{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,15]],"date-time":"2026-05-15T16:25:48Z","timestamp":1778862348851,"version":"3.51.4"},"publisher-location":"Cham","reference-count":19,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783030316532","type":"print"},{"value":"9783030316549","type":"electronic"}],"license":[{"start":{"date-parts":[[2019,1,1]],"date-time":"2019-01-01T00:00:00Z","timestamp":1546300800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2019]]},"DOI":"10.1007\/978-3-030-31654-9_14","type":"book-chapter","created":{"date-parts":[[2019,10,31]],"date-time":"2019-10-31T00:05:31Z","timestamp":1572480331000},"page":"160-171","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Power Line Corridor LiDAR Point Cloud Segmentation Using Convolutional Neural Network"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2324-4126","authenticated-orcid":false,"given":"Jisheng","family":"Yang","sequence":"first","affiliation":[]},{"given":"Zijun","family":"Huang","sequence":"additional","affiliation":[]},{"given":"Maochun","family":"Huang","sequence":"additional","affiliation":[]},{"given":"Xianxian","family":"Zeng","sequence":"additional","affiliation":[]},{"given":"Dong","family":"Li","sequence":"additional","affiliation":[]},{"given":"Yun","family":"Zhang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2019,10,31]]},"reference":[{"key":"14_CR1","unstructured":"Qi, C.R., et al.: PointNet: deep learning on point sets for 3D classification and segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2017)"},{"key":"14_CR2","first-page":"126","volume":"38","author":"HB Kim","year":"2010","unstructured":"Kim, H.B., Sohn, G.: 3D classification of power-line scene from airborne laser scanning data using random forests. Int. Arch. Photogramm. Remote Sens. 38, 126\u2013132 (2010)","journal-title":"Int. Arch. Photogramm. Remote Sens."},{"issue":"1","key":"14_CR3","doi-asserted-by":"publisher","first-page":"229","DOI":"10.1080\/01431161.2015.1125549","volume":"37","author":"H Guan","year":"2016","unstructured":"Guan, H., et al.: Extraction of power-transmission lines from vehicle-borne LiDAR data. Int. J. Remote Sens. 37(1), 229\u2013247 (2016)","journal-title":"Int. J. Remote Sens."},{"key":"14_CR4","doi-asserted-by":"crossref","unstructured":"Liu, Y., et al.: Classification of airborne LiDAR intensity data using statistical analysis and hough transform with application to power line corridors. In: 2009 Digital Image Computing: Techniques and Applications. IEEE (2009)","DOI":"10.1109\/DICTA.2009.83"},{"issue":"1","key":"14_CR5","doi-asserted-by":"publisher","first-page":"14","DOI":"10.3390\/ijgi6010014","volume":"6","author":"Y Zhang","year":"2017","unstructured":"Zhang, Y., et al.: UAV low altitude photogrammetry for power line inspection. ISPRS Int. J. Geo-Inf. 6(1), 14 (2017)","journal-title":"ISPRS Int. J. Geo-Inf."},{"key":"14_CR6","doi-asserted-by":"crossref","unstructured":"Maurer, M., et al.: Automated inspection of power line corridors to measure vegetation undercut using UAV-based images. ISPRS Ann. Photogrammetry Remote Sens. Spat. Inf. Sci. 4 (2017)","DOI":"10.5194\/isprs-annals-IV-2-W3-33-2017"},{"key":"14_CR7","unstructured":"Qi, C.R., et al.: PointNet++: deep hierarchical feature learning on point sets in a metric space. In: Advances in Neural Information Processing Systems (2017)"},{"key":"14_CR8","doi-asserted-by":"crossref","unstructured":"Long, J., Shelhamer, E., Darrell, T.: Fully convolutional networks for semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2015)","DOI":"10.1109\/CVPR.2015.7298965"},{"issue":"8","key":"14_CR9","doi-asserted-by":"publisher","first-page":"824","DOI":"10.3390\/rs9080824","volume":"9","author":"Y Zhang","year":"2017","unstructured":"Zhang, Y., et al.: Automatic power line inspection using UAV images. Remote Sens. 9(8), 824 (2017)","journal-title":"Remote Sens."},{"key":"14_CR10","doi-asserted-by":"crossref","unstructured":"Aubry, M., Schlickewei, U., Cremers, D.: The wave kernel signature: a quantum mechanical approach to shape analysis. In: 2011 IEEE International Conference on Computer Vision Workshops (ICCV Workshops). IEEE (2011)","DOI":"10.1109\/ICCVW.2011.6130444"},{"key":"14_CR11","doi-asserted-by":"crossref","unstructured":"Bronstein, M.M., Kokkinos, I.: Scale-invariant heat kernel signatures for non-rigid shape recognition. In: 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. IEEE (2010)","DOI":"10.1109\/CVPR.2010.5539838"},{"key":"14_CR12","doi-asserted-by":"crossref","unstructured":"Rusu, R.B., et al.: Aligning point cloud views using persistent feature histograms. In: 2008 IEEE\/RSJ International Conference on Intelligent Robots and Systems. IEEE (2008)","DOI":"10.1109\/IROS.2008.4650967"},{"key":"14_CR13","doi-asserted-by":"crossref","unstructured":"Maturana, D., Scherer, S.: VoxNet: a 3D convolutional neural network for real-time object recognition. In: 2015 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE (2015)","DOI":"10.1109\/IROS.2015.7353481"},{"key":"14_CR14","doi-asserted-by":"crossref","unstructured":"Qi, C.R., et al.: Volumetric and multi-view CNNs for object classification on 3D data. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2016)","DOI":"10.1109\/CVPR.2016.609"},{"key":"14_CR15","doi-asserted-by":"crossref","unstructured":"He, T., et al.: GeoNet: deep geodesic networks for point cloud analysis. arXiv preprint arXiv:1901.00680 (2019)","DOI":"10.1109\/CVPR.2019.00705"},{"key":"14_CR16","doi-asserted-by":"crossref","unstructured":"Geiger, A., Lenz, P., Urtasun, R.: Are we ready for autonomous driving? The KITTI Vision Benchmark Suite. In: Proceedings CVPR (2012)","DOI":"10.1109\/CVPR.2012.6248074"},{"key":"14_CR17","doi-asserted-by":"crossref","unstructured":"Wu, B., et al.: SqueezeSeg: convolutional neural nets with recurrent CRF for real-time road-object segmentation from 3D LiDAR point cloud. In: 2018 IEEE International Conference on Robotics and Automation (ICRA). IEEE (2018)","DOI":"10.1109\/ICRA.2018.8462926"},{"key":"14_CR18","doi-asserted-by":"crossref","unstructured":"Armeni, I., et al.: 3D semantic parsing of large-scale indoor spaces. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2016)","DOI":"10.1109\/CVPR.2016.170"},{"key":"14_CR19","unstructured":"https:\/\/www.danielgm.net\/cc\/"}],"container-title":["Lecture Notes in Computer Science","Pattern Recognition and Computer Vision"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-31654-9_14","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,31]],"date-time":"2024-10-31T00:06:34Z","timestamp":1730333194000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-31654-9_14"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019]]},"ISBN":["9783030316532","9783030316549"],"references-count":19,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-31654-9_14","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019]]},"assertion":[{"value":"31 October 2019","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"PRCV","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Chinese Conference on Pattern Recognition and Computer Vision  (PRCV)","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Xi'an","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2019","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"8 November 2019","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"11 November 2019","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ccprcv2019","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/www.prcv2019.com\/en\/index.html","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"412","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"165","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"40% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"4","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"4","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}