{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T04:10:31Z","timestamp":1760242231761,"version":"build-2065373602"},"reference-count":47,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2017,1,10]],"date-time":"2017-01-10T00:00:00Z","timestamp":1484006400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Seventh Framework Programme","award":["312912"],"award-info":[{"award-number":["312912"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJGI"],"abstract":"<jats:p>Very high spatial resolution (VHSR) stereo-imagery-derived digital surface models (DSM) can be used to generate digital elevation models (DEM). Filtering algorithms and triangular irregular network (TIN) densification are the most common approaches. Most filter-based techniques focus on image-smoothing. We propose a new approach which makes use of integrated object-based image analysis (OBIA) techniques. An initial land cover classification is followed by stratified land cover ground point sample detection, using object-specific features to enhance the sampling quality. The detected ground point samples serve as the basis for the interpolation of the DEM. A regional uncertainty index (RUI) is calculated to express the quality of the generated DEM in regard to the DSM, based on the number of samples per land cover object. The results of our approach are compared to a high resolution Light Detection and Ranging (LiDAR)-DEM, and a high level of agreement is observed\u2014especially for non-vegetated and scarcely-vegetated areas. Results show that the accuracy of the DEM is highly dependent on the quality of the initial DSM and\u2014in accordance with the RUI\u2014differs between the different land cover classes.<\/jats:p>","DOI":"10.3390\/ijgi6010009","type":"journal-article","created":{"date-parts":[[2017,1,10]],"date-time":"2017-01-10T10:16:18Z","timestamp":1484043378000},"page":"9","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Terrain Extraction in Built-Up Areas from Satellite Stereo-Imagery-Derived Surface Models: A Stratified Object-Based Approach"],"prefix":"10.3390","volume":"6","author":[{"given":"Fritjof","family":"Luethje","sequence":"first","affiliation":[{"name":"Department of Geoinformatics\u2014Z_GIS, University of Salzburg, Salzburg 5020, Austria"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5473-3344","authenticated-orcid":false,"given":"Dirk","family":"Tiede","sequence":"additional","affiliation":[{"name":"Department of Geoinformatics\u2014Z_GIS, University of Salzburg, Salzburg 5020, Austria"}]},{"given":"Clemens","family":"Eisank","sequence":"additional","affiliation":[{"name":"Department of Geoinformatics\u2014Z_GIS, University of Salzburg, Salzburg 5020, Austria"}]}],"member":"1968","published-online":{"date-parts":[[2017,1,10]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Florinsky, I.V. 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