{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,30]],"date-time":"2026-07-30T01:59:03Z","timestamp":1785376743344,"version":"3.55.0"},"reference-count":27,"publisher":"World Scientific Pub Co Pte Ltd","issue":"13","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Int. J. Patt. Recogn. Artif. Intell."],"published-print":{"date-parts":[[2026,10]]},"abstract":"<jats:p>High-resolution remote sensing terrain reconstruction is a critical application area for geospatial modeling, topographic analysis, and environmental monitoring. Interpolation-based techniques and classical 2D convolutional models struggle to capture the complex elevation structures due to a lack of contextual information and inadequate volumetric modeling. To mitigate this limitation, this paper presents a new deep learning architecture called TRIDENT (terrain reconstruction via intelligent deep 3D networks) that employs three-dimensional convolutional neural networks (3D-CNNs) for 3D terrain reconstruction from high-resolution remote sensing imagery. TRIDENT integrates multi-scale input fusion and dense voxel-wise encoding to achieve better learning of spatial\u2013spectral relationships and acceptable elevation variation. Experimental evaluations on standard datasets show that TRIDENT exhibits a 17.6% reduction in RMSE and an SSIM improvement of 12.3% over state-of-the-art 2D-CNN and standard surface reconstruction techniques. The outcomes validate the improved performance of TRIDENT in reconstructing correct and high-resolution digital elevation models (DEMs). The model has tremendous potential in mission-critical applications, such as disaster response planning, precision agriculture, urban development and planning, and detecting land-use alterations. With the integration of volumetric learning and remote sensing information, TRIDENT advances the art of terrain modeling with a technically valid and scalable solution.<\/jats:p>","DOI":"10.1142\/s0218001426550098","type":"journal-article","created":{"date-parts":[[2026,4,17]],"date-time":"2026-04-17T02:53:48Z","timestamp":1776394428000},"source":"Crossref","is-referenced-by-count":0,"title":["High-Resolution Remote Sensing Terrain Reconstruction Supported by Three-Dimensional Convolutional Neural Networks"],"prefix":"10.1142","volume":"40","author":[{"ORCID":"https:\/\/orcid.org\/0009-0006-6667-2813","authenticated-orcid":false,"given":"Jeff","family":"Wang","sequence":"first","affiliation":[{"name":"Taiyuan University of Technology, Taiyuan 030024, P. R. China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"219","published-online":{"date-parts":[[2026,7,11]]},"reference":[{"key":"S0218001426550098BIB001","doi-asserted-by":"publisher","DOI":"10.3390\/rs16020263"},{"key":"S0218001426550098BIB002","doi-asserted-by":"publisher","DOI":"10.1080\/13658816.2024.2410345"},{"key":"S0218001426550098BIB003","doi-asserted-by":"publisher","DOI":"10.3390\/min14070686"},{"key":"S0218001426550098BIB004","doi-asserted-by":"publisher","DOI":"10.3390\/rs15184490"},{"key":"S0218001426550098BIB005","doi-asserted-by":"publisher","DOI":"10.3390\/rs13050839"},{"key":"S0218001426550098BIB006","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2024.3383405"},{"key":"S0218001426550098BIB007","doi-asserted-by":"publisher","DOI":"10.3390\/s25164890"},{"key":"S0218001426550098BIB008","first-page":"1","volume":"62","author":"Fang P.","year":"2024","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"issue":"3","key":"S0218001426550098BIB009","first-page":"63","volume":"1","author":"Haider H.","year":"2024","journal-title":"PatternIQ Min."},{"key":"S0218001426550098BIB010","doi-asserted-by":"publisher","DOI":"10.48048\/tis.2024.7333"},{"key":"S0218001426550098BIB011","unstructured":"S. Khallaghi, J. R. Eastman and L. D. Estes, A review of technical factors to consider when designing neural networks for semantic segmentation of Earth Observation imagery, preprint (2023), arXiv:2308.09221."},{"key":"S0218001426550098BIB012","first-page":"104115","volume":"133","author":"Li M.","year":"2024","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"S0218001426550098BIB013","doi-asserted-by":"publisher","DOI":"10.1016\/j.srs.2024.100152"},{"key":"S0218001426550098BIB014","doi-asserted-by":"publisher","DOI":"10.3390\/rs16050773"},{"key":"S0218001426550098BIB015","doi-asserted-by":"publisher","DOI":"10.3390\/app15052371"},{"key":"S0218001426550098BIB016","doi-asserted-by":"publisher","DOI":"10.3390\/rs14174333"},{"key":"S0218001426550098BIB017","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-76189-8_1"},{"key":"S0218001426550098BIB018","doi-asserted-by":"crossref","unstructured":"J. Song, H. Chen, W. Xuan, J. Xia and N. Yokoya, SynRS3D: A synthetic dataset for global 3D semantic understanding from monocular remote sensing imagery, preprint (2024), arXiv:2406.18151.","DOI":"10.52202\/079017-3727"},{"key":"S0218001426550098BIB019","doi-asserted-by":"publisher","DOI":"10.3390\/s24072064"},{"key":"S0218001426550098BIB020","doi-asserted-by":"publisher","DOI":"10.1080\/17538947.2024.2356121"},{"key":"S0218001426550098BIB021","doi-asserted-by":"publisher","DOI":"10.1080\/17538947.2025.2484668"},{"key":"S0218001426550098BIB022","doi-asserted-by":"publisher","DOI":"10.1080\/13658816.2024.2414409"},{"key":"S0218001426550098BIB023","doi-asserted-by":"publisher","DOI":"10.3390\/s24216905"},{"key":"S0218001426550098BIB024","doi-asserted-by":"publisher","DOI":"10.2113\/2024\/lithosphere_2023_273"},{"key":"S0218001426550098BIB025","first-page":"103430","volume":"122","author":"Zhang X.","year":"2023","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"S0218001426550098BIB026","first-page":"1","volume":"62","author":"Zhang Y.","year":"2024","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"S0218001426550098BIB027","doi-asserted-by":"publisher","DOI":"10.3390\/rs16193722"}],"container-title":["International Journal of Pattern Recognition and Artificial Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.worldscientific.com\/doi\/pdf\/10.1142\/S0218001426550098","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,7,30]],"date-time":"2026-07-30T01:30:00Z","timestamp":1785375000000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.worldscientific.com\/doi\/10.1142\/S0218001426550098"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,7,11]]},"references-count":27,"journal-issue":{"issue":"13","published-print":{"date-parts":[[2026,10]]}},"alternative-id":["10.1142\/S0218001426550098"],"URL":"https:\/\/doi.org\/10.1142\/s0218001426550098","relation":{},"ISSN":["0218-0014","1793-6381"],"issn-type":[{"value":"0218-0014","type":"print"},{"value":"1793-6381","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,7,11]]},"article-number":"2655009"}}