2019/10/14 by Joseph Z. Xu, Xu, Joseph Z., Wenhan Lu +7 · 7 citations
Computer Science · Earth and Planetary Sciences · Engineering · Mathematics · #Anomaly Detection Techniques and Applications #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #FOS: Electrical engineering #Image and Video Processing (eess.IV) #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Remote Sensing and Land Use #Remote-Sensing Image Classification #cs.CV #cs.LG #eess.IV #electronic engineering #information engineering #stat.ML
paper · pdf · doi:10.48550/arxiv.1910.06444
arxiv created 2019/10/14 · openalex publication_date 2019/10/14 · arxiv updated 2019/10/16 · openalex created_date 2019/10/25 · openalex updated_date 2026/07/28
In all types of disasters, from earthquakes to armed conflicts, aid workers need accurate and timely data such as damage to buildings and population displacement to mount an effective response. Remote sensing provides this data at an unprecedented scale, but extracting operationalizable information from satellite images is slow and labor-intensive. In this work, we use machine learning to automate the detection of building damage in satellite imagery. We compare the performance of four different convolutional neural network models in detecting damaged buildings in the 2010 Haiti earthquake. We also quantify how well the models will generalize to future disasters by training and testing models on different disaster events.