Recent studies have shown high resolution satellite imagery to be a powerful data source for post-earthquake damage assessment of buildings. Manual interpretation of these images, while being a reliable method for finding damaged buildings, is a subjective and time-consuming endeavor, rendering it unviable at times of emergency. The present research, proposes a new state-of-the-art method for automatic damage assessment of buildings using high resolution satellite imagery. In this method, at the first step a set of pre-processing algorithms are performed on the images. Then, extracting a candidate building from both pre- and post-event images, the intact roof part after an earthquake is found. Afterwards, by considering the shape and other structural properties of this roof part with its pre-event condition in a fuzzy inference system, the rate of damage for each candidate building is estimated. The results obtained from evaluation of this algorithm using QuickBird images of the December 2003 Bam, Iran, earthquake prove the ability of this method for post-earthquake damage assessment of buildings

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The world has experienced urban changes rapidly, and this phenomenon encourages authors to contribute to the United Nations sustainable development goals (SDGs) 2030 and geospatial information. This study presents a proposed algorithm of change detection and extracting the borders of buildings. This proposed algorithm provides a set of instructions to describe the method of solving the problem of how extracting the boundary of buildings from the light detection and ranging (LiDAR) input data incorporating with the firefly and ant colony algorithms. The method has used two different epochs to compare buildings and to identify the type of changes in selected buildings. These changes are based on the newly built or demolished buildings. We also used drone images and mask the region-based convolutional neural network (R-CNN) method to compare the results of roof extraction of buildings vs. the proposed algorithm. This study shows that the proposed algorithm identifies the changes of all buildings with higher accuracy of extracting border of buildings than the existing methods, successfully. This study also determines that the amount of root mean square error (RMSE) is 2.40 m2 when we use LiDAR. This proposed algorithm contributes to identifying rapidly changed buildings, and it is helpful for global geospatial information of urban management that can add best practice and solution toward the UN SDGs connectivity dilemma of urban settlement, resilience, and sustainability.

Abstract. Roads network are the most important parts of urban infrastructures, which can cause difficulty to the city whenever they undergo a problem. This paper aims to provide and implement a deep learning-based method to determine the status of the streets network after an earthquake using LiDAR point cloud. The proposed framework composes of three main phases: (1) Deep features of LiDAR data are extracted using a Convolutional Neural Network (CNN). (2) The extracted features are used in a multilayer perceptron (MLP) neural network in which debris areas inside the road network are detected. (3) The amount of debris in each road is applied to damage index for classifying the road segments into blocked or un-blocked. To evaluate the efficiency of the proposed framework, LiDAR point cloud of the Port-au-Prince, Haiti after the 2010 Haiti earthquake was used. The overall accuracy of more than 97% proved the high performance of this framework for debris detection. Moreover, analyzing damage assessment of 37 road segments based on the detected debris and comparing to a visually generated damaged map, 31 of the road segments were correctly labelled as either blocked or un-blocked.