Next Article in Journal
Hydrological Modelling using Satellite-Based Crop Coefficients: A Comparison of Methods at the Basin Scale
Next Article in Special Issue
Accuracy Assessment of Digital Surface Models from Unmanned Aerial Vehicles’ Imagery on Glaciers
Previous Article in Journal / Special Issue
Testing Accuracy and Repeatability of UAV Blocks Oriented with GNSS-Supported Aerial Triangulation
Article Menu
Issue 2 (February) cover image

Export Article

Open AccessArticle
Remote Sens. 2017, 9(2), 171; doi:10.3390/rs9020171

Contour Detection for UAV-Based Cadastral Mapping

1
Faculty of Geo-Information Science and Earth Observation (ITC), University of Twente, NL-7500AE Enschede, The Netherlands
2
Institute of Geodesy und Photogrammetry, Technical University of Brunswick, D-38106 Braunschweig, Germany
*
Author to whom correspondence should be addressed.
Academic Editors: Farid Melgani, Francesco Nex, Richard Gloaguen and Prasad S. Thenkabail
Received: 2 December 2016 / Revised: 8 February 2017 / Accepted: 15 February 2017 / Published: 18 February 2017
(This article belongs to the Special Issue Recent Trends in UAV Remote Sensing)
View Full-Text   |   Download PDF [5383 KB, uploaded 18 February 2017]   |  

Abstract

Unmanned aerial vehicles (UAVs) provide a flexible and low-cost solution for the acquisition of high-resolution data. The potential of high-resolution UAV imagery to create and update cadastral maps is being increasingly investigated. Existing procedures generally involve substantial fieldwork and many manual processes. Arguably, multiple parts of UAV-based cadastral mapping workflows could be automated. Specifically, as many cadastral boundaries coincide with visible boundaries, they could be extracted automatically using image analysis methods. This study investigates the transferability of gPb contour detection, a state-of-the-art computer vision method, to remotely sensed UAV images and UAV-based cadastral mapping. Results show that the approach is transferable to UAV data and automated cadastral mapping: object contours are comprehensively detected at completeness and correctness rates of up to 80%. The detection quality is optimal when the entire scene is covered with one orthoimage, due to the global optimization of gPb contour detection. However, a balance between high completeness and correctness is hard to achieve, so a combination with area-based segmentation and further object knowledge is proposed. The localization quality exhibits the usual dependency on ground resolution. The approach has the potential to accelerate the process of general boundary delineation during the creation and updating of cadastral maps. View Full-Text
Keywords: UAV photogrammetry; remote sensing; computer vision; image segmentation; contour generation; object detection; boundary localization; cadastral boundaries; land administration UAV photogrammetry; remote sensing; computer vision; image segmentation; contour generation; object detection; boundary localization; cadastral boundaries; land administration
Figures

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

Scifeed alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

MDPI and ACS Style

Crommelinck, S.; Bennett, R.; Gerke, M.; Yang, M.Y.; Vosselman, G. Contour Detection for UAV-Based Cadastral Mapping. Remote Sens. 2017, 9, 171.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Remote Sens. EISSN 2072-4292 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top