Volume IV-2
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-2, 81-88, 2018
https://doi.org/10.5194/isprs-annals-IV-2-81-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-2, 81-88, 2018
https://doi.org/10.5194/isprs-annals-IV-2-81-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

  28 May 2018

28 May 2018

INTERACTIVE CADASTRAL BOUNDARY DELINEATION FROM UAV DATA

S. Crommelinck1, B. Höfle2,3,4, M. N. Koeva1, M. Y. Yang1, and G. Vosselman1 S. Crommelinck et al.
  • 1Faculty of Geo-Information Science and Earth Observation (ITC), University of Twente, Enschede, the Netherlands
  • 2GIScience Research Group, Institute of Geography, Heidelberg University, Germany
  • 3Heidelberg Center for the Environment (HCE), Heidelberg University, Germany
  • 4Interdisciplinary Center for Scientific Computing (IWR), Heidelberg University, Germany

Keywords: UAV Photogrammetry, Image Analysis, Image Segmentation, Object Detection, Cadastral Boundary Delineation, Machine Learning, Land Administration, Cadastral Mapping

Abstract. Unmanned aerial vehicles (UAV) are evolving as an alternative tool to acquire land tenure data. UAVs can capture geospatial data at high quality and resolution in a cost-effective, transparent and flexible manner, from which visible land parcel boundaries, i.e., cadastral boundaries are delineable. This delineation is to no extent automated, even though physical objects automatically retrievable through image analysis methods mark a large portion of cadastral boundaries. This study proposes (i) a methodology that automatically extracts and processes candidate cadastral boundary features from UAV data, and (ii) a procedure for a subsequent interactive delineation. Part (i) consists of two state-of-the-art computer vision methods, namely gPb contour detection and SLIC superpixels, as well as a classification part assigning costs to each outline according to local boundary knowledge. Part (ii) allows a user-guided delineation by calculating least-cost paths along previously extracted and weighted lines. The approach is tested on visible road outlines in two UAV datasets from Germany. Results show that all roads can be delineated comprehensively. Compared to manual delineation, the number of clicks per 100 m is reduced by up to 86 %, while obtaining a similar localization quality. The approach shows promising results to reduce the effort of manual delineation that is currently employed for indirect (cadastral) surveying.