3 Citations (Scopus)

Abstract

Unmanned aerial vehicles (UAV) are increasingly investigated with regard to their potential to create and update (cadastral) maps. UAVs provide a flexible and low-cost platform for high-resolution data, from which object outlines can be accurately delineated. This delineation could be automated with image analysis methods to improve existing mapping procedures that are cost, time and labor intensive and of little reproducibility. This study investigates a superpixel approach, namely simple linear iterative clustering (SLIC), in terms of its applicability to UAV data. The approach is investigated in terms of its applicability to high-resolution UAV orthoimages and in terms of its ability to delineate object outlines of roads and roofs. Results show that the approach is applicable to UAV orthoimages of 0.05 m GSD and extents of 100 million and 400 million pixels. Further, the approach delineates the objects with the high accuracy provided by the UAV orthoimages at completeness rates of up to 64%. The approach is not suitable as a standalone approach for object delineation. However, it shows high potential for a combination with further methods that delineate objects at higher correctness rates in exchange of a lower localization quality. This study provides a basis for future work that will focus on the incorporation of multiple methods for an interactive, comprehensive and accurate object delineation from UAV data. This aims to support numerous application fields such as topographic and cadastral mapping.
Original languageEnglish
Title of host publicationProceedings of International Conference on Unmanned Aerial Vehicles in Geomatics, 4-7 September 2017, Bonn, Germany. Peer reviewed Annals, Volume IV-2/W3, 201
EditorsC. Stachniss, W. Förstner, J. Schneider
PublisherInternational Society for Photogrammetry and Remote Sensing (ISPRS)
Pages9-16
Number of pages8
VolumeIV-2/W3
DOIs
Publication statusPublished - 7 Sep 2017
EventInternational Conference on Unmanned Aerial Vehicles in Geomatics - Bonn, Germany
Duration: 4 Sep 20177 Sep 2017

Publication series

NameThe International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
PublisherISPRS

Conference

ConferenceInternational Conference on Unmanned Aerial Vehicles in Geomatics
CountryGermany
CityBonn
Period4/09/177/09/17

Fingerprint

cost
image analysis
roof
vehicle
pixel
labor
road
method
rate
incorporation

Cite this

Crommelinck, S. C., Bennett, R. M., Gerke, M., Koeva, M. N., Yang, M. Y., & Vosselman, G. (2017). SLIC superpixels for object delineation UAV data. In C. Stachniss, W. Förstner, & J. Schneider (Eds.), Proceedings of International Conference on Unmanned Aerial Vehicles in Geomatics, 4-7 September 2017, Bonn, Germany. Peer reviewed Annals, Volume IV-2/W3, 201 (Vol. IV-2/W3, pp. 9-16). (The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences). International Society for Photogrammetry and Remote Sensing (ISPRS). https://doi.org/10.5194/isprs-annals-IV-2-W3-9-2017
Crommelinck, S.C. ; Bennett, R.M. ; Gerke, M. ; Koeva, M.N. ; Yang, M.Y. ; Vosselman, G. / SLIC superpixels for object delineation UAV data. Proceedings of International Conference on Unmanned Aerial Vehicles in Geomatics, 4-7 September 2017, Bonn, Germany. Peer reviewed Annals, Volume IV-2/W3, 201. editor / C. Stachniss ; W. Förstner ; J. Schneider. Vol. IV-2/W3 International Society for Photogrammetry and Remote Sensing (ISPRS), 2017. pp. 9-16 (The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences).
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abstract = "Unmanned aerial vehicles (UAV) are increasingly investigated with regard to their potential to create and update (cadastral) maps. UAVs provide a flexible and low-cost platform for high-resolution data, from which object outlines can be accurately delineated. This delineation could be automated with image analysis methods to improve existing mapping procedures that are cost, time and labor intensive and of little reproducibility. This study investigates a superpixel approach, namely simple linear iterative clustering (SLIC), in terms of its applicability to UAV data. The approach is investigated in terms of its applicability to high-resolution UAV orthoimages and in terms of its ability to delineate object outlines of roads and roofs. Results show that the approach is applicable to UAV orthoimages of 0.05 m GSD and extents of 100 million and 400 million pixels. Further, the approach delineates the objects with the high accuracy provided by the UAV orthoimages at completeness rates of up to 64{\%}. The approach is not suitable as a standalone approach for object delineation. However, it shows high potential for a combination with further methods that delineate objects at higher correctness rates in exchange of a lower localization quality. This study provides a basis for future work that will focus on the incorporation of multiple methods for an interactive, comprehensive and accurate object delineation from UAV data. This aims to support numerous application fields such as topographic and cadastral mapping.",
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Crommelinck, SC, Bennett, RM, Gerke, M, Koeva, MN, Yang, MY & Vosselman, G 2017, SLIC superpixels for object delineation UAV data. in C Stachniss, W Förstner & J Schneider (eds), Proceedings of International Conference on Unmanned Aerial Vehicles in Geomatics, 4-7 September 2017, Bonn, Germany. Peer reviewed Annals, Volume IV-2/W3, 201. vol. IV-2/W3, The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, International Society for Photogrammetry and Remote Sensing (ISPRS), pp. 9-16, International Conference on Unmanned Aerial Vehicles in Geomatics, Bonn, Germany, 4/09/17. https://doi.org/10.5194/isprs-annals-IV-2-W3-9-2017

SLIC superpixels for object delineation UAV data. / Crommelinck, S.C.; Bennett, R.M.; Gerke, M.; Koeva, M.N.; Yang, M.Y.; Vosselman, G.

Proceedings of International Conference on Unmanned Aerial Vehicles in Geomatics, 4-7 September 2017, Bonn, Germany. Peer reviewed Annals, Volume IV-2/W3, 201. ed. / C. Stachniss; W. Förstner; J. Schneider. Vol. IV-2/W3 International Society for Photogrammetry and Remote Sensing (ISPRS), 2017. p. 9-16 (The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences).

Research output: Chapter in Book/Report/Conference proceedingChapterAcademicpeer-review

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T1 - SLIC superpixels for object delineation UAV data

AU - Crommelinck, S.C.

AU - Bennett, R.M.

AU - Gerke, M.

AU - Koeva, M.N.

AU - Yang, M.Y.

AU - Vosselman, G.

PY - 2017/9/7

Y1 - 2017/9/7

N2 - Unmanned aerial vehicles (UAV) are increasingly investigated with regard to their potential to create and update (cadastral) maps. UAVs provide a flexible and low-cost platform for high-resolution data, from which object outlines can be accurately delineated. This delineation could be automated with image analysis methods to improve existing mapping procedures that are cost, time and labor intensive and of little reproducibility. This study investigates a superpixel approach, namely simple linear iterative clustering (SLIC), in terms of its applicability to UAV data. The approach is investigated in terms of its applicability to high-resolution UAV orthoimages and in terms of its ability to delineate object outlines of roads and roofs. Results show that the approach is applicable to UAV orthoimages of 0.05 m GSD and extents of 100 million and 400 million pixels. Further, the approach delineates the objects with the high accuracy provided by the UAV orthoimages at completeness rates of up to 64%. The approach is not suitable as a standalone approach for object delineation. However, it shows high potential for a combination with further methods that delineate objects at higher correctness rates in exchange of a lower localization quality. This study provides a basis for future work that will focus on the incorporation of multiple methods for an interactive, comprehensive and accurate object delineation from UAV data. This aims to support numerous application fields such as topographic and cadastral mapping.

AB - Unmanned aerial vehicles (UAV) are increasingly investigated with regard to their potential to create and update (cadastral) maps. UAVs provide a flexible and low-cost platform for high-resolution data, from which object outlines can be accurately delineated. This delineation could be automated with image analysis methods to improve existing mapping procedures that are cost, time and labor intensive and of little reproducibility. This study investigates a superpixel approach, namely simple linear iterative clustering (SLIC), in terms of its applicability to UAV data. The approach is investigated in terms of its applicability to high-resolution UAV orthoimages and in terms of its ability to delineate object outlines of roads and roofs. Results show that the approach is applicable to UAV orthoimages of 0.05 m GSD and extents of 100 million and 400 million pixels. Further, the approach delineates the objects with the high accuracy provided by the UAV orthoimages at completeness rates of up to 64%. The approach is not suitable as a standalone approach for object delineation. However, it shows high potential for a combination with further methods that delineate objects at higher correctness rates in exchange of a lower localization quality. This study provides a basis for future work that will focus on the incorporation of multiple methods for an interactive, comprehensive and accurate object delineation from UAV data. This aims to support numerous application fields such as topographic and cadastral mapping.

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BT - Proceedings of International Conference on Unmanned Aerial Vehicles in Geomatics, 4-7 September 2017, Bonn, Germany. Peer reviewed Annals, Volume IV-2/W3, 201

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Crommelinck SC, Bennett RM, Gerke M, Koeva MN, Yang MY, Vosselman G. SLIC superpixels for object delineation UAV data. In Stachniss C, Förstner W, Schneider J, editors, Proceedings of International Conference on Unmanned Aerial Vehicles in Geomatics, 4-7 September 2017, Bonn, Germany. Peer reviewed Annals, Volume IV-2/W3, 201. Vol. IV-2/W3. International Society for Photogrammetry and Remote Sensing (ISPRS). 2017. p. 9-16. (The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences). https://doi.org/10.5194/isprs-annals-IV-2-W3-9-2017