{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,22]],"date-time":"2026-03-22T04:33:10Z","timestamp":1774153990635,"version":"3.50.1"},"reference-count":57,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2018,6,19]],"date-time":"2018-06-19T00:00:00Z","timestamp":1529366400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Inspired by the success of deep learning techniques in dense-label prediction and the increasing availability of high precision airborne light detection and ranging (LiDAR) data, we present a research process that compares a collection of well-proven semantic segmentation architectures based on the deep learning approach. Our investigation concludes with the proposition of some novel deep learning architectures for generating detailed land resource maps by employing a semantic segmentation approach. The contribution of our work is threefold. (1) First, we implement the multiclass version of the intersection-over-union (IoU) loss function that contributes to handling highly imbalanced datasets and preventing overfitting. (2) Thereafter, we propose a novel deep learning architecture integrating the deep atrous network architecture with the stochastic depth approach for speeding up the learning process, and impose a regularization effect. (3) Finally, we introduce an early fusion deep layer that combines image-based and LiDAR-derived features. In a benchmark study carried out using the Follo 2014 LiDAR data and the NIBIO AR5 land resources dataset, we compare our proposals to other deep learning architectures. A quantitative comparison shows that our best proposal provides more than 5% relative improvement in terms of mean intersection-over-union over the atrous network, providing a basis for a more frequent and improved use of LiDAR data for automatic land cover segmentation.<\/jats:p>","DOI":"10.3390\/rs10060973","type":"journal-article","created":{"date-parts":[[2018,6,19]],"date-time":"2018-06-19T11:20:36Z","timestamp":1529407236000},"page":"973","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":26,"title":["Land Cover Segmentation of Airborne LiDAR Data Using Stochastic Atrous Network"],"prefix":"10.3390","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8236-7046","authenticated-orcid":false,"given":"Hasan Asy\u2019ari","family":"Arief","sequence":"first","affiliation":[{"name":"Faculty of Science and Technology, Norwegian University of Life Sciences, 1432 \u00c5s, Norway"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7516-0282","authenticated-orcid":false,"given":"Geir-Harald","family":"Strand","sequence":"additional","affiliation":[{"name":"Faculty of Science and Technology, Norwegian University of Life Sciences, 1432 \u00c5s, Norway"},{"name":"Division of Survey and Statistics, Norwegian Institute of Bioeconomy Research, 1431 \u00c5s, Norway"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7818-7050","authenticated-orcid":false,"given":"H\u00e5vard","family":"Tveite","sequence":"additional","affiliation":[{"name":"Faculty of Science and Technology, Norwegian University of Life Sciences, 1432 \u00c5s, Norway"}]},{"given":"Ulf Geir","family":"Indahl","sequence":"additional","affiliation":[{"name":"Faculty of Science and Technology, Norwegian University of Life Sciences, 1432 \u00c5s, Norway"}]}],"member":"1968","published-online":{"date-parts":[[2018,6,19]]},"reference":[{"key":"ref_1","unstructured":"Norwegian Mapping Authority (2018, February 04). 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