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%.
Highlights
Post-disaster map updating is one of the essential tasks to support officials/governments to make decisions, policies, and plans for both the response phase to conduct emergency actions and the recovery phase to return to normalcy after the event—even to build back better as per the Sendai Framework [1]
We propose a framework for updating the building database after a disaster through an automthraotueIgndhtRahneissaUpuatnopemetra,CtewRdeFR,peursoUspinnoesgte-CoaRufFtrd,aumastieenwdgooOrukStdMfoartebudupOidlSdaMtiinnbgguditlhdaeitnabguadinladdtianmagnuddlamttia-ubtleatims-etepamoftpreaorrlaasladstaiestealllsiltiteteer images (Figure i1m).agTehse(Fpigruorpeo1s)e
We tested the proposed post-disaster building database updating framework on satellite images of Tacloban city, the Philippines, which was hit by super Typhoon Haiyan in November 2013, resulting in massive damages and losses (Figure 5)
Summary
Post-disaster map updating is one of the essential tasks to support officials/governments to make decisions, policies, and plans for both the response phase to conduct emergency actions and the recovery phase to return to normalcy after the event—even to build back better as per the Sendai Framework [1]. Updating the building database is vital to provide accurate information related to demolition, reconstruction, and building modification that is taking place during the response and recovery phases [2]. Building map updating requires new building data for detecting changes in the status of the buildings and the identification of newly built ones. Satellite remote sensing (RS) has become an essential and quick tool for acquiring suitable geospatial data given its synoptic coverage and the fact that it is readily available. The availability of free high-resolution images that are provided by platforms such as Google Earth has been attracting researchers in the remote sensing domain to focus on image-based building detection and mapping [3,4]
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
