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Remote Sensing
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Remote Sensing for 2D/3D Mapping
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Remote Sensing for 2D/3D Mapping

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

Deadline for manuscript submissions: 30 September 2024 | Viewed by 3199

Special Issue Editors

Dr. Fangning He

E-Mail Website
Guest Editor
Nvidia, Redmond, WA 98052, USA
Interests: mapping; 3D reconstruction; computer vision; photogrammetry; unmanned aerial mapping system; LiDAR; point cloud processing
Dr. Hongzhou Yang

E-Mail Website
Guest Editor
Department of Geomatics Engineering, Schulich School of Engineering, University of Calgary, Calgary, AB T2N 1N4, Canada
Interests: mapping; navigation; state estimation optimization
Dr. Shengjun Tang

E-Mail Website
Guest Editor
Research Institute of Smart Cities, Shenzhen University, Shenzhen, China
Interests: 3D mapping; SLAM; computer vision
Special Issues, Collections and Topics in MDPI journals
Dr. Ding Ma

E-Mail Website
Guest Editor
Research Institute for Smart Cities, School of Architecture and Urban Planning, Shenzhen University, Shenzhen, China
Interests: spatio-temporal data mining; spatial modeling; big data; complex networks; fractal geometry
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Ayman F. Habib

E-Mail Website
Guest Editor
Lyles School of Civil Engineering, Purdue University, West Lafayette, IN 47907, USA
Interests: photogrammetry; laser scanning; mobile mapping systems; system calibration; computer vision; unmanned aerial mapping systems; multisensor/multiplatform data integration
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Remote sensing has long served as a cornerstone for monitoring, studying, and understanding our dynamic planet in disciplines such as geophysics, optical physics, and geospatial science. Leveraging cutting-edge techniques like LiDAR and photogrammetry, we can now generate detailed 2D/3D maps for supporting diverse applications, from environmental charting to powering digital twin and smart city initiatives. Beyond traditional mapping, these methods also play a critical role in addressing global challenges such as climate change, rapid urbanization, and environmental preservation. Recent advancements have seen the integration of remote sensing with advanced machine and deep learning algorithms, including convolutional neural networks, transformers, and Neural Radiance Fields (NeRF). These innovations have significantly enhanced our capacity to interpret remote sensing data. As the field of remote sensing continues to evolve, we anticipate advancements in both data processing and sensor technologies. These developments will further push the boundaries of remote sensing for achieving more precise and comprehensive 2D/3D mapping techniques.

This Special Issue aims to explore the latest research advancements in remote sensing for 2D/3D mapping. We emphasize both the methodological and algorithmic innovations as well as the broad application of remote sensing-based 2D/3D mapping techniques. This covers a spectrum of applications such as transportation planning, environmental modeling and analysis, property management, cultural heritage preservation, urban planning and design, and precision agriculture.

This Special Issue welcomes contributions centered on data acquisition, processing, analysis, and interpretation within the domain of 2D/3D mapping via remote sensing. We invite both original research articles and comprehensive reviews, covering areas such as, but not limited to:

  • Advances in mapping methodologies and algorithmic techniques, like LiDAR scanning, photogrammetry reconstruction, point cloud processing, and Neural Radiance Field;
  • Examination of remote sensing data sourced from different sensors, such as satellite and airborne imagery, as well as LiDAR and Radar point clouds;
  • Exploration of emerging mapping platforms, like UAVs and mobile mapping systems;
  • Incorporation of machine learning and deep learning methodologies into remote sensing mapping;
  • Application of remote sensing-driven mapping in applications such as digital twins, transportation planning, environmental modeling, precision farming, etc.;
  • Application of night-time lights for mapping urban structure and dynamics.

Dr. Fangning He
Dr. Hongzhou Yang
Dr. Shengjun Tang
Dr. Ding Ma
Prof. Dr. Ayman F. Habib
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

  • remote sensing
  • mapping
  • 3D reconstruction
  • deep learning
  • geoscience
  • GIS
  • satellite imagery
  • UAV
  • photogrammetry
  • point cloud processing
  • neural radiance field

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Published Papers (2 papers)

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Research

26 pages, 15055 KiB  
Article
Building Better Models: Benchmarking Feature Extraction and Matching for Structure from Motion at Construction Sites
by Carlos Roberto Cueto Zumaya, Iacopo Catalano and Jorge Peña Queralta
Remote Sens. 2024, 16(16), 2974; https://doi.org/10.3390/rs16162974 - 14 Aug 2024
Viewed by 554
Abstract
The popularity of Structure from Motion (SfM) techniques has significantly advanced 3D reconstruction in various domains, including construction site mapping. Central to SfM, is the feature extraction and matching process, which identifies and correlates keypoints across images. Previous benchmarks have assessed traditional and [...] Read more.
The popularity of Structure from Motion (SfM) techniques has significantly advanced 3D reconstruction in various domains, including construction site mapping. Central to SfM, is the feature extraction and matching process, which identifies and correlates keypoints across images. Previous benchmarks have assessed traditional and learning-based methods for these tasks but have not specifically focused on construction sites, often evaluating isolated components of the SfM pipeline. This study provides a comprehensive evaluation of traditional methods (e.g., SIFT, AKAZE, ORB) and learning-based methods (e.g., D2-Net, DISK, R2D2, SuperPoint, SOSNet) within the SfM pipeline for construction site mapping. It also compares matching techniques, including SuperGlue and LightGlue, against traditional approaches such as nearest neighbor. Our findings demonstrate that deep learning-based methods such as DISK with LightGlue and SuperPoint with various matchers consistently outperform traditional methods like SIFT in both reconstruction quality and computational efficiency. Overall, the deep learning methods exhibited better adaptability to complex construction environments, leveraging modern hardware effectively, highlighting their potential for large-scale and real-time applications in construction site mapping. This benchmark aims to assist researchers in selecting the optimal combination of feature extraction and matching methods for SfM applications at construction sites. Full article
(This article belongs to the Special Issue Remote Sensing for 2D/3D Mapping)
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22 pages, 4906 KiB  
Article
Using Remote Sensing Multispectral Imagery for Invasive Species Quantification: The Effect of Image Resolution on Area and Biomass Estimation
by Manuel de Figueiredo Meyer, José Alberto Gonçalves and Ana Maria Ferreira Bio
Remote Sens. 2024, 16(4), 652; https://doi.org/10.3390/rs16040652 - 9 Feb 2024
Viewed by 1657
Abstract
This study assesses the applicability of different-resolution multispectral remote sensing images for mapping and estimating the aboveground biomass (AGB) of Carpobrotus edulis, a prominent invasive species in European coastal areas. This study was carried out on the Cávado estuary sand spit (Portugal). [...] Read more.
This study assesses the applicability of different-resolution multispectral remote sensing images for mapping and estimating the aboveground biomass (AGB) of Carpobrotus edulis, a prominent invasive species in European coastal areas. This study was carried out on the Cávado estuary sand spit (Portugal). The performance of three sets of multispectral images with different Ground Sample Distances (GSDs) were compared: 2.5 cm, 5 cm, and 10 cm. The images were classified using the supervised classification algorithm random forest and later improved by applying a sieve filter. Samples of C. edulis were also collected, dried, and weighed to estimate the AGB using the relationship between the dry weight (DW) and vegetation indices (VIs). The resulting regression models were evaluated based on their coefficient of determination (R2), Normalised Root Mean Square Error (NRMSE), p-value, Akaike information criterion (AIC), and the Bayesian information criterion (BIC). The results show that the three tested image resolutions allow for constructing reliable coverage maps of C. edulis, with overall accuracy values of 89%, 85%, and 88% for the classification of the 2.5 cm, 5 cm, and 10 cm GSD images, respectively. The best-performing VI-DW regression models achieved R2 = 0.87 and NRMSE = 0.09 for the 2.5 cm resolution; R2 = 0.77 and NRMSE = 0.12 for the 5 cm resolution; and R2 = 0.64 and NRMSE = 0.15 for the 10 cm resolution. The C. edulis area and total AGB were 3441.10 m2 and 28,327.1 kg (with an AGB relative error (RE) = 0.08) for the 2.5 cm resolution; 3070.04 m2 and 29,170.8 kg (AGB RE = 0.08) for the 5 cm resolution; and 2305.06 m2 and 22,135.7 kg (AGB RE = 0.11) for the 10 cm resolution. Spatial and model differences were analysed in detail to determine their causes. Final analyses suggest that multispectral imagery of up to 5 cm GSD is adequate for estimating C. edulis distribution and biomass. Full article
(This article belongs to the Special Issue Remote Sensing for 2D/3D Mapping)
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