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Application of Spatial Information Science and Cartography in the Big Remotely Sensed Data Era

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

Deadline for manuscript submissions: 31 August 2025 | Viewed by 4153

Special Issue Editors

Prof. Dr. Xi Chen

E-Mail Website
Guest Editor
The Key Laboratory of Geographic Information Science (Ministry of Education), East China Normal University, Shanghai 200241, China
Interests: artificial intelligence; deep learning; big data analysis; applications of remote sensing images
Prof. Dr. Mingqiang Guo

E-Mail Website
Guest Editor
School of Computer Science, China University of Geosciences, Wuhan 430074, China
Interests: machine learning; image processing; 3D modelling; super segmented reconstruction of remote sensing images; high performance spatial computing; web-based GIS and its applications
Dr. Antonios Emmanouil Chatzipavlis

E-Mail Website
Guest Editor
Department of Physics and Earth Science, University of Ferrara, 44122 Ferrara, Italy
Interests: machine learning methods and their applications of the nearshore hydro-morphodynamic and sediment dynamic processes
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The era of big remotely sensed data has transformed our understanding of the Earth's surface. Integrating spatial information science and cartography is crucial for leveraging the vast data generated by remote sensing technologies. Spatial information science involves techniques for collecting, analyzing, and interpreting spatial data, while cartography focuses on designing and creating visual representations. Together, they enable precise analyses of environmental, social, and economic phenomena. This Special Issue explores advancements in spatial information science and cartography, emphasizing their importance in urban planning, disaster management, environmental monitoring, and resource management.

This Special Issue aims to provide a platform for researchers, practitioners, and policymakers to share innovative approaches and case studies on applying spatial information science and cartography in the big remotely sensed data era. By gathering diverse perspectives, this Special Issue seeks to enhance spatial data utilization for better decision-making and problem-solving processes. This subject aligns with Remote Sensing’s scope as we aim to promote interdisciplinary research and the development of new tools and techniques for accurate, efficient spatial analyses and cartographic representations in large-scale data contexts.

We invite submissions focused on the following topics:

  • Innovative methods for processing and analyzing big remotely sensed data;
  • Advances in cartographic visualization techniques;
  • Integration of spatial information science and machine learning;
  • Applications in urban planning and smart cities;
  • Disaster management and emergency response;
  • Environmental monitoring and assessment;
  • Resource management and sustainable development;
  • Case studies of practical applications;
  • Classification and retrieval for optical or multispectral remote sensing;
  • Geological hazards monitoring and early warning;
  • Multisource heterogeneous geosciences knowledge graph;
  • Large language models;
  • Remote sensing images and multimedia steganography;
  • Super-resolution remote sensing images.

We welcome the following types of papers:

  • Original research articles;
  • Review articles;
  • Case studies;
  • Technical notes on new tools, software, or datasets.

Contributors are welcome to present interdisciplinary research and collaborative efforts that showcase the transformative potential of spatial information science and cartography in the big remotely sensed data era.

Prof. Dr. Xi Chen
Prof. Dr. Mingqiang Guo
Dr. Antonios Emmanouil Chatzipavlis
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
  • artificial intelligence
  • deep learning
  • large language models
  • remote sensing images processing
  • knowledge graph

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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

27 pages, 49665 KiB  
Article
ETQ-Matcher: Efficient Quadtree-Attention-Guided Transformer for Detector-Free Aerial–Ground Image Matching
by Chuan Xu, Beikang Wang, Zhiwei Ye and Liye Mei
Remote Sens. 2025, 17(7), 1300; https://doi.org/10.3390/rs17071300 - 5 Apr 2025
Viewed by 268
Abstract
UAV aerial–ground feature matching is used for remote sensing applications, such as urban mapping, disaster management, and surveillance. However, current semi-dense detectors are sparse and inadequate for comprehensively addressing problems like scale variations from inherent viewpoint differences, occlusions, illumination changes, and repeated textures. [...] Read more.
UAV aerial–ground feature matching is used for remote sensing applications, such as urban mapping, disaster management, and surveillance. However, current semi-dense detectors are sparse and inadequate for comprehensively addressing problems like scale variations from inherent viewpoint differences, occlusions, illumination changes, and repeated textures. To address these issues, we propose an efficient quadtree-attention-guided transformer (ETQ-Matcher) based on efficient LoFTR, which integrates the multi-layer transformer with channel attention (MTCA) to capture global features. Specifically, to tackle various complex urban building scenarios, we propose quadtree-attention feature fusion (QAFF), which implements alternating self- and cross-attention operations to capture the context of global images and establish correlations between image pairs. We collect 12 pairs of UAV remote sensing images using drones and handheld devices, and we further utilize representative multi-source remote sensing images along with MegaDepth datasets to demonstrate their strong generalization ability. We compare ETQ-Matcher to classic algorithms, and our experimental results demonstrate its superior performance in challenging aerial–ground urban scenes and multi-source remote sensing scenarios. Full article
(This article belongs to the Special Issue Application of Spatial Information Science and Cartography in the Big Remotely Sensed Data Era)
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20 pages, 4952 KiB  
Article
Construction and Application of Feature Recommendation Model for Remote Sensing Interpretation of Rock Strata Based on Knowledge Graph
by Liufeng Tao, Qirui Wu, Miao Tian, Zhong Xie, Jianguo Chen, Yueyu Wu and Qinjun Qiu
Remote Sens. 2025, 17(6), 973; https://doi.org/10.3390/rs17060973 - 10 Mar 2025
Viewed by 534
Abstract
The enhancement of remote sensing interpretation accuracy for rock strata in complex terrain areas has long been limited by challenges in field validation and the insufficient integration of geological knowledge in traditional spectral–spatial feature selection methods. This study proposes a geological remote sensing [...] Read more.
The enhancement of remote sensing interpretation accuracy for rock strata in complex terrain areas has long been limited by challenges in field validation and the insufficient integration of geological knowledge in traditional spectral–spatial feature selection methods. This study proposes a geological remote sensing interpretation framework that integrates textual geological data, which enhances lithological identification accuracy by systematically combining multi-source geological knowledge with machine learning algorithms. Using a dataset of 2591 geological survey reports and scientific literature, a remote sensing interpretation ontology model was established, featuring four core entities (rock type, stratigraphic unit, spectral feature, and geomorphological indicator). A hybrid information extraction process combining rule-based parsing and a fine-tuned Universal Information Extraction (UIE) model was employed to extract knowledge from unstructured texts. A knowledge graph constructed using the TransE algorithm consists of 766 entity nodes and 1008 relationships, enabling a quantitative evaluation of feature correlations based on semantic similarity. When combined with Landsat multispectral data and digital elevation model (DEM)-derived terrain parameters, the knowledge-enhanced Random Forest (81.79%) and Support Vector Machine (75.76%) models demonstrated excellent performance in identifying rock-stratigraphic assemblages in the study area. While reducing subjective biases in manual interpretation, the method still has limitations. These include limited use of cross-modal data (e.g., geochemical tables, outcrop images) and a reliance on static knowledge representations. Future research will introduce dynamic graph updating mechanisms and multi-modal fusion architectures to improve adaptability across diverse geological lithological and structural environments. Full article
(This article belongs to the Special Issue Application of Spatial Information Science and Cartography in the Big Remotely Sensed Data Era)
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22 pages, 14975 KiB  
Article
Estimating Water Depth of Different Waterbodies Using Deep Learning Super Resolution from HJ-2 Satellite Hyperspectral Images
by Shuangyin Zhang, Kailong Hu, Xinsheng Wang, Baocheng Zhao, Ming Liu, Changjun Gu, Jian Xu and Xuejun Cheng
Remote Sens. 2024, 16(23), 4607; https://doi.org/10.3390/rs16234607 - 8 Dec 2024
Viewed by 1297
Abstract
Hyperspectral remote sensing images offer a unique opportunity to quickly monitor water depth, but how to utilize the enriched spectral information and improve its spatial resolution remains a challenge. We proposed a water depth estimation framework to improve spatial resolution using deep learning [...] Read more.
Hyperspectral remote sensing images offer a unique opportunity to quickly monitor water depth, but how to utilize the enriched spectral information and improve its spatial resolution remains a challenge. We proposed a water depth estimation framework to improve spatial resolution using deep learning and four inversion methods and verified the effectiveness of different super resolution and inversion methods in three waterbodies based on HJ-2 hyperspectral images. Results indicated that it was feasible to use HJ-2 hyperspectral images with a higher spatial resolution via super resolution methods to estimate water depth. Deep learning improves the spatial resolution of hyperspectral images from 48 m to 24 m and shows less information loss with peak signal-to-noise ratio (PSNR), structural similarity (SSIM), and spectral angle mapper (SAM) values of approximately 37, 0.92, and 2.42, respectively. Among four inversion methods, the multilayer perceptron demonstrates superior performance for the water reservoir, achieving the mean absolute error (MAE) and the mean absolute percentage error (MAPE) of 1.292 m and 22.188%, respectively. For two rivers, the random forest model proves to be the best model, with an MAE of 0.750 m and an MAPE of 10.806%. The proposed method can be used for water depth estimation of different water bodies and can improve the spatial resolution of water depth mapping, providing refined technical support for water environment management and protection. Full article
(This article belongs to the Special Issue Application of Spatial Information Science and Cartography in the Big Remotely Sensed Data Era)
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20 pages, 5537 KiB  
Article
TTMGNet: Tree Topology Mamba-Guided Network Collaborative Hierarchical Incremental Aggregation for Change Detection
by Hongzhu Wang, Zhaoyi Ye, Chuan Xu, Liye Mei, Cheng Lei and Du Wang
Remote Sens. 2024, 16(21), 4068; https://doi.org/10.3390/rs16214068 - 31 Oct 2024
Cited by 2 | Viewed by 1151
Abstract
Change detection (CD) identifies surface changes by analyzing bi-temporal remote sensing (RS) images of the same region and is essential for effective urban planning, ensuring the optimal allocation of resources, and supporting disaster management efforts. However, deep-learning-based CD methods struggle with background noise [...] Read more.
Change detection (CD) identifies surface changes by analyzing bi-temporal remote sensing (RS) images of the same region and is essential for effective urban planning, ensuring the optimal allocation of resources, and supporting disaster management efforts. However, deep-learning-based CD methods struggle with background noise and pseudo-changes due to local receptive field limitations or computing resource constraints, which limits long-range dependency capture and feature integration, normally resulting in fragmented detections and high false positive rates. To address these challenges, we propose a tree topology Mamba-guided network (TTMGNet) based on Mamba architecture, which combines the Mamba architecture for effectively capturing global features, a unique tree topology structure for retaining fine local details, and a hierarchical feature fusion mechanism that enhances multi-scale feature integration and robustness against noise. Specifically, the a Tree Topology Mamba Feature Extractor (TTMFE) leverages the similarity of pixels to generate minimum spanning tree (MST) topology sequences, guiding information aggregation and transmission. This approach utilizes a Tree Topology State Space Model (TTSSM) to embed spatial and positional information while preserving the global feature extraction capability, thereby retaining local features. Subsequently, the Hierarchical Incremental Aggregation Module is utilized to gradually align and merge features from deep to shallow layers to facilitate hierarchical feature integration. Through residual connections and cross-channel attention (CCA), HIAM enhances the interaction between neighboring feature maps, ensuring that critical features are retained and effectively utilized during the fusion process, thereby enabling more accurate detection results in CD. The proposed TTMGNet achieved F1 scores of 92.31% on LEVIR-CD, 90.94% on WHU-CD, and 77.25% on CL-CD, outperforming current mainstream methods in suppressing the impact of background noise and pseudo-change and more accurately identifying change regions. Full article
(This article belongs to the Special Issue Application of Spatial Information Science and Cartography in the Big Remotely Sensed Data Era)
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