{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,10]],"date-time":"2026-05-10T06:31:57Z","timestamp":1778394717387,"version":"3.51.4"},"reference-count":67,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2019,5,28]],"date-time":"2019-05-28T00:00:00Z","timestamp":1559001600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001655","name":"Deutscher Akademischer Austauschdienst","doi-asserted-by":"publisher","award":["57186656"],"award-info":[{"award-number":["57186656"]}],"id":[{"id":"10.13039\/501100001655","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Various deep learning applications benefit from multi-task learning with multiple regression and classification objectives by taking advantage of the similarities between individual tasks. This can result in improved learning efficiency and prediction accuracy for the task-specific models compared to separately trained models. In this paper, we make an observation of such influences for important remote sensing applications like elevation model generation and semantic segmentation tasks from the stereo half-meter resolution satellite digital surface models (DSMs). Mainly, we aim to generate good-quality DSMs with complete, as well as accurate level of detail (LoD)2-like building forms and to assign an object class label to each pixel in the DSMs. For the label assignment task, we select the roof type classification problem to distinguish between flat, non-flat, and background pixels. To realize those tasks, we train a conditional generative adversarial network (cGAN) with an objective function based on least squares residuals and an auxiliary term based on normal vectors for further roof surface refinement. Besides, we investigate recently published deep learning architectures for both tasks and develop the final end-to-end network, which combines different models, as using them first separately, they provide the best results for their individual tasks.<\/jats:p>","DOI":"10.3390\/rs11111262","type":"journal-article","created":{"date-parts":[[2019,5,28]],"date-time":"2019-05-28T11:18:09Z","timestamp":1559042289000},"page":"1262","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":18,"title":["Multi-Task cGAN for Simultaneous Spaceborne DSM Refinement and Roof-Type Classification"],"prefix":"10.3390","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4048-3583","authenticated-orcid":false,"given":"Ksenia","family":"Bittner","sequence":"first","affiliation":[{"name":"German Aerospace Center (DLR), Remote Sensing Technology Institute, M\u00fcnchner Str. 20, 82234 We\u00dfling, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9186-4175","authenticated-orcid":false,"given":"Marco","family":"K\u00f6rner","sequence":"additional","affiliation":[{"name":"Technical University of Munich (TUM), Department of Civil, Geo and Environmental Engineering, Chair of Remote Sensing Technology, Arcisstra\u00dfe 21, 80333 Munich, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5805-8892","authenticated-orcid":false,"given":"Friedrich","family":"Fraundorfer","sequence":"additional","affiliation":[{"name":"Institute for Computer Graphics and Vision, Graz University of Technology, 8010 Graz, Austria"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8122-1475","authenticated-orcid":false,"given":"Peter","family":"Reinartz","sequence":"additional","affiliation":[{"name":"German Aerospace Center (DLR), Remote Sensing Technology Institute, M\u00fcnchner Str. 20, 82234 We\u00dfling, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2019,5,28]]},"reference":[{"key":"ref_1","unstructured":"Hoja, D., Reinartz, P., and Lehner, M. 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