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3D City Modeling and Observation Using Remote Sensing and Artificial Intelligence

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

Deadline for manuscript submissions: 31 August 2025 | Viewed by 1273

Special Issue Editors

Dr. Zheng Ji

E-Mail Website
Guest Editor
School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China
Interests: 3D reconstruction; UAV intelligent remote sensing; 3D measurement; 3D real scene modelling; VR-AR application; deep learning algorithm; stereo visual system; laser scanning
Dr. Brian Alan Johnson

E-Mail Website
Guest Editor
Natural Resources and Ecosystem Services, Institute for Global Environmental Strategies, Kanagawa 240-0115, Japan
Interests: geographic information systems (GIS); remote sensing; spatial modeling; and data mining for urban and environmental analysis and planning; mapping urban land cover (green space, impervious surfaces, etc.); monitoring forest health using fine resolution satellite imagery
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

3D City modeling stands as a fundamental pillar for the burgeoning field of digital twin applications in modern cities. The evolution of cutting-edge remote sensing equipment, platforms, and methodologies has facilitated the acquisition of three-dimensional spatial data, making it an increasingly accessible process. Leveraging urban maps and three-dimensional datasets, the observation and analysis of critical urban objects in both static and dynamic states have been integrated into a variety of sectors, including urban planning, management, safety monitoring, and the burgeoning domain of smart cities.

While remote sensing offers the tools for modeling and observation, artificial intelligence (AI) has elevated these processes to new heights. AI's broad spectrum of object recognition and execution of complex tasks is characterized by its efficiency and reliability. The proliferation of mobile remote sensing platforms, such as drones and laser scanners, has democratized low-cost, automated urban remote sensing monitoring.

The evolution of 3D modeling techniques from various optimized models of multi-view geometry reconstruction is now progressing towards methods based on neural networks, such as deep learning for stereo reconstruction and neural radiance fields.

The quest to integrate more sophisticated AI algorithms into the realms of modeling and observational data analysis has emerged as a pivotal area of interest. The ongoing advancements in SOTA methods have not only validated this trend but have also set the stage for a new era of innovation in the field of 3D modeling.

This Special Issue aims at studies covering remote sensing platforms, systems,new algorithms, and the latest experimental datasets. Relevant research can contribute to enhancing the quality, efficiency, and application level of 3D city modeling and observational data analysis. This special issue includes, but is not limited to, the following topics:

  • Intelligent mobile measurement platform
  • 3D building modelling
  • 3D modelling with multi-source space data
  • Object recognition using AI algorithm
  • City space plane & management
  • UAV smart task plan
  • Realtime data processing
  • Neural network 3D algorithm 
  • Laser scanning data processing
  • City objects datasets

Dr. Zheng Ji
Dr. Brian Alan Johnson
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

  • 3D city modlling
  • city observation
  • AI algorithm
  • mobile measurement platform
  • laser scanning data processing
  • object recognition and positioning
  • neural network for 3D reconstruction
  • multi-source space data processing

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Published Papers (2 papers)

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Research

19 pages, 43835 KiB  
Article
A Stereo Disparity Map Refinement Method Without Training Based on Monocular Segmentation and Surface Normal
by Haoxuan Sun and Taoyang Wang
Remote Sens. 2025, 17(9), 1587; https://doi.org/10.3390/rs17091587 - 30 Apr 2025
Viewed by 172
Abstract
Stereo disparity estimation is an essential component in computer vision and photogrammetry with many applications. However, there is a lack of real-world large datasets and large-scale models in the domain. Inspired by recent advances in the foundation model for image segmentation, we explore [...] Read more.
Stereo disparity estimation is an essential component in computer vision and photogrammetry with many applications. However, there is a lack of real-world large datasets and large-scale models in the domain. Inspired by recent advances in the foundation model for image segmentation, we explore the RANSAC disparity refinement based on zero-shot monocular surface normal prediction and SAM segmentation masks, which combine stereo matching models and advanced monocular large-scale vision models. The disparity refinement problem is formulated as follows: extracting geometric structures based on SAM masks and surface normal prediction, building disparity map hypotheses of the geometric structures, and selecting the hypotheses-based weighted RANSAC method. We believe that after obtaining geometry structures, even if there is only a part of the correct disparity in the geometry structure, the entire correct geometry structure can be reconstructed based on the prior geometry structure. Our method can best optimize the results of traditional models such as SGM or deep learning models such as MC-CNN. The model obtains 15.48% D1-error without training on the US3D dataset and obtains 6.09% bad 2.0 error and 3.65% bad 4.0 error on the Middlebury dataset. The research helps to promote the development of scene and geometric structure understanding in stereo disparity estimation and the application of combining advanced large-scale monocular vision models with stereo matching methods. Full article
(This article belongs to the Special Issue 3D City Modeling and Observation Using Remote Sensing and Artificial Intelligence)
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18 pages, 22424 KiB  
Article
Class-Incremental Semantic Segmentation for Mobile Laser Scanning Point Clouds Using Feature Representation Preservation and Loss Cross-Coupling
by Xucheng Chen, Haifeng Luo, Tianqiang Huang, Hanxian He and Wenyan Hu
Remote Sens. 2025, 17(3), 541; https://doi.org/10.3390/rs17030541 - 5 Feb 2025
Viewed by 698
Abstract
Significant progress has been made in the semantic segmentation of mobile laser scanning (MLS) point clouds based on deep learning. However, the segmentation classes of deep learning models depend on the label classes of the source point clouds used for training, which makes [...] Read more.
Significant progress has been made in the semantic segmentation of mobile laser scanning (MLS) point clouds based on deep learning. However, the segmentation classes of deep learning models depend on the label classes of the source point clouds used for training, which makes it difficult to generalize the models to target point clouds with novel classes. In addition, retraining models using complete class label datasets is time-consuming, and the source point clouds are often unavailable or occupy a large amount of storage space. In this paper, we propose a new class-incremental semantic segmentation framework for MLS point clouds. Firstly, to prevent catastrophic forgetting of original class knowledge when the model learns novel classes, we design a feature representation preservation-based knowledge distillation module to maintain the encoding ability of the target models for original classes. Then, to further separate novel classes from the original background classes, we introduce a background shift mechanism based on loss cross-coupling and pseudo-label collaborative training, which adaptively balances the model plasticity when learning novel class knowledge. Finally, we conducted extensive experiments on two benchmark datasets (Paris-Lille-3D and Toronto-3D), and our proposed method achieved impressive results, which indicate that the proposed framework could effectively achieve class-incremental semantic segmentation for MLS point clouds. Full article
(This article belongs to the Special Issue 3D City Modeling and Observation Using Remote Sensing and Artificial Intelligence)
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