A Deep Learning Application for Building Damage Assessment Using Ultra-High-Resolution Remote Sensing Imagery in Turkey Earthquake

Rapid building damage assessment following an earthquake is important for humanitarian relief and disaster emergency responses.In February 2023,two magnitude-7.8 earthquakes struck Turkey in quick succession,impacting over 30 major cities across nearly 300 km.A quick and comprehensive understanding of the distribution of building damage is essential for e fficiently deploying rescue forces during critical rescue periods.This article presents the training of a two-stage convolutional neural network called BDANet that integrated image features captured before and after the disaster to evaluate the extent of building damage in Islahiye.Based on high-resolution remote sensing data from WorldView2,BDANet used predisaster imagery to extract building outlines;the image features before and after the disaster were then combined to conduct building damage assessment.We optimized these results to improve the accuracy of building edges and analyzed the damage to each building,and used population distribution information to estimate the population count and urgency of rescue at different disaster levels.The results indicate that the building area in the Islahiye region was 156.92 ha,with an affected area of 26.60 ha.Severely damaged buildings accounted for 15.67%of the total building area in the affected areas.WorldPop population distribution data indicated approximately 253,297,and 1,246 people in the collapsed,severely damaged,and lightly damaged areas,respectively.Accuracy verification showed that the BDANet model exhibited good performance in handling high-resolution images and can be used to directly assess building damage and provide rapid information for rescue operations in future disasters using model weights.

A Low-Cost Mobile Mapping System (LCMMS) for field data acquisition: a potential use to validate aerial/satellite building damage assessment

Among the major natural disasters that occurred in 2010,the Haiti earthquake was a real turning point concerning the availability,dissemination and licensing of a huge quantity of geospatial data.In a few days several map products based on the analysis of remotely sensed data-sets were delivered to users.This demonstrated the need for reliable methods to validate the increasing variety of open source data and remote sensing-derived products for crisis management,with the aim to correctly spatially reference and interconnect these data with other global digital archives.As far as building damage assessment is concerned,the need for accurate field data to overcome the limitations of both vertical and oblique view satellite and aerial images was evident.To cope with the aforementioned need,a newly developed Low-Cost Mobile Mapping System(LCMMS)was deployed in Port-au-Prince(Haiti)and tested during a five-day survey in FebruaryMarch 2010.The system allows for acquisition of movies and single georeferenced frames by means of a transportable device easily installable(or adaptable)to every type of vehicle.It is composed of four webcams with a total field of view of about 180 degrees and one Global Positioning System(GPS)receiver,with the main aim to rapidly cover large areas for effective usage in emergency situations.The main technical features of the LCMMS,the operational use in the field(and related issues)and a potential approach to be adopted for the validation of satellite/aerial building damage assessments are thoroughly described in the article.