Authors: Kyle M Monahan*, Tufts Data Lab, TTS, Tufts University, Medford MA 02144, Yuehui (Aurora) Li, Urban and Environmental Policy and Planning, Tufts University, Medford MA 02144
Topics: Remote Sensing, Spatial Analysis & Modeling, Drones
Keywords: deep learning, Hurricane Maria, Puerto Rico
Session Type: Virtual Poster
Start / End Time: 3:05 PM / 4:20 PM
Room: Virtual 52
Presentation File: No File Uploaded
Hurricane Maria made landfall in southeast Puerto Rico on September 20th, 2017. This powerful storm affected millions of people, causing a partial collapse of the power and healthcare systems, and placed much of the affected population at risk for adverse mental and physical health outcomes. In this work, a remotely sensed indicator of damage to infrastructure was developed for Puerto Rico using remotely sensed blue-colored tarps placed over damaged roofs using a machine learning technique. A random forest and deep learning model were created using ArcGIS Pro, training on both drone imagery (1 meter resolution) and Sentinel-2 imagery (15 meter resolution). Results of models were compared using accuracy, F1, and sensitivity values for each class. Future work using change detection should be performed.