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Investigating Prior-Level Fusion Approaches for Enriched Semantic Segmentation of Urban LiDAR Point Clouds

Ballouch, Zouhair ; Hajji, Rafika ; Kharroubi, Abderrazzaq ; Poux, Florent ; Billen, Roland

Remote sensing (Basel, Switzerland), 2024-01, Vol.16 (2), p.329 [Periódico revisado por pares]

Basel: MDPI AG

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Título:
Investigating Prior-Level Fusion Approaches for Enriched Semantic Segmentation of Urban LiDAR Point Clouds
Autor: Ballouch, Zouhair ; Hajji, Rafika ; Kharroubi, Abderrazzaq ; Poux, Florent ; Billen, Roland
Assuntos: Accuracy ; Automation ; Cities ; Comparative analysis ; data fusion ; Datasets ; Decision making ; Deep learning ; Digital twins ; Earth sciences & physical geography ; enriched semantic segmentation ; Enrichment ; Image segmentation ; Investigations ; Knowledge ; Level fusion ; Lidar ; LiDAR point cloud ; LiDAR point clouds ; Neural networks ; Optical radar ; Parameter modification ; Physical, chemical, mathematical & earth Sciences ; Physique, chimie, mathématiques & sciences de la terre ; Point-clouds ; Prior-knowledge ; prior-level fusion ; Remote sensing ; Sciences de la terre & géographie physique ; Semantic segmentation ; Semantics ; urban environment ; Urban environments ; Urban planning
É parte de: Remote sensing (Basel, Switzerland), 2024-01, Vol.16 (2), p.329
Notas: scopus-id:2-s2.0-85183319519
Descrição: 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’s learning pipeline with the point cloud data. The goal was to identify the one that most profoundly enhanced the neural network’s knowledge. Two deep learning techniques, “RandLaNet” and “KPConv”, 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.
Editor: Basel: MDPI AG
Idioma: Inglês

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Investigating Prior-Level Fusion Approaches for Enriched Semantic Segmentation of Urban LiDAR Point Clouds

Ballouch, Zouhair ; Hajji, Rafika ; Kharroubi, Abderrazzaq ; Poux, Florent ; Billen, Roland

Basel: MDPI AG

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