Automatic 3D Building Footprint Extraction from High Resolution Satellite Image using OSM
A. Shyamalaprasanna1, V.Vakula2, S. Vidhya3, S. Hema vikasini4
1Ms.A.Shyamalaprasanna., M.E, Faculty of Electronics and Communication Engineering, Bannari Amman Institute of Technology, Sathiyamangalam, Erode.
2Ms.V.Vakula.M.E, Research Scholar of Computer Science and Engineering, Bannari Amman Institute of Technology, Sathiyamangalam, Erode.
3Ms. S. Vidhya, PG Scholar of Electronics and Communication Engineering, Bannari Amman Institute of Technology, Sathiyamangalam, Erode.
4Ms. S. Hema vikasini, PG Scholar of Electronics and Communication Engineering, Bannari Amman Institute of Technology, Sathiyamangalam, Erode.
Manuscript received on September 16, 2019. | Revised Manuscript received on 24 September, 2019. | Manuscript published on October 10, 2019. | PP: 1642-1646 | Volume-8 Issue-12, October 2019. | Retrieval Number: L31611081219/2019©BEIESP | DOI: 10.35940/ijitee.L3161.1081219
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Building impression data is fundamental for 3D building demonstrating. Customarily, in remote detecting, building impressions are removed and outlined from ethereal symbolism and LiDAR point cloud. Adopting an alternate strategy, this paper is devoted to the advancement of OpenStreetMap (OSM) building impressions misusing the shape data, which is gotten from profound learning-based semantic division of angled pictures procured by the Unmanned Aerial Vehicle (UAV). Initial, an improved 3D building model of Level of Detail 1 (LoD 1) is instated utilizing the impression data from OSM and the height data from Digital Surface Model (DSM). In parallel, a profound neural system for pixel-wise semantic picture division is prepared so as to extricate the structure limits as shape proof. Thusly, a streamlining incorporating the shape proof from multi-see pictures as a requirement brings about a refined 3D building model with improved impressions and stature. This technique is utilized to advance OSM building impressions for four datasets with various structure types, exhibiting hearty execution for both individual structures and different structures paying little respect to picture goals. At long last, the contrast the outcome and reference information from German Authority Topographic-Cartographic Information System (ATKIS). Quantitative and subjective assessments uncover that the first OSM building impressions have enormous balanced, yet can be fundamentally improved from meter level to decimeter level after enhancement.
Keywords: Building extraction, OpenStreetMap, Very High Spatial Resolution, urban area
Scope of the Article: Building Energy