Investigating Semi-Automated Cadastral Boundaries Extraction from Airborne Laser Scanned Data

Xianghuan Luo, Rohan Mark Bennett, Mila Koeva and Christiaan Lemmen
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Xianghuan Luo: Department of Land Surveying and Geo-informatics, The Hong Kong Polytechnic University, HongKong, China
Rohan Mark Bennett: Swinburne Business School, Swinburne University of Technology, Hawthorn VIC 3122, Australia
Mila Koeva: Faculty of Geo-Information Science and Earth Observation (ITC), University of Twente, Enschede 7500AE, The Netherlands
Christiaan Lemmen: Faculty of Geo-Information Science and Earth Observation (ITC), University of Twente, Enschede 7500AE, The Netherlands

Land, 2017, vol. 6, issue 3, 1-23

Abstract: Many developing countries have witnessed the urgent need of accelerating cadastral surveying processes. Previous studies found that large portions of cadastral boundaries coincide with visible physical objects, namely roads, fences, and building walls. This research explores the application of airborne laser scanning (ALS) techniques on cadastral surveys. A semi-automated workflow is developed to extract cadastral boundaries from an ALS point clouds. Firstly, a two-phased workflow was developed that focused on extracting digital representations of physical objects. In the automated extraction phase, after classifying points into semantic components, the outline of planar objects such as building roofs and road surfaces were generated by an α-shape algorithm, whilst the centerlines delineatiation approach was fitted into the lineate object—a fence. Afterwards, the extracted vector lines were edited and refined during the post-refinement phase. Secondly, we quantitatively evaluated the workflow performance by comparing results against an exiting cadastral map as reference. It was found that the workflow achieved promising results: around 80% completeness and 60% correctness on average, although the spatial accuracy is still modest. It is argued that the semi-automated extraction workflow could effectively speed up cadastral surveying, with both human resources and equipment costs being reduced

Keywords: cadastral survey; boundary mapping; feature extraction; semi-automation; point cloud (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2017
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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