Vol.10 (2020) No. 1 ISSN: 2088-5334 Estimating Damaged Volume of Historic Pagodas in Bagan after Earthquake using 3D Hough Transform Thida Aung # , Myint Myint Sein * # Faculty of Computer Science, University of Computer Studies, Yangon, No. (4) Road, Shwe Pyi Thar, Yangon, 11411, Myanmar E-mail: tdathida@ucsy.edu.com *Geographic Information System Lab, University of Computer Studies, Yangon, No. (4) Road, Shwe Pyi Thar, Yangon, 11411, Myanmar E-mail: myint@ucsy.edu.mm Abstract— On 24 th August 2016, the magnitude of a 6.8 earthquake struck in Bagan from the depth of 52 miles. This earthquake caused much damage in historic pagodas in Bagan, one of the archeological houses in Asia. Analyzing the affected areas is an essential task for the restoration and reconstruction of historic buildings after a disaster. Traditional methods of detecting damage to buildings focus on detecting 2D changes (i.e., only the appearance of the image), but the 2D information provided by the image is not sufficient when it involves detecting damage to buildings is often not precise. For finding out the solution, a method of 3D change detection is needed for estimating the volumes of damaged pagodas after the earthquake. The proposed system aims at producing a quick assessment of the damaged pagodas accurately and correctly. This system estimates the damaged volume of the pagoda based on the nature of the 3D point clouds. Post-earthquake photos are taken using an anonymous aircraft (UAV) and point cloud data is generated using VisualSFM software. The 3D Hough transform is used to find the intersection of the tower vertex and the 3D vertex at the line boundary. Besides, the proposed system can detect the reformed structure of the entire pagoda. The results show that the proposed approach facilitates the automated 3D detection of damaged pagodas and is a time-saving method for estimating the volume of damage caused to precious historic pagodas after a disaster. Keywords— 2D/3D change detection; point clouds; Unnamed Aerial Vehicle (UAV); VisualSFM; 3D hough transform. I. INTRODUCTION Cultural heritage is not only a valuable monument for the generations but also an important thing to renovate the glory of the past of the country. It is the essential thing that reveals the many aspects of religion, traditions, and beliefs. Cultural heritage is one category of the heritages that refers to cultural aspects like monuments and historic sites. Detailed damage assessment for cultural heritage after the calamity has gotten a necessary assignment in compelling crisis reaction and recuperation. In crisis reaction and recuperation, a manual appraisal is costly and tedious. Sometimes the urgent response is impossible in response. Remote sensing and geographic information system (GIS) is a valuable elective route for the harm evaluation process. We can get the satellite and aerial images effectively (UAV images) after the disaster occurred. We can get a critical harm evaluation utilizing the brushing the GIS advances and computerized picture handling. These days, in digital image processing, numerous procedures are utilized to identify harms brought about by natural disasters such as landslide, earthquake, and fire and flood. There are commonly two sorts of identifying the harms; object-based harmed identification and locale-based harmed discovery. Article based harmed recognition accentuates the shape, textures, background information and spectral information of the particular picture. Traditional change discovery techniques were primarily created on radiometric data investigation of multi-worldly remote detected unearthly or optical pictures. Numerous applications, for example, checking land use/land cover classes, calamity evaluation, are created by utilizing change discovery methods. Change recognition results utilizing just 2D image's data are frequently affected by a lot of bogus alerts; for the most part, brought about via occasional varieties, diverse climate conditions, shadows, and impediments. Conventional structure harm change identification is primarily centered around 2D change systems, mainly dependent on the presence of the image. In 2D change, the method can figure the changed and unaltered territory of the structure; however, it is inadequate and exacts the entire structure change identification. So the change of the structure harm is wanted dependent on the 3D 90