Post-Disaster Building Database Updating Using Automated Deep Learning: An Integration of Pre-Disaster OpenStreetMap and Multi-Temporal Satellite Data

Abstract

First responders and recovery planners need accurate and quickly derived information about the status of buildings as well as newly built ones to both help victims and to make decisions for reconstruction processes after a disaster. Deep learning and, in particular, convolutional neural network (CNN)-based approaches have recently become state-of-the-art methods to extract information from remote sensing images, in particular for image-based structural damage assessment. However, they are predominantly based on manually extracted training samples. In the present study, we use pre-disaster OpenStreetMap building data to automatically generate training samples to train the proposed deep learning approach after the co-registration of the map and the satellite images. The proposed deep learning framework is based on the U-net design with residual connections, which has been shown to be an effective method to increase the efficiency of CNN-based models. The ResUnet is followed by a Conditional Random Field (CRF) implementation to further refine the results. Experimental analysis was carried out on selected very high resolution (VHR) satellite images representing various scenarios after the 2013 Super Typhoon Haiyan in both the damage and the recovery phases in Tacloban, the Philippines. The results show the robustness of the proposed ResUnet-CRF framework in updating the building map after a disaster for both damage and recovery situations by producing an overall F1-score of 84.2%.

Evaluating Resilience-Centered Development Interventions with Remote Sensing

“…An analysis of the damage map created during a Humanitarian OpenStreetMap campaign showed an overestimation of destroyed buildings in Tacloban by some 92% [74], and also the damage and resulting recovery maps in [20,75] show this overestimation to some extent. It is likely that some of the barangays that in this study show building reconstruction rates of >1000% within about 3 years also suffered less actual building damage.…”

“…A single high resolution multispectral Pléiades images for this area would cost in excess of 5000 US$, while an 8-band WorldView-3 image would cost about 7500 US$, and a simple recovery assessment requires three time steps as a minimum (T0, T1, and T2). For the image analysis part of this work we made use of image data costing approximately 45,000 US$, part of which was donated by the Digital Globe An analysis of the damage map created during a Humanitarian OpenStreetMap campaign showed an overestimation of destroyed buildings in Tacloban by some 92% [74], and also the damage and resulting recovery maps in [20,75] show this overestimation to some extent. It is likely that some of the barangays that in this study show building reconstruction rates of >1000% within about 3 years also suffered less actual building damage.…”

“…For the image analysis part of this work we made use of image data costing approximately 45,000 US$, part of which was donated by the Digital Globe Foundation. In particular immediate post-event images are also often acquired at large off-nadir angles, resulting in additional challenges for detailed recovery assessment [75]. The recent recovery study that focused on Tacloban [20] showed that a detailed LU classification that is needed for comprehensive urban recovery assessment requires very high spatial resolution imagery.…”

“…Vast amounts of detailed image data are necessary, resulting also in high processing cost. However, the efficiency and sophistication of damage-and recovery-focused processing has also been increasing, in particular through advances in deep learning/CNN [75,78], where a trend towards automatically handling large datasets as training samples is evident. However, even when sophisticated image processing methods were used in our study also showed that remote sensing-based recovery assessment, which requires LCLU-based change analysis over multi-temporal data, significantly depended on the accuracy of the image classification results.…”

A Novel Change Detection Method for Natural Disaster Detection and Segmentation from Video Sequence

“…After that, MOEA is applied to obtain a set of multiple binary change masks by iteratively minimizing two objective functions for changed and unchanged regions and the final binary mask is optimally fused by MRF. With the purpose of improving efficiency, in Ghaffarian et al [ 27 ], extended U-net based on deep residual (ResUnet) followed a Conditional Random Field (CRF) implementation was proposed to update the post-disaster buildings from very high resolution imagery. Alizadeh et al [ 12 ] established a new hybrid framework of Analytic Network Process (ANP) and Artificial Neural Network (ANN) models for earthquake vulnerability assessment.…”