{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,3]],"date-time":"2026-06-03T05:04:54Z","timestamp":1780463094483,"version":"3.54.1"},"reference-count":11,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2019,1,18]],"date-time":"2019-01-18T00:00:00Z","timestamp":1547769600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJGI"],"abstract":"<jats:p>The 3D information of road infrastructures is growing in importance with the development of autonomous driving. In this context, the exact 2D position of road markings as well as height information play an important role in, e.g., lane-accurate self-localization of autonomous vehicles. In this paper, the overall task is divided into an automatic segmentation followed by a refined 3D reconstruction. For the segmentation task, we applied a wavelet-enhanced fully convolutional network on multiview high-resolution aerial imagery. Based on the resulting 2D segments in the original images, we propose a successive workflow for the 3D reconstruction of road markings based on a least-squares line-fitting in multiview imagery. The 3D reconstruction exploits the line character of road markings with the aim to optimize the best 3D line location by minimizing the distance from its back projection to the detected 2D line in all the covering images. Results showed an improved IoU of the automatic road marking segmentation by exploiting the multiview character of the aerial images and a more accurate 3D reconstruction of the road surface compared to the semiglobal matching (SGM) algorithm. Further, the approach avoids the matching problem in non-textured image parts and is not limited to lines of finite length. In this paper, the approach is presented and validated on several aerial image data sets covering different scenarios like motorways and urban regions.<\/jats:p>","DOI":"10.3390\/ijgi8010047","type":"journal-article","created":{"date-parts":[[2019,1,18]],"date-time":"2019-01-18T11:26:55Z","timestamp":1547810815000},"page":"47","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["Deep Learning Segmentation and 3D Reconstruction of Road Markings Using Multiview Aerial Imagery"],"prefix":"10.3390","volume":"8","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1718-0004","authenticated-orcid":false,"given":"Franz","family":"Kurz","sequence":"first","affiliation":[{"name":"German Aerospace Center (DLR), Remote Sensing Technology Institute, 82234 We\u00dfling, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6084-2272","authenticated-orcid":false,"given":"Seyed Majid","family":"Azimi","sequence":"additional","affiliation":[{"name":"German Aerospace Center (DLR), Remote Sensing Technology Institute, 82234 We\u00dfling, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chun-Yu","family":"Sheu","sequence":"additional","affiliation":[{"name":"Bosch AG., 3801-856 Stuttgart, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8541-3856","authenticated-orcid":false,"given":"Pablo","family":"d\u2019Angelo","sequence":"additional","affiliation":[{"name":"German Aerospace Center (DLR), Remote Sensing Technology Institute, 82234 We\u00dfling, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2019,1,18]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"147","DOI":"10.5194\/isprs-annals-IV-1-147-2018","article-title":"Automatic 3D lane marking reconstruction using multi-view aerial imagery","volume":"IV-1","author":"Sheu","year":"2018","journal-title":"ISPRS Ann. Photogramm. Remote. Sens. Spat. Inf. Sci."},{"key":"ref_2","unstructured":"Schmid, C., and Zisserman, A. (1997, January 17\u201319). Automatic Line Matching across Views. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern, San Juan, Puerto Rico, USA."},{"key":"ref_3","unstructured":"Bay, H., Ferrari, V., and Gool, L.V. (2005, January 20\u201325). Wide-baseline stereo matching with line segments. Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, San Diego, CA, USA."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"941","DOI":"10.1016\/j.patcog.2008.08.035","article-title":"MSLD: A robust descriptor for line matching","volume":"42","author":"Wang","year":"2009","journal-title":"Pattern Recognit."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Azimi, S.M., Fischer, P., K\u00f6rner, M., and Reinartz, P. (arXiv, 2018). Aerial LaneNet: Lane Marking Semantic Segmentation in Aerial Imagery using Wavelet-Enhanced Cost-sensitive Symmetric Fully Convolutional Neural Networks, arXiv.","DOI":"10.1109\/TGRS.2018.2878510"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Kurz, F., T\u00fcrmer, S., Meynberg, O., Rosenbaum, D., Runge, H., Reinartz, P., and Leitloff, J. (2012). Low-cost Systems for real-time Mapping Applications. Photogramm. Fernerkund. Geoinf., 159\u2013176.","DOI":"10.1127\/1432-8364\/2012\/0109"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1021","DOI":"10.1109\/34.473228","article-title":"Structure and motion from line segments in multiple images","volume":"17","author":"Taylor","year":"1995","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_8","first-page":"189","article-title":"Performance of a real-time sensor and processing system on a helicopter","volume":"XL-1","author":"Kurz","year":"2014","journal-title":"ISPRS Int. Arch. Photogramm. Remote. Sens. Spat. Inf. Sci."},{"key":"ref_9","unstructured":"Fischer, P., Pla\u00df, B., Kurz, F., Krauss, T., and Runge, H. (2017, January 4\u20136). Validation of HD maps for autonomous driving. Proceedings of the International Conference on Intelligent Transport Systems in Theory and Practice, Munich, Germany."},{"key":"ref_10","first-page":"1","article-title":"Semiglobal Matching Results on the ISPRS Stereo Matching Benchmark","volume":"XXXVIII-4\/W19","author":"Reinartz","year":"2011","journal-title":"ISPRS Hann. Workshop"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"31","DOI":"10.1007\/978-3-319-11752-2_3","article-title":"High-Resolution Stereo Datasets with Subpixel-Accurate Ground Truth","volume":"Volume 8753","author":"Scharstein","year":"2014","journal-title":"German Conference on Pattern Recognition"}],"container-title":["ISPRS International Journal of Geo-Information"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2220-9964\/8\/1\/47\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T12:27:15Z","timestamp":1760185635000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2220-9964\/8\/1\/47"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,1,18]]},"references-count":11,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2019,1]]}},"alternative-id":["ijgi8010047"],"URL":"https:\/\/doi.org\/10.3390\/ijgi8010047","relation":{},"ISSN":["2220-9964"],"issn-type":[{"value":"2220-9964","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,1,18]]}}}