Building Damage Detection in Satellite Imagery Using Convolutional Neural Networks
This work addresses the need for faster and more efficient damage assessment for aid workers in disaster response, though it appears incremental as it compares existing models on a specific dataset.
The researchers tackled the problem of automating building damage detection in satellite imagery by comparing four convolutional neural network models on data from the 2010 Haiti earthquake, achieving performance metrics that were evaluated for generalization to future disasters.
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.