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Remote Sensing
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Remote Sensing and Geospatial Analysis in the Big Data Era
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Remote Sensing and Geospatial Analysis in the Big Data Era

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

Deadline for manuscript submissions: 31 July 2025 | Viewed by 6241

Special Issue Editors

Dr. Robert Tenzer

E-Mail Website
Guest Editor
Department of Land Surveying and Geo-Informatics, The Hong Kong Polytechnic University, Hong Kong, China
Interests: physical geodesy; satellite geodesy; geophysics; geodynamics; climate change; gravimetry
Special Issues, Collections and Topics in MDPI journals
Dr. Hok Sum Fok

E-Mail Website
Guest Editor
School of Geodesy and Geomatics, Wuhan University, Wuhan 430079, China
Interests: topography; satellite; remote sensing; satellite geodesy; sea ice; geophysics; spatial analysis; geomatics; radar
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Remote sensing techniques revolutionized our ability to observe and understand the Earth’s surface and processes. With advancements in technology, remote sensing has become a crucial tool for collecting enormous amounts of geospatial data. In the era of Big Data, the availability of massive datasets presents both challenges and opportunities for geospatial analysis and their practical applications. By employing advanced algorithms, statistical techniques, and spatial modeling, geospatial analysis provides valuable information about complex spatial patterns, relationships, and processes. It enables the identification of land cover changes, monitoring of environmental conditions, assessment of natural hazards, and planning of urban infrastructure, among many other applications. This allows researchers to address critical environmental and societal challenges.

The aim of this Special Issue is to explore the advancements, challenges, and opportunities in exploiting the potential of geospatial Big Data. The objective is to present research that addresses scientific and technical aspects of processing a large amount and variety of geospatial datasets collected by remote sensing (and other) sensors. This Special Issue aims to showcase innovative methodologies, algorithms, and applications that effectively handle and analyze geospatial data in order to better understand and tackle complex societal and environmental challenges. This special issue aligns closely with the scope of the Remote Sensing journal by focusing on aspects associated with the geospatial analysis of remote sensing data.

We invite articles focusing on the advancement and integration of techniques applied for the analysis and interpretation of geospatial data acquired by remote sensing sensors. This involves studies dealing with data processing strategies, data management, machine learning, data fusion, and their applications in environmental monitoring, urban planning, disaster response, and other applications. The space will be given also for studies that focus on a combination and fusion of geospatial data acquired by terrestrial and remote sensing sensors.

Dr. Robert Tenzer
Dr. Hok Sum Fok
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
  • geospatial analysis
  • Big Data
  • machine learning
  • data fusion and integration
  • spatial modeling
  • advanced algorithms

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Published Papers (3 papers)

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Research

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21 pages, 46055 KiB  
Article
The 3D Density Structure of the South China Sea Based on Wavelet Multi-Scale Analysis of Gravity Data and Its Tectonic Implications
by Chuang Xu, Shiquan Su, Haopeng Chen, Hangtao Yu, Jinbo Li, Feiyu Zhang, Juntao Liang and Xu Lin
Remote Sens. 2024, 16(19), 3675; https://doi.org/10.3390/rs16193675 - 1 Oct 2024
Viewed by 1037
Abstract
Due to its unique geographical location and complex geological evolution processes, the South China Sea has been a focus of extensive research. Previous studies on the density structure of the South China Sea mostly focused on 2D density structures, with relatively limited research [...] Read more.
Due to its unique geographical location and complex geological evolution processes, the South China Sea has been a focus of extensive research. Previous studies on the density structure of the South China Sea mostly focused on 2D density structures, with relatively limited research on 3D density structures. A comprehensive study is still needed to refine the expansion mechanism and tectonic evolution of the South China Sea. In this study, we utilized wavelet multi-scale analysis of gravity data to obtain a 3D density model of the South China Sea and discussed its tectonic evolution from the pattern of density anomalies. The inversion results show that (1) the expansion of the South China Sea caused the typical thin oceanic crust and parts of the continent–ocean transition zone may fracture due to the expansion; (2) the low-density anomaly in the upper mantle of Luzon Island may indicate partial melting or the upwelling of asthenosphere materials; and (3) the expansion of the South China Sea is influenced by multiple plate forces and uneven forces affect the distribution of high-density anomalies in the upper mantle. Full article
(This article belongs to the Special Issue Remote Sensing and Geospatial Analysis in the Big Data Era)
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Review

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30 pages, 443 KiB  
Review
Remote Sensing and Geospatial Analysis in the Big Data Era: A Survey
by Elias Dritsas and Maria Trigka
Remote Sens. 2025, 17(3), 550; https://doi.org/10.3390/rs17030550 - 6 Feb 2025
Cited by 1 | Viewed by 3332
Abstract
The present survey examines the role of big data analytics in advancing remote sensing and geospatial analysis. The increasing volume and complexity of geospatial data are driving the adoption of machine learning (ML) and artificial intelligence (AI) techniques, such as convolutional neural networks [...] Read more.
The present survey examines the role of big data analytics in advancing remote sensing and geospatial analysis. The increasing volume and complexity of geospatial data are driving the adoption of machine learning (ML) and artificial intelligence (AI) techniques, such as convolutional neural networks (CNNs) and long short-term memory (LSTM) networks, to extract meaningful insights from large, diverse datasets. These AI methods enhance the accuracy and efficiency of spatial and temporal data analysis, benefiting applications in environmental monitoring, urban planning, and disaster management. Despite these advancements, challenges related to computational efficiency, data integration, and model transparency remain. This paper also discusses emerging trends and highlights the potential of hybrid approaches, cloud computing, and edge processing in overcoming these challenges. The integration of AI with geospatial data is poised to significantly improve our ability to monitor and manage Earth systems, supporting more informed and sustainable decision-making. Full article
(This article belongs to the Special Issue Remote Sensing and Geospatial Analysis in the Big Data Era)
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Other

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15 pages, 5651 KiB  
Technical Note
The EL-BIOS Earth Observation Data Cube for Supporting Biodiversity Monitoring in Greece
by Vangelis Fotakidis, Themistoklis Roustanis, Konstantinos Panayiotou, Irene Chrysafis, Eleni Fitoka and Giorgos Mallinis
Remote Sens. 2024, 16(20), 3771; https://doi.org/10.3390/rs16203771 - 11 Oct 2024
Viewed by 1209
Abstract
In recent years, the need to protect and conserve biodiversity has become more critical than ever before, as a prerequisite for both sustainable development and the very survival of the human species. This has made it a priority for the scientific community to [...] Read more.
In recent years, the need to protect and conserve biodiversity has become more critical than ever before, as a prerequisite for both sustainable development and the very survival of the human species. This has made it a priority for the scientific community to develop technological solutions that provide data and information for monitoring, directly or indirectly, biodiversity and the drivers of change. A new era of satellite earth observation upgrades the potential of Remote Sensing (RS) to support, at relatively low cost, but with high accuracy the extraction of information over large areas, at regular intervals, and over extended periods of time. Also, the recent development of the Earth Observation Data Cubes (EODC) framework facilitates EO data management and information extraction, enabling the mapping and monitoring of temporal and spatial patterns on the Earth’s surface. This submission presents the ELBIOS EODC, specifically developed to support the biodiversity management and conservation over Greece. Based on the Open Data Cube (ODC) framework, it exploits multi-spectral optical Copernicus Sentinel-2 data and provides a series of Satellite Earth Observation (SEO) biodiversity products and spectral indices nationwide. Full article
(This article belongs to the Special Issue Remote Sensing and Geospatial Analysis in the Big Data Era)
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