{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,14]],"date-time":"2026-03-14T18:02:41Z","timestamp":1773511361069,"version":"3.50.1"},"reference-count":37,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2024,1,13]],"date-time":"2024-01-13T00:00:00Z","timestamp":1705104000000},"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>Three-dimensional semantic segmentation is the foundation for automatically creating enriched Digital Twin Cities (DTCs) and their updates. For this task, prior-level fusion approaches show more promising results than other fusion levels. This article proposes a new approach by developing and benchmarking three prior-level fusion scenarios to enhance the outcomes of point cloud-enriched semantic segmentation. The latter were compared with a baseline approach that used the point cloud only. In each scenario, specific prior knowledge (geometric features, classified images, or classified geometric information) and aerial images were fused into the neural network\u2019s learning pipeline with the point cloud data. The goal was to identify the one that most profoundly enhanced the neural network\u2019s knowledge. Two deep learning techniques, \u201cRandLaNet\u201d and \u201cKPConv\u201d, were adopted, and their parameters were modified for different scenarios. Efficient feature engineering and selection for the fusion step facilitated the learning process and improved the semantic segmentation results. Our contribution provides a good solution for addressing some challenges, particularly for more accurate extraction of semantically rich objects from the urban environment. The experimental results have demonstrated that Scenario 1 has higher precision (88%) on the SensatUrban dataset compared to the baseline approach (71%), the Scenario 2 approach (85%), and the Scenario 3 approach (84%). Furthermore, the qualitative results obtained by the first scenario are close to the ground truth. Therefore, it was identified as the efficient fusion approach for point cloud-enriched semantic segmentation, which we have named the efficient prior-level fusion (Efficient-PLF) approach.<\/jats:p>","DOI":"10.3390\/rs16020329","type":"journal-article","created":{"date-parts":[[2024,1,15]],"date-time":"2024-01-15T04:54:38Z","timestamp":1705294478000},"page":"329","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Investigating Prior-Level Fusion Approaches for Enriched Semantic Segmentation of Urban LiDAR Point Clouds"],"prefix":"10.3390","volume":"16","author":[{"given":"Zouhair","family":"Ballouch","sequence":"first","affiliation":[{"name":"College of Geomatic Sciences and Surveying Engineering, IAV Hassan II, Rabat 6202, Morocco"},{"name":"UR SPHERES, Geomatics Unit, University of Li\u00e8ge, 4000 Li\u00e8ge, Belgium"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1291-397X","authenticated-orcid":false,"given":"Rafika","family":"Hajji","sequence":"additional","affiliation":[{"name":"College of Geomatic Sciences and Surveying Engineering, IAV Hassan II, Rabat 6202, Morocco"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7712-6208","authenticated-orcid":false,"given":"Abderrazzaq","family":"Kharroubi","sequence":"additional","affiliation":[{"name":"UR SPHERES, Geomatics Unit, University of Li\u00e8ge, 4000 Li\u00e8ge, Belgium"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6368-4399","authenticated-orcid":false,"given":"Florent","family":"Poux","sequence":"additional","affiliation":[{"name":"UR SPHERES, Geomatics Unit, University of Li\u00e8ge, 4000 Li\u00e8ge, Belgium"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3101-8057","authenticated-orcid":false,"given":"Roland","family":"Billen","sequence":"additional","affiliation":[{"name":"UR SPHERES, Geomatics Unit, University of Li\u00e8ge, 4000 Li\u00e8ge, Belgium"}]}],"member":"1968","published-online":{"date-parts":[[2024,1,13]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Shahat, E., Hyun, C.T., and Yeom, C. 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