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3D Reconstruction and Mobile Mapping in Urban Environments Using Remote Sensing

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

Deadline for manuscript submissions: 19 July 2024 | Viewed by 7537

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Special Issue Editors

Dr. San Jiang

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Guest Editor

1. School of Computer Sciences, China University of Geosciences, Wuhan 430074, China
2. Department of Land Surveying and Geo-Informatics, The Hong Kong Polytechnic University, Hong Kong 999077, China
Interests: image retrieval; image matching; structure from motion; multi-view stereo; deep learning
Special Issues, Collections and Topics in MDPI journals

Dr. Duojie Weng

E-Mail Website
Guest Editor

1. Department of Land Surveying and Geo-Informatics, The Hong Kong Polytechnic University, Hong Kong 999077, China
2. Research Institute for Artificial Intelligence of Things, The Hong Kong Polytechnic University, Hong Kong 999077, China
Interests: GNSS; urban planning and navigation; indoor positioning
Special Issues, Collections and Topics in MDPI journals

Dr. Jianchen Liu

E-Mail Website
Guest Editor

The College of Geodesy and Geomatics, Shandong University of Science and Technology, Qingdao 266590, China
Interests: image matching; bundle adjustment; 3D reconstruction; image based positioning

Dr. Wanshou Jiang

E-Mail Website
Guest Editor

State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430072, China
Interests: image registering; image classification; change detection; 3D reconstruction
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Urban environments are the support platform for the development and evolution of society, economy, and human life. Recently, remote-sensing-based techniques have become a meaningful solution to maintain the orderly evaluation of urban environments. As two critical and complementary roles, 3D reconstruction and mobile mapping are essential to support varying applications in urban environments, including but not limited to automatic driving, smart logistics, pedestrian navigation, and virtual reality. With the rapid evolution of classical techniques, e.g., SfM (Structure from Motion) and SLAM (Simultaneous Localization and Mapping), and the development of cutting-edge techniques, especially related to deep learning, such as NeRF (Neural Radiance Field), recent years have witnessed the explosive development of 3D reconstruction and mobile mapping in urban environments.

This Special Issue focuses on the techniques for 3D reconstruction and mobile mapping in urban environments, especially for new instruments for data acquisitions in complex urban environments, scale-illumination invariant algorithms for robust feature matching, efficient image retrieval for image or LiDAR-based localization, SfM-based solutions for image orientation, SLAM-based solutions for image or LiDAR processing, and deep-learning-based network for feature detection and matching, etc.

In this topic, the involved data sources are limited to the remote sensing field, including images from high altitude satellites, aerial planes, UAVs and MMS vehicles, and point clouds from airborne and ground scanners.

new instruments for data acquisitions in complex urban environments
scale-illumination invariant algorithms for robust feature matching
deep learning for feature detection and matching
efficient image retrieval for image or LiDAR-based localization
SfM-based solutions for image orientation
SLAM-based solutions for image or LiDAR processing
Neural Radiance Field for 3D reconstruction
high-resolution satellite images for urban building 3D modeling

Dr. San Jiang
Dr. Duojie Weng
Dr. Jianchen Liu
Dr. Wanshou Jiang
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 reconstruction
mobile mapping
photogrammetry
mobile mapping system (MMS)
structure from motion (SfM)
simultaneous localization and mapping (SLAM)
multi-view stereo (MVS)
neural radiance field (NeRF)
global navigation satellite system (GNSS)
light detection and ranging (LiDAR)

Published Papers (10 papers)

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Research

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23 pages, 18047 KiB

Open AccessArticle

A Multi-Level Robust Positioning Method for Three-Dimensional Ground Penetrating Radar (3D GPR) Road Underground Imaging in Dense Urban Areas

by Ju Zhang, Qingwu Hu, Yemei Zhou, Pengcheng Zhao and Xuzhe Duan

Remote Sens. 2024, 16(9), 1559; https://doi.org/10.3390/rs16091559 - 27 Apr 2024

Viewed by 292

Abstract

Three-Dimensional Ground Penetrating Radar (3D GPR) detects subsurface targets non-destructively, rapidly, and continuously. The complex environment around urban roads affects the positioning accuracy of 3D GPR. The positioning accuracy directly affects the data quality, as inaccurate positioning can lead to distortion and misalignment of 3D GPR data. This paper proposed a multi-level robust positioning method to improve the positioning accuracy of 3D GPR in dense urban areas in order to obtain more accurate underground data. In environments with good GNSS signals, fast and high-precision positioning can be achieved based on GNSS data using differential GNSS technology; in scenes with weak GNSS signals, high-precision positioning of subsurface data can be achieved by using GNSS and IMU as well as using GNSS/INS tightly coupled solution technology; in scenes with no GNSS signals, SLAM technology is used for positioning based on INS data and 3D point cloud data. In summary, this method ensures a positioning accuracy of 3D GPR better than 10 cm and high-quality 3D images of underground urban roads in any environment. This provides data support for urban road underground structure surveys and has broad application prospects in underground disease detection and prevention. Full article

(This article belongs to the Special Issue 3D Reconstruction and Mobile Mapping in Urban Environments Using Remote Sensing)

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29 pages, 18448 KiB

Open AccessArticle

Urban Building Height Extraction from Gaofen-7 Stereo Satellite Images Enhanced by Contour Matching

by Yunfan Cui, Shuangming Zhao, Wanshou Jiang and Guorong Yu

Remote Sens. 2024, 16(9), 1556; https://doi.org/10.3390/rs16091556 - 27 Apr 2024

Viewed by 251

Abstract

The traditional method for extracting the heights of urban buildings involves utilizing dense matching algorithms on stereo images to generate a digital surface model (DSM). However, for urban buildings, the disparity discontinuity issue that troubles the dense matching algorithm makes the elevations of high-rise buildings and the surrounding areas inaccurate. The occlusion caused by trees in greenbelts makes it difficult to accurately extract the ground elevation around the building. To tackle these problems, a method for building height extraction from Gaofen-7 (GF-7) stereo images enhanced by contour matching is presented. Firstly, a contour matching algorithm was proposed to extract accurate building roof elevation from GF-7 images. Secondly, a ground filtering algorithm was employed on the DSM to generate a digital elevation model (DEM), and ground elevation can be extracted from this DEM. The difference between the rooftop elevation and the ground elevation represents the building height. The presented method was verified in Yingde, Guangzhou, Guangdong Province, and Xi’an, Shaanxi Province. The experimental results demonstrate that our proposed method outperforms existing methods in building height extraction concerning accuracy. Full article

(This article belongs to the Special Issue 3D Reconstruction and Mobile Mapping in Urban Environments Using Remote Sensing)

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28 pages, 18297 KiB

Open AccessArticle

LVI-Fusion: A Robust Lidar-Visual-Inertial SLAM Scheme

by Zhenbin Liu, Zengke Li, Ao Liu, Kefan Shao, Qiang Guo and Chuanhao Wang

Remote Sens. 2024, 16(9), 1524; https://doi.org/10.3390/rs16091524 - 25 Apr 2024

Viewed by 292

Abstract

With the development of simultaneous positioning and mapping technology in the field of automatic driving, the current simultaneous localization and mapping scheme is no longer limited to a single sensor and is developing in the direction of multi-sensor fusion to enhance the robustness and accuracy. In this study, a localization and mapping scheme named LVI-fusion based on multi-sensor fusion of camera, lidar and IMU is proposed. Different sensors have different data acquisition frequencies. To solve the problem of time inconsistency in heterogeneous sensor data tight coupling, the time alignment module is used to align the time stamp between the lidar, camera and IMU. The image segmentation algorithm is used to segment the dynamic target of the image and extract the static key points. At the same time, the optical flow tracking based on the static key points are carried out and a robust feature point depth recovery model is proposed to realize the robust estimation of feature point depth. Finally, lidar constraint factor, IMU pre-integral constraint factor and visual constraint factor together construct the error equation that is processed with a sliding window-based optimization module. Experimental results show that the proposed algorithm has competitive accuracy and robustness. Full article

(This article belongs to the Special Issue 3D Reconstruction and Mobile Mapping in Urban Environments Using Remote Sensing)

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18 pages, 20434 KiB

Open AccessArticle

High-Precision Map Construction in Degraded Long Tunnel Environments of Urban Subways

by Cheng Li, Wenbo Pan, Xiwen Yuan, Wenyu Huang, Chao Yuan, Quandong Wang and Fuyuan Wang

Remote Sens. 2024, 16(5), 809; https://doi.org/10.3390/rs16050809 - 26 Feb 2024

Viewed by 544

Abstract

In response to the demand for high-precision point cloud mapping of subway trains in long tunnel degradation scenarios in major urban cities, we propose a map construction method based on LiDAR and inertial measurement sensors. This method comprises a tightly coupled frontend odometry system based on error Kalman filters and backend optimization using factor graphs. In the frontend odometry, inertial calculation results serve as predictions for the filter, and residuals between LiDAR points and local map plane point clouds are used for filter updates. The global pose graph is constructed based on inter-frame odometry and other constraint factors, followed by a smoothing optimization for map building. Multiple experiments in subway tunnel scenarios demonstrate that the proposed method achieves robust trajectory estimation in long tunnel scenes, where classical multi-sensor fusion methods fail due to sensor degradation. The proposed method achieves a trajectory consistency of 0.1 m in tunnel scenes, meeting the accuracy requirements for train arrival, parking, and interval operations. Additionally, in an industrial park scenario, the method is compared with ground truth provided by inertial navigation, showing an accumulated error of less than 0.2%, indicating high precision. Full article

(This article belongs to the Special Issue 3D Reconstruction and Mobile Mapping in Urban Environments Using Remote Sensing)

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15 pages, 11133 KiB

Open AccessArticle

Quasi-Dense Matching for Oblique Stereo Images through Semantic Segmentation and Local Feature Enhancement

by Guobiao Yao, Jin Zhang, Fengqi Zhu, Jianya Gong, Fengxiang Jin, Qingqing Fu and Xiaofang Ren

Remote Sens. 2024, 16(4), 632; https://doi.org/10.3390/rs16040632 - 08 Feb 2024

Viewed by 585

Abstract

This paper proposes a quasi-dense feature matching algorithm that combines image semantic segmentation and local feature enhancement networks to address the problem of the poor matching of image features because of complex distortions, considerable occlusions, and a lack of texture on large oblique stereo images. First, a small amount of typical complex scene data are used to train the VGG16-UNet, followed by completing the semantic segmentation of multiplanar scenes across large oblique images. Subsequently, the prediction results of the segmentation are subjected to local adaptive optimization to obtain high-precision semantic segmentation results for each planar scene. Afterward, the LoFTR (Local Feature Matching with Transformers) strategy is used for scene matching, enabling enhanced matching for regions with poor local texture in the corresponding planes. The proposed method was tested on low-altitude large baseline stereo images of complex scenes and compared with five classical matching methods. Results reveal that the proposed method exhibits considerable advantages in terms of the number of correct matches, correct rate of matches, matching accuracy, and spatial distribution of corresponding points. Moreover, it is well-suitable for quasi-dense matching tasks of large baseline stereo images in complex scenes with considerable viewpoint variations. Full article

(This article belongs to the Special Issue 3D Reconstruction and Mobile Mapping in Urban Environments Using Remote Sensing)

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20 pages, 9271 KiB

Open AccessArticle

A Novel Automatic Registration Method for Array InSAR Point Clouds in Urban Scenes

by Chenghao Cui, Yuling Liu, Fubo Zhang, Minan Shi, Longyong Chen, Wenjie Li and Zhenhua Li

Remote Sens. 2024, 16(3), 601; https://doi.org/10.3390/rs16030601 - 05 Feb 2024

Viewed by 721

Abstract

The array interferometric synthetic aperture radar (Array InSAR) system resolves shadow issues by employing two scans in opposite directions, facilitating the acquisition of a comprehensive three-dimensional representation of the observed scene. The point clouds obtained from the two scans need to be transformed into the same coordinate system using registration techniques to create a more comprehensive visual representation. However, the two-point clouds lack corresponding points and exhibit distinct geometric distortions, thereby preventing direct registration. This paper analyzes the error characteristics of array InSAR point clouds and proposes a robust registration method for array InSAR point clouds in urban scenes. It represents the 3D information of the point clouds using images, with pixel positions corresponding to the azimuth and ground range directions. Pixel intensity denotes the average height of points within the pixel. The KAZE algorithm and enhanced matching approach are used to obtain the homonymous points of two images, subsequently determining the transformation relationship between them. Experimental results with actual data demonstrate that, for architectural elements within urban scenes, the relative angular differences of registered facades are below 0.5°. As for ground elements, the Root Mean Square Error (RMSE) after registration is less than 1.5 m, thus validating the superiority of the proposed method. Full article

(This article belongs to the Special Issue 3D Reconstruction and Mobile Mapping in Urban Environments Using Remote Sensing)

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23 pages, 12227 KiB

Open AccessEditor’s ChoiceArticle

3D Reconstruction of Ancient Buildings Using UAV Images and Neural Radiation Field with Depth Supervision

by Yingwei Ge, Bingxuan Guo, Peishuai Zha, San Jiang, Ziyu Jiang and Demin Li

Remote Sens. 2024, 16(3), 473; https://doi.org/10.3390/rs16030473 - 25 Jan 2024

Cited by 1 | Viewed by 1387

Abstract

The 3D reconstruction of ancient buildings through inclined photogrammetry finds a wide range of applications in surveying, visualization and heritage conservation. Unlike indoor objects, reconstructing ancient buildings presents unique challenges, including the slow speed of 3D reconstruction using traditional methods, the complex textures of ancient structures and geometric issues caused by repeated textures. Additionally, there is a hash conflict problem when rendering outdoor scenes using neural radiation fields. To address these challenges, this paper proposes a 3D reconstruction method based on depth-supervised neural radiation fields. To enhance the representation of the geometric neural network, the addition of a truncated signed distance function (TSDF) supplements the existing signed distance function (SDF). Furthermore, the neural network’s training is supervised using depth information, leading to improved geometric accuracy in the reconstruction model through depth data obtained from sparse point clouds. This study also introduces a progressive training strategy to mitigate hash conflicts, allowing the hash table to express important details more effectively while reducing feature overlap. The experimental results demonstrate that our method, under the same number of iterations, produces images with clearer structural details, resulting in an average 15% increase in the Peak Signal-to-Noise Ratio (PSNR) value and a 10% increase in the Structural Similarity Index Measure (SSIM) value. Moreover, our reconstruction model produces higher-quality surface models, enabling the fast and highly geometrically accurate 3D reconstruction of ancient buildings. Full article

(This article belongs to the Special Issue 3D Reconstruction and Mobile Mapping in Urban Environments Using Remote Sensing)

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18 pages, 4654 KiB

Open AccessArticle

On-Site Stability Assessment of Rubble Mound Breakwaters Using Unmanned Aerial Vehicle-Based Photogrammetry and Random Sample Consensus

by Marcos Arza-García, José Alberto Gonçalves, Vladimiro Ferreira Pinto and Guillermo Bastos

Remote Sens. 2024, 16(2), 331; https://doi.org/10.3390/rs16020331 - 14 Jan 2024

Cited by 1 | Viewed by 852

Abstract

Traditional methods for assessing the stability of rubble mound breakwaters (RMBs) often rely on 2.5D data, which may fall short in capturing intricate changes in the armor units, such as tilting and lateral shifts. Achieving a detailed analysis of RMB geometry typically requires fully 3D methods, but these often hinge on expensive acquisition technologies like terrestrial laser scanning (TLS) or airborne light detection and ranging (LiDAR). This article introduces an innovative approach to evaluate the structural stability of RMBs by integrating UAV-based photogrammetry and the random sample consensus (RANSAC) algorithm. The RANSAC algorithm proves to be an efficient and scalable tool for extracting primitives from point clouds (PCs), effectively addressing challenges presented by outliers and data noise in photogrammetric PCs. Photogrammetric PCs of the RMB, generated using Structure-from-Motion and MultiView Stereo (SfM-MVS) from both pre- and post-storm flights, were subjected to the RANSAC algorithm for plane extraction and segmentation. Subsequently, a spatial proximity criterion was employed to match cuboids between the two time periods. The methodology was validated on the detached breakwater of Cabedelo do Douro in Porto, Portugal, with a specific focus on potential rotations or tilting of Antifer cubes within the protective layer. The results, assessing the effects of the Leslie storm in 2018, demonstrate the potential of our approach in identifying and quantifying structural changes in RMBs. Full article

(This article belongs to the Special Issue 3D Reconstruction and Mobile Mapping in Urban Environments Using Remote Sensing)

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21 pages, 75822 KiB

Open AccessArticle

Reliable Feature Matching for Spherical Images via Local Geometric Rectification and Learned Descriptor

by San Jiang, Junhuan Liu, Yaxin Li, Duojie Weng and Wu Chen

Remote Sens. 2023, 15(20), 4954; https://doi.org/10.3390/rs15204954 - 13 Oct 2023

Cited by 1 | Viewed by 1075

Abstract

Spherical images have the advantage of recording full scenes using only one camera exposure and have been becoming an important data source for 3D reconstruction. However, geometric distortions inevitably exist due to the spherical camera imaging model. Thus, this study proposes a reliable feature matching algorithm for spherical images via the combination of local geometric rectification and CNN (convolutional neural network) learned descriptor. First, image patches around keypoints are reprojected to their corresponding tangent planes based on a spherical camera imaging model, which uses scale and orientation data from the keypoints to achieve both rotation and scale invariance. Second, feature descriptors are then calculated from the rectified image patches by using a pre-trained separate detector and descriptor learning network, which improves the discriminability by exploiting the high representation learning ability of the CNN. Finally, after classical feature matching with the ratio test and cross check, refined matches are obtained based on an essential matrix-based epipolar geometry constraint for outlier removal. By using three real spherical images and an incremental structure from motion (SfM) engine, the proposed algorithm is verified and compared in terms of feature matching and image orientation. The experiment results demonstrate that the geometric distortions can be efficiently reduced from rectified image patches, and the increased ratio of the match numbers ranges from 26.8% to 73.9%. For SfM-based spherical image orientation, the proposed algorithm provides reliable feature matches to achieve complete reconstruction with comparative accuracy. Full article

(This article belongs to the Special Issue 3D Reconstruction and Mobile Mapping in Urban Environments Using Remote Sensing)

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Other

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18 pages, 20257 KiB

Open AccessTechnical Note

Fast Digital Orthophoto Generation: A Comparative Study of Explicit and Implicit Methods

by Jianlin Lv, Guang Jiang, Wei Ding and Zhihao Zhao

Remote Sens. 2024, 16(5), 786; https://doi.org/10.3390/rs16050786 - 24 Feb 2024

Viewed by 552

Abstract

A digital orthophoto is an image with geometric accuracy and no distortion. It is acquired through a top view of the scene and finds widespread applications in map creation, planning, and related fields. This paper classifies the algorithms for digital orthophoto generation into two groups: explicit methods and implicit methods. Explicit methods rely on traditional geometric methods, obtaining geometric structure presented with explicit parameters with Multi-View Stereo (MVS) theories, as seen in our proposed Top view constrained Dense Matching (TDM). Implicit methods rely on neural rendering, obtaining implicit neural representation of scenes through the training of neural networks, as exemplified by Neural Radiance Fields (NeRFs). Both of them obtain digital orthophotos via rendering from a top-view perspective. In addition, this paper conducts an in-depth comparative study between explicit and implicit methods. The experiments demonstrate that both algorithms meet the measurement accuracy requirements and exhibit a similar level of quality in terms of generated results. Importantly, the explicit method shows a significant advantage in terms of efficiency, with a time consumption reduction of two orders of magnitude under our latest Compute Unified Device Architecture (CUDA) version TDM algorithm. Although explicit and implicit methods differ significantly in their representation forms, they share commonalities in the implementation across algorithmic stages. These findings highlight the potential advantages of explicit methods in orthophoto generation while also providing beneficial references and practical guidance for fast digital orthophoto generation using implicit methods. Full article

(This article belongs to the Special Issue 3D Reconstruction and Mobile Mapping in Urban Environments Using Remote Sensing)

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