its classification accuracy is not very high for linearly separable data in comparison to LSVM. In this paper, we present a new classifier that combines the LSVM with the SVSA, to be called the Hybrid SVM and SVSA method (HSVSA), for classification of both linearly and nonlinearly separable data and remote sensing images as well. The experimental results show that the HSVSA has higher classification accuracy than the traditional LSVM, the nonlinear SVM (NSVM) with the radial basis kernel, and the previous SVSA