A MULTI-AGENT METHOD FOR SIMULTANEOUS BUILDING EXTRACTION AND SEGMENTATION FROM LIDAR POINT CLOUD

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SAJADIAN M.; AREFI H.

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Journal: JOURNAL OF GEOMATICS SCIENCE AND TECHNOLOGY Year:2014 | Volume:4 | Issue:2 Page(s): 53-65

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Title

A MULTI-AGENT METHOD FOR SIMULTANEOUS BUILDING EXTRACTION AND SEGMENTATION FROM LIDAR POINT CLOUD

Author(s)

SAJADIAN M. | AREFI H. | Issue Writer Certificate

Keywords

LIDARQ2

POINT CLOUDQ2

DELAUNAY TRIANGULATIONQ2

NORMAL VECTORQ2

SEGMENTATIONQ2

BUILDING EXTRACTIONQ2

BUILDING SEGMENTSQ2

Abstract

Nowadays, automatic processing and object extraction from data acquired by airborne sensors has been an important research topic in photogrammetric institutes. Airborne laser scanning system, which is commonly referred to as LIDAR, is a superior technology for three-dimensional spatial data acquisition from Earth’s surface in high speed and density. 3D city modeling is one of the main applications of LIDAR system. An important first step to reconstruct the building as one of the most important components of urban models is to identify and separate the building points from other points such as; terrain, trees, and vegetation. In addition, building roof SEGMENTATION is another important step in POINT CLOUD processing. In this paper, a multi-agent strategy is proposed for simultaneous extraction of building and SEGMENTATION of roof points from LIDAR POINT CLOUD. First, the ground and vegetation candidate points are removed from building points using local minimums of heights and returned pulse. Then different segments are extracted by analysis of the triangles formed on the remaining points by means of region growing method based on NORMAL VECTORs. Finally, BUILDING SEGMENTS are separated from other segments using area criterion and the united BUILDING SEGMENTS are detected using a new method named ‘Grid Dilation’. The proposed method has been tested on the LIDAR data of the Vaihingen city, Germany. In addition to a visual interpretation, a quantitative assessment has been done. Due to lack of a ground truth, control points was selected manually from LIDAR POINT CLOUD and compared with points that classified using proposed method. The proposed method extracts the roof of buildings with an accuracy of 93%. Overall, the results indicate that proposed method could successfully identify the BUILDING SEGMENTS without using additional resources, such as map or aerial photo. The main advantage of this method is its capability for extraction and SEGMENTATION of buildings containing parallel multi-level roof structures even with a very small height differences (e.g. 10 cm).

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APA: Copy

SAJADIAN, M., & AREFI, H.. (2014). A MULTI-AGENT METHOD FOR SIMULTANEOUS BUILDING EXTRACTION AND SEGMENTATION FROM LIDAR POINT CLOUD. JOURNAL OF GEOMATICS SCIENCE AND TECHNOLOGY, 4(2), 53-65. SID. https://sid.ir/paper/249279/en

Vancouver: Copy

SAJADIAN M., AREFI H.. A MULTI-AGENT METHOD FOR SIMULTANEOUS BUILDING EXTRACTION AND SEGMENTATION FROM LIDAR POINT CLOUD. JOURNAL OF GEOMATICS SCIENCE AND TECHNOLOGY[Internet]. 2014;4(2):53-65. Available from: https://sid.ir/paper/249279/en

IEEE: Copy

M. SAJADIAN, and H. AREFI, “A MULTI-AGENT METHOD FOR SIMULTANEOUS BUILDING EXTRACTION AND SEGMENTATION FROM LIDAR POINT CLOUD,” JOURNAL OF GEOMATICS SCIENCE AND TECHNOLOGY, vol. 4, no. 2, pp. 53–65, 2014, [Online]. Available: https://sid.ir/paper/249279/en

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