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3D and Semantic Reconstruction of the Urban Environment Using Multi-Modal and Multi-Resolution Remote Sensing Data

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Urban Remote Sensing".

Deadline for manuscript submissions: 31 August 2025 | Viewed by 8780

Special Issue Editors

Dr. Wufan Zhao

E-Mail Website
Guest Editor
Urban Governance and Design Thrust, The Hong Kong University of Science and Technology (Guangzhou), Guangzhou 511453, China
Interests: computer vision; remote sensing; 3D modeling; urban analytics
Special Issues, Collections and Topics in MDPI journals
Dr. Andrea Nascetti

E-Mail Website
Guest Editor
Faculty of Science, Department of Geography, University of Liège, Allée du Six Août 19, 4000 Liège, Belgium
Interests: photogrammetry; remote sensing; SAR; image processing; 3D modeling
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Maarten Vergauwen

E-Mail Website
Guest Editor
Geomatics Research Group, Department of Civil Engineering, KU Leuven, Gebroeders De Smetstraat 1, 9000 Ghent, Belgium
Interests: photogrammetry; computer vision; machine learning; 3D modeling
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Photogrammetry and remote sensing techniques are utilized to produce 3D models of urban scenes using satellite, aerial, and terrestrial data with varying levels of automation, accuracy, and replicability. These 3D models serve as the fundamental geometric structure for 3D geospatial environments and facilitate the integration of indoor data. The semantic information associated with 3D data enables spatiotemporal querying and analysis, making high-quality 3D semantic models valuable tools for environmental analysis and urban management.

The enormous amount of different modality remote sensing data available today requires powerful and efficient processing techniques to allow an ever-more accurate mapping of the urban environment at ever-higher spatial and temporal resolutions. Recently, advanced deep learning techniques have significantly improved the interpretation quality, efficiency, and automation levels in 3D scene understanding using remote sensing data. This Special Issue seeks to address the latest developments in remote sensing-based 3D urban scene reconstruction—from innovative methods and new benchmark datasets to relevant application examples.

This Special Issue welcomes contributions that showcase recent advancements in processing multimodal data for the geometric and semantic generation of 3D scenes, focusing on the urban environment. The entire pipeline, from data collection and processing to 3D object detection, modeling, and reconstruction and up to their representation, visualization, and application in urban environment analytics, is envisaged. We encourage the submission of open-source and open access methodologies and datasets.

The topics of this Special Issue include, but are not limited to:

  • Weakly or self-supervised methods for extracting 3D semantic information of the urban environment;
  • Multimodal approaches for combining different sensing technologies (e.g., multispectral, LiDAR, and SAR);
  • Multiplatform (satellite, aerial, and terrestrial) and multiresolution data fusion approaches for 3D urban scene reconstruction;
  • New benchmark datasets;
  • Automatic 3D urban object identification and change detection methods from imagery, point clouds, and meshes;
  • End-to-end approaches for the automatic generation of high-level semantic objects (e.g., LoD and BIM);
  • Methods for efficient storage, processing, and visualization of 3D urban objects with a high level of detail and semantic attributes;
  • Assessment of quality of 3D urban scene models, analysis of scalability, and replicability of proposed methods;
  • Urban analytics based on multimodal remote sensing data and 3D building models.

Dr. Wufan Zhao
Dr. Andrea Nascetti
Prof. Dr. Maarten Vergauwen
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • machine learning/deep learning
  • data efficient learning (foundation model, transfer learning, self-supervised, semi-supervised, and weakly supervised learning)
  • semantic/instance segmentation, object detection/tracking, and change detection
  • SCAN-to-BIM
  • data processing and feature extraction
  • point cloud registration
  • multisource data fusion
  • application of 3D point clouds and remote sensing images
  • spatial digital twins and smart cities

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (4 papers)

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Research

22 pages, 15296 KiB  
Article
Reconstructing Geometric Models from the Point Clouds of Buildings Based on a Transformer Network
by Cheng Wang, Haibing Liu and Fei Deng
Remote Sens. 2025, 17(3), 359; https://doi.org/10.3390/rs17030359 - 22 Jan 2025
Viewed by 890
Abstract
Geometric building models are essential in BIM technology. The reconstruction results using current methods are usually represented using mesh, which is limited to visualization purposes and hard to directly import into BIM or modeling software for further application. In this paper, we propose [...] Read more.
Geometric building models are essential in BIM technology. The reconstruction results using current methods are usually represented using mesh, which is limited to visualization purposes and hard to directly import into BIM or modeling software for further application. In this paper, we propose a building model reconstruction method based on a transformer network (DeepBuilding). Instead of reconstructing the polyhedron model of buildings, we strive to recover the CAD modeling operation of constructing the building models from the building point cloud. By representing the building model with its modeling sequence, the reconstruction results can be imported into BIM software for further application. We first translate the procedure of constructing a building model into a command sequence that can be vectorized and processed by the transformer network. Then, we propose a transformer-based network that can convert input point clouds into the vectorized representation of the modeling sequences by decoding the geometry information encoded in the point features. A tool is developed to convert the vectorized modeling sequence into a 3D shape representation (such as mesh) or file format that other BIM software supports. Comprehensive experiments are conducted, and the evaluation results demonstrate that our method can produce competitive reconstruction results with high geometric fidelity while preserving more details of the building reconstruction. Full article
(This article belongs to the Special Issue 3D and Semantic Reconstruction of the Urban Environment Using Multi-Modal and Multi-Resolution Remote Sensing Data)
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18 pages, 4924 KiB  
Article
LOD2-Level+ Low-Rise Building Model Extraction Method for Oblique Photography Data Using U-NET and a Multi-Decision RANSAC Segmentation Algorithm
by Yufeng He, Xiaobian Wu, Weibin Pan, Hui Chen, Songshan Zhou, Shaohua Lei, Xiaoran Gong, Hanzeyu Xu and Yehua Sheng
Remote Sens. 2024, 16(13), 2404; https://doi.org/10.3390/rs16132404 - 30 Jun 2024
Cited by 2 | Viewed by 1563
Abstract
Oblique photography is a regional digital surface model generation technique that can be widely used for building 3D model construction. However, due to the lack of geometric and semantic information about the building, these models make it difficult to differentiate more detailed components [...] Read more.
Oblique photography is a regional digital surface model generation technique that can be widely used for building 3D model construction. However, due to the lack of geometric and semantic information about the building, these models make it difficult to differentiate more detailed components in the building, such as roofs and balconies. This paper proposes a deep learning-based method (U-NET) for constructing 3D models of low-rise buildings that address the issues. The method ensures complete geometric and semantic information and conforms to the LOD2 level. First, digital orthophotos are used to perform building extraction based on U-NET, and then a contour optimization method based on the main direction of the building and the center of gravity of the contour is used to obtain the regular building contour. Second, the pure building point cloud model representing a single building is extracted from the whole point cloud scene based on the acquired building contour. Finally, the multi-decision RANSAC algorithm is used to segment the building detail point cloud and construct a triangular mesh of building components, followed by a triangular mesh fusion and splicing method to achieve monolithic building components. The paper presents experimental evidence that the building contour extraction algorithm can achieve a 90.3% success rate and that the resulting single building 3D model contains LOD2 building components, which contain detailed geometric and semantic information. Full article
(This article belongs to the Special Issue 3D and Semantic Reconstruction of the Urban Environment Using Multi-Modal and Multi-Resolution Remote Sensing Data)
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24 pages, 30702 KiB  
Article
Towards Urban Digital Twins: A Workflow for Procedural Visualization Using Geospatial Data
by Sanjay Somanath, Vasilis Naserentin, Orfeas Eleftheriou, Daniel Sjölie, Beata Stahre Wästberg and Anders Logg
Remote Sens. 2024, 16(11), 1939; https://doi.org/10.3390/rs16111939 - 28 May 2024
Cited by 6 | Viewed by 3201
Abstract
A key feature for urban digital twins (DTs) is an automatically generated detailed 3D representation of the built and unbuilt environment from aerial imagery, footprints, LiDAR, or a fusion of these. Such 3D models have applications in architecture, civil engineering, urban planning, construction, [...] Read more.
A key feature for urban digital twins (DTs) is an automatically generated detailed 3D representation of the built and unbuilt environment from aerial imagery, footprints, LiDAR, or a fusion of these. Such 3D models have applications in architecture, civil engineering, urban planning, construction, real estate, Geographical Information Systems (GIS), and many other areas. While the visualization of large-scale data in conjunction with the generated 3D models is often a recurring and resource-intensive task, an automated workflow is complex, requiring many steps to achieve a high-quality visualization. Methods for building reconstruction approaches have come a long way, from previously manual approaches to semi-automatic or automatic approaches. This paper aims to complement existing methods of 3D building generation. First, we present a literature review covering different options for procedural context generation and visualization methods, focusing on workflows and data pipelines. Next, we present a semi-automated workflow that extends the building reconstruction pipeline to include procedural context generation using Python and Unreal Engine. Finally, we propose a workflow for integrating various types of large-scale urban analysis data for visualization. We conclude with a series of challenges faced in achieving such pipelines and the limitations of the current approach. However, the steps for a complete, end-to-end solution involve further developing robust systems for building detection, rooftop recognition, and geometry generation and importing and visualizing data in the same 3D environment, highlighting a need for further research and development in this field. Full article
(This article belongs to the Special Issue 3D and Semantic Reconstruction of the Urban Environment Using Multi-Modal and Multi-Resolution Remote Sensing Data)
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23 pages, 5828 KiB  
Article
Investigating Prior-Level Fusion Approaches for Enriched Semantic Segmentation of Urban LiDAR Point Clouds
by Zouhair Ballouch, Rafika Hajji, Abderrazzaq Kharroubi, Florent Poux and Roland Billen
Remote Sens. 2024, 16(2), 329; https://doi.org/10.3390/rs16020329 - 13 Jan 2024
Cited by 2 | Viewed by 1814
Abstract
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 [...] Read more.
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. Full article
(This article belongs to the Special Issue 3D and Semantic Reconstruction of the Urban Environment Using Multi-Modal and Multi-Resolution Remote Sensing Data)
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