Building damage annotation on post-hurricane satellite imagery based on convolutional neural networks
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Building damage annotation on post‑hurricane satellite imagery based on convolutional neural networks Quoc Dung Cao1 · Youngjun Choe1 Received: 15 April 2019 / Accepted: 20 June 2020 © Springer Nature B.V. 2020
Abstract After a hurricane, damage assessment is critical to emergency managers for efficient response and resource allocation. One way to gauge the damage extent is to quantify the number of flooded/damaged buildings, which is traditionally done by ground survey. This process can be labor-intensive and time-consuming. In this paper, we propose to improve the efficiency of building damage assessment by applying image classification algorithms to post-hurricane satellite imagery. At the known building coordinates (available from public data), we extract square-sized images from the satellite imagery to create training, validation, and test datasets. Each square-sized image contains a building to be classified as either ‘Flooded/Damaged’ (labeled by volunteers in a crowd-sourcing project) or ‘Undamaged’. We design and train a convolutional neural network from scratch and compare it with an existing neural network used widely for common object classification. We demonstrate the promise of our damage annotation model (over 97% accuracy) in the case study of building damage assessment in the Greater Houston area affected by 2017 Hurricane Harvey. Keywords Image classification · Neural network · Damage assessment · Building · Remote sensing
1 Introduction When a hurricane makes landfall, situational awareness is one of the most critical needs that emergency managers face before they can respond to the event. To assess the situation and damage, the current practice largely relies on emergency response crews and volunteers to drive around the affected area, which is also known as windshield survey. Another way to assess hurricane damage level is flood detection through synthetic aperture radar (SAR) images [e.g., see the work at the Darthmouth Flood Observatory (DFO 2020)), or * Youngjun Choe [email protected] Quoc Dung Cao [email protected] 1
Department of Industrial and Systems Engineering, University of Washington, 3900 E Stevens Way NE, Seattle, WA 98195, USA
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the damage proxy map to identify regional-level damages on the built environment (e.g., the Advanced Rapid Imaging and Analysis (ARIA) Project by Caltech and NASA (ARIA 2020)]. SAR imagery is useful in terms of mapping different surface features, texture, or roughness pattern but is harder for laymen to interpret than optical sensor imagery. The resolutions of virtually all SAR images of today are too coarse to permit the building-level (as opposed to regional-level) damage assessment. Also, satellites equipped with SAR sensors are far fewer than those with optical sensors, making timely and frequent data collection challenging. In this paper, we focus on using optical sensor imagery as a more intuitive and accessible way to analyze hurricane damage by distinguishing damaged buildings from the ones still
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