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Disclaimer

This repository is modified from the code for the the DIUx xView Detection Challenge. The paper is available here.

This repository is created for Automatic Damage Annotation on Post-Hurricane Satellite Imagery, one of three projects from the 2018 Data Science for Social Good summer fellowship at the University of Washington eScience Institute.

Introduction

Two object detection algorithms, Single Shot Multibox Detector and Faster R-CNN were applied to satellite imagery for hurricane Harvey provided by DigitalGlobe Open Data Program and crowd-sourced damaged buildings labels provided by Tomnod. Our team built dataset for damaged building object detection by placing bounding boxes for damaged buildings whose locations are labelled by Tomnod. For more information about dataset creation, please visit our website.

We usedtensorflow object detection API to run SSD and Faster R-CNN. We used a baseline model provided by xView Challenge as pre-trained model for SSD. This repository contains code performs data processing training, specifically, 1) image chipping, 2) bounding box visualization, 3) train-test split, 4) data augmentation, 5) convert imagery and labels into TFrecord, 6) inference, 7) scoring, 8) cloud removal, 9) black region removal, etc. See here. We fed the data into SSD and Faster R-CNN and predicted the bounding boxes for damaged and non-damaged buildings.

Reference:

• Object Detection Baselines in Overhead Imagery with DIUx xView: https://medium.com/@dariusl/object-detection-baselines-in-overhead-imagery-with-diux-xview-c39b1852f24f