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

Deadline for manuscript submissions: closed (31 March 2026) | Viewed by 20069

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Special Issue Editors

Dr. Fangning He

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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

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Guest Editor

Department of Geomatics Engineering, Schulich School of Engineering, University of Calgary, Calgary, AB T2N 1N4, Canada
Interests: AI for navigation; mapping; perception; computer vision; sensor fusion; end to end autonomous driving
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Dr. Shengjun Tang

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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

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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
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Prof. Dr. Ayman F. Habib

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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 250 words) can be sent to the Editorial Office for assessment.

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 (7 papers)

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Research

Jump to: Review

27 pages, 17739 KB

Open AccessArticle

3D Radiometric Thermography Mosaics with Low-Cost Mobile Sensor Stack

by Scott McAvoy, Jonathan Klingspon, Adrian Tong, Eric Lo, Nathan Hui, Maurizio Seracini, Dominique Rissolo, Neal Driscoll and Falko Kuester

Remote Sens. 2026, 18(9), 1335; https://doi.org/10.3390/rs18091335 - 27 Apr 2026

Viewed by 466

Abstract

Infrared thermography provides key information for a wide range of diagnostic applications within built and natural environments. As thermal states are changing with ambient conditions, it is important to deploy thermal imaging systems and operators opportunistically. It is therefore an attractive proposition to make these systems more affordable and accessible. Low-cost thermal sensors generally produce low-resolution outputs. To increase data density across large subjects, diagnosticians may create image mosaics from multiple overlapping thermographs. The registration of individual inputs into large mosaics is aided by the acquisition of additional sensor data (photographs and depthmaps), which can provide critical spatial references. In many cases, the materials inherent to the modern built environment present challenges to traditional data registration workflows between multiple sensor streams. Mobile devices offer an opportunity to innovate in the creation of these mosaics, integrating rapid geospatial mapping functionality with radiometric thermography within a 3D context. In this paper the authors evaluate the FLIR One Pro thermal camera module along with iOS/iPhone specific rapid mapping capabilities, and present a methodology: (1) introducing a workflow for the integration of short-range (within 0.3–5 m capture distance) iPhone mobile sensor data into modeling pipelines; (2) introducing a calibration model enabling effective registration and fusion of multi-modal inputs from the iPhone mobile sensor stack and FLIR One thermographic module; and (3) detailing an alternative open-source methodology for the evaluation and translation of thermographic imagery for multi-sensor fusion. The end product of this pipeline is a 3D radiometric thermographic mosaic: a spatially continuous, textured surface model in which hundreds of individual low-resolution thermographs are fused into a single queryable output retaining full 16-bit temperature values at every point. All datasets have been made openly available and the two case studies used in this paper have been made accessible at full resolution for interactive 3D online viewing. Full article

(This article belongs to the Special Issue Remote Sensing for 2D/3D Mapping)

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23 pages, 2407 KB

Open AccessArticle

Replicability of Digital Terrain Models and Canopy Height Models Derived from Drone Photogrammetry

by Jurjen Van der Sluijs, Robert H. Fraser and Trevor C. Lantz

Remote Sens. 2026, 18(4), 627; https://doi.org/10.3390/rs18040627 - 17 Feb 2026

Viewed by 971

Abstract

Replicability of Digital Terrain Models (DTMs) and Canopy Height Models (CHMs) derived from drone photogrammetry is important to understand the extent to which time-series are exposed to methodological noise and conceal real environmental changes. Root mean square error (RMSE) distribution metrics (median/IQR) were used as indicators of replicability across seven drone survey setups, three dense matching scales, and 13 ground point filters in a challenging shrubland environment (total of 273 DTMs and CHMs). We conclude that methodological effects have considerable potential to negatively affect replicability. A power-law relationship between point cloud density and dense matching resolution suggested that important dense matching resolution thresholds exist beyond which replicability degrades considerably. For our Arctic study area, replicability of DTMs (median ± 0.1 m RMSE Vegetated Vertical Accuracy) and CHMs (within ±0.05 m of true site-level heights) is most likely when source imagery is collected with ≤1.5 cm spatial resolution and side-lap of >80%, and if classified point clouds are generated using full-scale dense matching and Triangular Irregular Network filtering. Negative biases for maximum shrub height estimates increased from 4–9% to 14–50% with coarser imagery. We advocate for increased attention to drone-derived model replicability to separate real environmental changes from noise during a period of rapid ecological and geomorphic change. Full article

(This article belongs to the Special Issue Remote Sensing for 2D/3D Mapping)

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34 pages, 9553 KB

Open AccessArticle

Research on Multi-Stage Optimization for High-Precision Digital Surface Model and True Digital Orthophoto Map Generation Methods

by Yingwei Ge, Renke Ji, Bingxuan Guo, Qinsi Wang, Xiao Jiang and Mofei Chen

Remote Sens. 2026, 18(2), 197; https://doi.org/10.3390/rs18020197 - 7 Jan 2026

Viewed by 620

Abstract

To enhance the overall quality and consistency of depth maps, Digital Surface Models (DSM), and True Digital Orthophoto Map (TDOM) in UAV image reconstruction, this paper proposes a multi-stage adaptive optimization generation method. First, to address the noise and outlier issues in depth maps, an adaptive joint bilateral filtering-based optimization method is introduced. This method repairs anomalous depth values using a four-directional filling strategy, incorporates image-guided joint bilateral filtering to enhance edge structure representation, effectively improving the accuracy and continuity of the depth map. Next, during the DSM generation stage, a method based on depth value voting space and elevation anomaly detection is proposed. A joint mechanism of elevation calculation and anomaly point detection is used to suppress noise and errors, while a height value completion strategy significantly enhances the geometric accuracy and integrity of the DSM. Finally, in the TDOM generation process, occlusion detection and gap-line generation techniques are introduced. Together with uniform lighting, color adjustment, and image gap optimization strategies, this improves texture stitching continuity and brightness consistency, effectively reducing artifacts caused by gaps, blurriness, and lighting differences. Experimental results show that the proposed method significantly improves depth map smoothness, DSM geometric accuracy, and TDOM visual consistency compared to traditional methods, providing a complete and efficient technical pathway for high-quality surface reconstruction. Full article

(This article belongs to the Special Issue Remote Sensing for 2D/3D Mapping)

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28 pages, 8102 KB

Open AccessArticle

Multi-Neighborhood Sparse Feature Selection for Semantic Segmentation of LiDAR Point Clouds

by Rui Zhang, Guanlong Huang, Fengpu Bao and Xin Guo

Remote Sens. 2025, 17(13), 2288; https://doi.org/10.3390/rs17132288 - 3 Jul 2025

Cited by 3 | Viewed by 2068

Abstract

LiDAR point clouds, as direct carriers of 3D spatial information, comprehensively record the geometric features and spatial topological relationships of object surfaces, providing intelligent systems with rich 3D scene representation capability. However, current point cloud semantic segmentation methods primarily extract features through operations such as convolution and pooling, yet fail to adequately consider sparse features that significantly influence the final results of point cloud-based scene perception, resulting in insufficient feature representation capability. To address these problems, a sparse feature dynamic graph convolutional neural network, abbreviated as SFDGNet, is constructed in this paper for LiDAR point clouds of complex scenes. In the context of this paper, sparse features refer to feature representations in which only a small number of activation units or channels exhibit significant responses during the forward pass of the model. First, a sparse feature regularization method was used to motivate the network model to learn the sparsified feature weight matrix. Next, a split edge convolution module, abbreviated as SEConv, was designed to extract the local features of the point cloud from multiple neighborhoods by dividing the input feature channels, and to effectively learn sparse features to avoid feature redundancy. Finally, a multi-neighborhood feature fusion strategy was developed that combines the attention mechanism to fuse the local features of different neighborhoods and obtain global features with fine-grained information. Taking S3DIS and ScanNet v2 datasets, we evaluated the feasibility and effectiveness of SFDGNet by comparing it with six typical semantic segmentation models. Compared with the benchmark model DGCNN, SFDGNet improved overall accuracy

(O A)

, mean accuracy

(m A c c)

, mean intersection over union

(m I o U)

, and

s p a r s i t y

1.8 %

3.7 %, 3.5 %

, and

85.5 %

on the S3DIS dataset, respectively. The

m I o U

on the ScanNet v2 validation set,

m I o U

on the test set, and

s p a r s i t y

were improved by

3.2 %, 7.0 %

, and

54.5 %

, respectively. Full article

(This article belongs to the Special Issue Remote Sensing for 2D/3D Mapping)

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26 pages, 15055 KB

Open AccessArticle

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

Cited by 5 | Viewed by 6975

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 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 KB

Open AccessEditor’s ChoiceArticle

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

Cited by 9 | Viewed by 6158

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). 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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Review

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35 pages, 4212 KB

Open AccessReview

2D and 3D Urban Change Detection Methods Using Remote Sensing: A Review

by Masoomeh Gomroki, Amirreza Gomroki, Robert H. Gulden, Dilshan I. Benaragama, Mahdi Hasanlou, Nasem Badreldin, Bahareh Kalantar and Husam Al-Najjar

Remote Sens. 2026, 18(10), 1606; https://doi.org/10.3390/rs18101606 - 16 May 2026

Viewed by 433

Abstract

Change detection is a fundamental task in remote sensing with broad applications in urban monitoring, agriculture, watershed management, and land use and land cover analysis. In urban environments, accurate change detection is particularly critical for resource management, urban planning, and smart city development. Rapid urbanization has led to frequent and complex changes in buildings, which constitute key structural components of cities. Consequently, continuous and precise monitoring of building dynamics is essential for informed decision-making related to urban growth, environmental assessment, traffic management, and sustainable development. This paper presents a comprehensive review of two-dimensional (2D) and three-dimensional (3D) change detection methods applied to urban areas. Conventional and advanced approaches are systematically analyzed, and their strengths and limitations are critically discussed from a holistic perspective. Special emphasis is placed on recent learning-based techniques, which demonstrate enhanced robustness and accuracy in complex urban environments. Finally, current challenges and future research directions are identified to support the further development of effective 2D and 3D urban change detection methods. Full article

(This article belongs to the Special Issue Remote Sensing for 2D/3D Mapping)

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