Contour detection for UAV-based cadastral mapping

S.C. Crommelinck, R.H. Bennett, M. Gerke, Ying Yang, G. Vosselman

Research output: Contribution to journalArticleAcademicpeer-review

12 Citations (Scopus)
56 Downloads (Pure)

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.
Original languageEnglish
Article number171
Pages (from-to)1-13
Number of pages13
JournalRemote sensing
Volume9
Issue number2
DOIs
Publication statusPublished - 2017

Fingerprint

computer vision
image analysis
segmentation
fieldwork
detection
vehicle
imagery
cost
method
state of the art
rate

Keywords

  • METIS-322173
  • ITC-ISI-JOURNAL-ARTICLE
  • ITC-GOLD

Cite this

@article{6bae2a74573e4387812f2cbcdf571c2f,
title = "Contour detection for UAV-based cadastral mapping",
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.",
keywords = "METIS-322173, ITC-ISI-JOURNAL-ARTICLE, ITC-GOLD",
author = "S.C. Crommelinck and R.H. Bennett and M. Gerke and Ying Yang and G. Vosselman",
year = "2017",
doi = "10.3390/rs9020171",
language = "English",
volume = "9",
pages = "1--13",
journal = "Remote sensing",
issn = "2072-4292",
publisher = "Multidisciplinary Digital Publishing Institute",
number = "2",

}

Contour detection for UAV-based cadastral mapping. / Crommelinck, S.C.; Bennett, R.H.; Gerke, M.; Yang, Ying; Vosselman, G.

In: Remote sensing, Vol. 9, No. 2, 171, 2017, p. 1-13.

Research output: Contribution to journalArticleAcademicpeer-review

TY - JOUR

T1 - Contour detection for UAV-based cadastral mapping

AU - Crommelinck, S.C.

AU - Bennett, R.H.

AU - Gerke, M.

AU - Yang, Ying

AU - Vosselman, G.

PY - 2017

Y1 - 2017

N2 - 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.

AB - 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.

KW - METIS-322173

KW - ITC-ISI-JOURNAL-ARTICLE

KW - ITC-GOLD

UR - https://ezproxy2.utwente.nl/login?url=https://webapps.itc.utwente.nl/library/2017/isi/crommelinck_con.pdf

U2 - 10.3390/rs9020171

DO - 10.3390/rs9020171

M3 - Article

VL - 9

SP - 1

EP - 13

JO - Remote sensing

JF - Remote sensing

SN - 2072-4292

IS - 2

M1 - 171

ER -