{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,5,22]],"date-time":"2025-05-22T13:40:02Z","timestamp":1747921202619,"version":"3.41.0"},"publisher-location":"Cham","reference-count":19,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031928048","type":"print"},{"value":"9783031928055","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2025,5,12]],"date-time":"2025-05-12T00:00:00Z","timestamp":1747008000000},"content-version":"vor","delay-in-days":131,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025]]},"abstract":"<jats:title>Abstract<\/jats:title>\n          <jats:p>In this work, we propose a novel way of performing remote inspection of wooden utility poles, based on state-of-the-art computer vision techniques. We collect regular pictures, LiDAR\u00a0scans and hyperspectral scans from a set of five poles, and\u00a0apply state-of-the-art deep learning algorithms to detect and crop\u00a0the poles in the pictures and scans to extract features from these.\u00a0We then study the correlations of these features with pole density\u00a0and surface depth by employing linear models. Our results show that\u00a0our features can be used to predict density and depth with\u00a0good accuracy, a MAE between 500 and 900 for a density range of 14000\u201322000 (unitless) and a MAE of 0.04 for a depth range of 0.6\u20130.9. This study emphasises the potential of both remote sensing\u00a0and state-of-the-art deep learning for wooden pole inspection,\u00a0with techniques suitable for real-world automation.\n<\/jats:p>","DOI":"10.1007\/978-3-031-92805-5_3","type":"book-chapter","created":{"date-parts":[[2025,5,22]],"date-time":"2025-05-22T12:59:08Z","timestamp":1747918748000},"page":"35-49","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Multimodal Computer Vision Techniques for\u00a0Wooden Utility Pole Density Estimation with\u00a0Contact-Free Sensing"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8113-7786","authenticated-orcid":false,"given":"Luis","family":"Gonzalez-Naharro","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6048-9379","authenticated-orcid":false,"given":"Arnoud","family":"Jochemsen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9233-3723","authenticated-orcid":false,"given":"Nabil","family":"Belbachir","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Erik T.","family":"Hauge","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,5,12]]},"reference":[{"issue":"1","key":"3_CR1","doi-asserted-by":"publisher","first-page":"119","DOI":"10.1007\/s13753-020-00254-1","volume":"11","author":"MM Alam","year":"2020","unstructured":"Alam, M.M., Zhu, Z., Eren Tokgoz, B., Zhang, J., Hwang, S.: Automatic assessment and prediction of the resilience of utility poles using unmanned aerial vehicles and computer vision techniques. 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