{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,15]],"date-time":"2026-06-15T11:42:23Z","timestamp":1781523743057,"version":"3.54.1"},"reference-count":42,"publisher":"MDPI AG","issue":"17","license":[{"start":{"date-parts":[[2021,8,31]],"date-time":"2021-08-31T00:00:00Z","timestamp":1630368000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100004731","name":"Natural Science Foundation of Zhejiang Province","doi-asserted-by":"publisher","award":["LHQ20F020001"],"award-info":[{"award-number":["LHQ20F020001"]}],"id":[{"id":"10.13039\/501100004731","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100017577","name":"Basic Public Welfare Research Program of Zhejiang Province","doi-asserted-by":"publisher","award":["LGG18F030003"],"award-info":[{"award-number":["LGG18F030003"]}],"id":[{"id":"10.13039\/501100017577","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>This study presents a novel workflow for automated Digital Terrain Model (DTM) extraction from Airborne LiDAR point clouds based on a convolutional neural network (CNN), considering a transfer learning approach. The workflow consists of three parts: feature image generation, transfer learning using ResNet, and interpolation. First, each point is transformed into a featured image based on its elevation differences with neighboring points. Then, the feature images are classified into ground and non-ground using ImageNet pretrained ResNet models. The ground points are extracted by remapping each feature image to its corresponding points. Last, the extracted ground points are interpolated to generate a continuous elevation surface. We compared the proposed workflow with two traditional filters, namely the Progressive Morphological Filter (PMF) and the Progressive Triangulated Irregular Network Densification (PTD). Our results show that the proposed workflow establishes an advantageous DTM extraction accuracy with yields of only 0.52%, 4.84%, and 2.43% for Type I, Type II, and the total error, respectively. In comparison, Type I, Type II, and the total error for PMF are 7.82%, 11.60%, and 9.48% and for PTD 1.55%, 5.37%, and 3.22%, respectively. The root means square error (RMSE) for the 1 m resolution interpolated DTM is only 7.3 cm. Moreover, we conducted a qualitative analysis to investigate the reliability and limitations of the proposed workflow.<\/jats:p>","DOI":"10.3390\/rs13173448","type":"journal-article","created":{"date-parts":[[2021,8,31]],"date-time":"2021-08-31T22:58:15Z","timestamp":1630450695000},"page":"3448","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":25,"title":["High-Resolution Terrain Modeling Using Airborne LiDAR Data with Transfer Learning"],"prefix":"10.3390","volume":"13","author":[{"given":"Huxiong","family":"Li","sequence":"first","affiliation":[{"name":"School of Mechanical and Electrical Engineering, Shaoxin University, Shaoxing 312000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Weiya","family":"Ye","sequence":"additional","affiliation":[{"name":"Department of Geography and Environmental Management, University of Waterloo, Waterloo, ON N2L 3G1, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jun","family":"Liu","sequence":"additional","affiliation":[{"name":"College of Computer Science and Artificial Intelligence, Wenzhou University, Wenzhou 325035, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7133-0419","authenticated-orcid":false,"given":"Weikai","family":"Tan","sequence":"additional","affiliation":[{"name":"Department of Geography and Environmental Management, University of Waterloo, Waterloo, ON N2L 3G1, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3177-037X","authenticated-orcid":false,"given":"Saied","family":"Pirasteh","sequence":"additional","affiliation":[{"name":"Department of Geography and Environmental Management, University of Waterloo, Waterloo, ON N2L 3G1, Canada"},{"name":"Faculty of Geosciences & Environmental Engineering, Southwest Jiaotong University, Chengdu 611756, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sarah Narges","family":"Fatholahi","sequence":"additional","affiliation":[{"name":"Department of Geography and Environmental Management, University of Waterloo, Waterloo, ON N2L 3G1, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7899-0049","authenticated-orcid":false,"given":"Jonathan","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Geography and Environmental Management, University of Waterloo, Waterloo, ON N2L 3G1, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2021,8,31]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"7323","DOI":"10.3390\/s8117323","article-title":"A comprehensive automated 3D approach for building extraction, reconstruction, and regularization from airborne laser scanning point clouds","volume":"8","author":"Dorninger","year":"2008","journal-title":"Sensors"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"3358","DOI":"10.1109\/JSTARS.2018.2859957","article-title":"Quantifying the carbon storage in urban trees using multispectral ALS data","volume":"11","author":"Chen","year":"2018","journal-title":"IEEE J. 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