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Article

Developing an Algorithm for Buildings Extraction and Determining Changes from Airborne LiDAR, and Comparing with R-CNN Method from Drone Images

1
Department of Surveying and Geoinformatics, Faculty of Geosciences and Environmental Engineering, Southwest Jiaotong University (SWJTU), the Western Park of the Hi-Tech Industrial Development Zone, Chengdu, Sichuan 611756, China
2
Mobile Sensing and Data Science Lab, University of Waterloo, 200 University Ave., Waterloo, ON N2L 3G1, Canada
3
School of Surveying and Geospatial Engineering, College of Engineering, University of Tehran, Tehran 14174-66191, Iran
*
Author to whom correspondence should be addressed.
Remote Sens. 2019, 11(11), 1272; https://doi.org/10.3390/rs11111272
Received: 30 April 2019 / Revised: 21 May 2019 / Accepted: 23 May 2019 / Published: 29 May 2019
(This article belongs to the Section Urban Remote Sensing)
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. View Full-Text
Keywords: LiDAR; change detection; border extraction; firefly algorithm; ant colony algorithm; drone images; R-CNN LiDAR; change detection; border extraction; firefly algorithm; ant colony algorithm; drone images; R-CNN
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MDPI and ACS Style

Pirasteh, S.; Rashidi, P.; Rastiveis, H.; Huang, S.; Zhu, Q.; Liu, G.; Li, Y.; Li, J.; Seydipour, E. Developing an Algorithm for Buildings Extraction and Determining Changes from Airborne LiDAR, and Comparing with R-CNN Method from Drone Images. Remote Sens. 2019, 11, 1272. https://doi.org/10.3390/rs11111272

AMA Style

Pirasteh S, Rashidi P, Rastiveis H, Huang S, Zhu Q, Liu G, Li Y, Li J, Seydipour E. Developing an Algorithm for Buildings Extraction and Determining Changes from Airborne LiDAR, and Comparing with R-CNN Method from Drone Images. Remote Sensing. 2019; 11(11):1272. https://doi.org/10.3390/rs11111272

Chicago/Turabian Style

Pirasteh, Saied; Rashidi, Pejman; Rastiveis, Heidar; Huang, Shengzhi; Zhu, Qing; Liu, Guoxiang; Li, Yun; Li, Jonathan; Seydipour, Erfan. 2019. "Developing an Algorithm for Buildings Extraction and Determining Changes from Airborne LiDAR, and Comparing with R-CNN Method from Drone Images" Remote Sens. 11, no. 11: 1272. https://doi.org/10.3390/rs11111272

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