{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,29]],"date-time":"2026-01-29T08:49:10Z","timestamp":1769676550361,"version":"3.49.0"},"reference-count":69,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2023,2,9]],"date-time":"2023-02-09T00:00:00Z","timestamp":1675900800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["42001400"],"award-info":[{"award-number":["42001400"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["220100028"],"award-info":[{"award-number":["220100028"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Special Fund of Hubei Luojia Laboratory","award":["42001400"],"award-info":[{"award-number":["42001400"]}]},{"name":"Special Fund of Hubei Luojia Laboratory","award":["220100028"],"award-info":[{"award-number":["220100028"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Ground filtering (GF) is a fundamental step for airborne laser scanning (ALS) data processing. The advent of deep learning techniques provides new solutions to this problem. Existing deep-learning-based methods utilize a segmentation or classification framework to extract ground\/non-ground points, which suffers from a dilemma in keeping high spatial resolution while acquiring rich contextual information when dealing with large-scale ALS data due to the computing resource limits. To this end, we propose SeqGP, a novel deep-learning-based GF pipeline that explicitly converts the GF task into an iterative sequential ground prediction (SeqGP) problem using points-profiles. The proposed SeqGP utilizes deep reinforcement learning (DRL) to optimize the prediction sequence and retrieve the bare terrain gradually. The 3D sparse convolution is integrated with the SeqGP strategy to generate high-precision classification results with memory efficiency. Extensive experiments on two challenging test sets demonstrate the state-of-the-art filtering performance and universality of the proposed method in dealing with large-scale ALS data.<\/jats:p>","DOI":"10.3390\/rs15040961","type":"journal-article","created":{"date-parts":[[2023,2,10]],"date-time":"2023-02-10T05:51:06Z","timestamp":1676008266000},"page":"961","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["Deep Ground Filtering of Large-Scale ALS Point Clouds via Iterative Sequential Ground Prediction"],"prefix":"10.3390","volume":"15","author":[{"given":"Hengming","family":"Dai","sequence":"first","affiliation":[{"name":"School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China"}]},{"given":"Xiangyun","family":"Hu","sequence":"additional","affiliation":[{"name":"School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China"},{"name":"Hubei Luojia Laboratory, Wuhan 430079, China"},{"name":"Institute of Artificial Intelligence in Geomatics, Wuhan University, Wuhan 430079, China"}]},{"given":"Zhen","family":"Shu","sequence":"additional","affiliation":[{"name":"School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China"}]},{"given":"Nannan","family":"Qin","sequence":"additional","affiliation":[{"name":"School of Remote Sensing and Geomatics Engineering, Nanjing University of Information Science and Technology, Nanjing 210044, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7294-7496","authenticated-orcid":false,"given":"Jinming","family":"Zhang","sequence":"additional","affiliation":[{"name":"Key Laboratory of Network Information System Technology, Institute of Electronic, Chinese Academy of Sciences, Beijing 100190, China"},{"name":"The Aerospace Information Research Institute, Chinese Academic of Sciences, Beijing 100190, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,2,9]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"112114","DOI":"10.1016\/j.rse.2020.112114","article-title":"Estimating wildfire fuel consumption with multitemporal airborne laser scanning data and demonstrating linkage with MODIS-derived fire radiative energy","volume":"251","author":"McCarley","year":"2020","journal-title":"Remote Sens. 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