The contribution of Deep Learning to the semantic segmentation of 3D point-clouds in urban areas

Ballouch, Zouhair; Hajji, Rafika; Elmir, Mohammed

doi:10.1109/morgeo49228.2020.9121898

Cited by 3 publications

(3 citation statements)

References 24 publications

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“…These last ones have shown good performance in terms of precision, efficiency, and robustness. However, they are more data-intensive and require performant computing platforms [21]. This is due to the massive characteristics of the fused data, which can easily exceed the memory limit of desktop computers.…”

Section: Discussionmentioning

confidence: 99%

“…It requires significant financial and material resources, as well as a lot of computational memory and consequently a high computation time. Furthermore, these data-intensive approaches need to collect different types of data in a minimal time interval to avoid any change in the urban environment [21]. In addition, some information would not add much to the differentiation of urban objects.…”

Section: Introductionmentioning

confidence: 99%

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A Prior Level Fusion Approach for the Semantic Segmentation of 3D Point Clouds Using Deep Learning

Ballouch

Hajji²,

Poux³

et al. 2022

Remote Sensing

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Three-dimensional digital models play a pivotal role in city planning, monitoring, and sustainable management of smart and Digital Twin Cities (DTCs). In this context, semantic segmentation of airborne 3D point clouds is crucial for modeling, simulating, and understanding large-scale urban environments. Previous research studies have demonstrated that the performance of 3D semantic segmentation can be improved by fusing 3D point clouds and other data sources. In this paper, a new prior-level fusion approach is proposed for semantic segmentation of large-scale urban areas using optical images and point clouds. The proposed approach uses image classification obtained by the Maximum Likelihood Classifier as the prior knowledge for 3D semantic segmentation. Afterwards, the raster values from classified images are assigned to Lidar point clouds at the data preparation step. Finally, an advanced Deep Learning model (RandLaNet) is adopted to perform the 3D semantic segmentation. The results show that the proposed approach provides good results in terms of both evaluation metrics and visual examination with a higher Intersection over Union (96%) on the created dataset, compared with (92%) for the non-fusion approach.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Prior Level Fusion Approach for the Semantic Segmentation of 3D Point Clouds Using Deep Learning

Ballouch

Hajji²,

Poux³

et al. 2022

Remote Sensing

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“…However, despite their performance, this family has some disadvantages related to significant memory and computation time requirements. In addition, they require to have a simultaneous or minimal difference between the acquisition times of the two types of data (Ballouch et al, 2020). There is a large number of point-level fusion processes that have been developed for 3D semantic segmentation.…”

Section: Point-level Fusion Approachesmentioning

confidence: 99%

3D Point Cloud Enriched Semantic Segmentation through Knowledge-Enhanced Deep Learning Technique

Ballouch¹,

Hajji²,

Kharroubi³

et al. 2023

Preprint

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Digital Twin Cities (DTCs) play a fundamental role in city planning and management. They allow three-dimensional modeling and simulation of cities. 3D semantic segmentation is the foundation for automatically creating enriched DTCs, as well as their updates. Past studies indicate that prior level fusion approaches demonstrate more promising precisions in 3D semantic segmentation compared to point level fusion, features level fusion, and decision level fusion families. In order to improve point cloud enriched semantic segmentation outcomes, this article proposes a new approach for 3D point cloud semantic segmentation through developing and benchmarking three prior level fusion scenarios. A reference approach based on point clouds and aerial images was proposed to compare it with the different developed scenarios. In each scenario, we inject a specific prior knowledge (geometric features,classified images ,etc) and aerial images as attributes of point clouds into the neural network’s learning pipeline. The objective is to find the one that integrates the most significant prior knowledge and enhances neural network knowledge more profoundly, which we have named the "smart fusion approach". The advanced Deep Learning algorithm "RandLaNet" was adopted to implement the different proposed scenarios and the reference approach, due to its excellent performance demonstrated in the literature. The introduction of some significant features associated with the label classes facilitated the learning process and improved the semantic segmentation results that can be achievable with the same neural network alone. Overall, our contribution provides a promising solution for addressing some challenges, in particular more accurate extraction of semantically rich objects from the urban fabric. An assessment of the semantic segmentation results obtained by the different scenarios is performed based on metrics computation and visual investigations. Finally,the smart fusion approach was derived based on the obtained qualitative and quantitative results.

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Development of the BIM Model

Hajji¹,

Oulidi²

2021

Building Information Modeling for a Smart and Sustainable Urban Space

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The contribution of Deep Learning to the semantic segmentation of 3D point-clouds in urban areas

Cited by 3 publications

References 24 publications

A Prior Level Fusion Approach for the Semantic Segmentation of 3D Point Clouds Using Deep Learning

A Prior Level Fusion Approach for the Semantic Segmentation of 3D Point Clouds Using Deep Learning

3D Point Cloud Enriched Semantic Segmentation through Knowledge-Enhanced Deep Learning Technique

Development of the BIM Model

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