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Remote Sensing
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Problem-Driven Geospatial Data Acquisition, Tools and Applications in Earth Observation
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Problem-Driven Geospatial Data Acquisition, Tools and Applications in Earth Observation

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: 15 October 2025 | Viewed by 2274

Special Issue Editors

Dr. Artan Hysa

E-Mail Website
Guest Editor
TUM School of Life Sciences, Technische Universität München, Munich, Germany
Interests: geographic information system; spatial analysis; data mining; geoinformation; landscape analysis; urban planning
Special Issues, Collections and Topics in MDPI journals
Dr. Pai-Hui Hsu

E-Mail Website
Guest Editor
Department of Civil Engineering, National Taiwan University, Taipei, Taiwan
Interests: image processing; geomatics;machine learning; geographic information systems; spatial analysis; data mining; geoinformation; geospatial science; earth observation; surveying
Dr. Yongze Song

E-Mail Website
Guest Editor
School of Design and the Built Environment, Curtin University, Kent Street, Bentley, WA 6102, Australia
Interests: sustainable development; spatial statistics; geospatial methods; urban remote sensing; sustainable infrastructure
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

“Problem-Driven Geospatial Data Acquisition, Tools and Applications in Earth Observation” presents an extensive and comprehensive compilation of recent advancements in Earth observation. Earth observation, which interfaces with space, remote sensing, communication, and information technologies, assumes an increasingly crucial role in scientific studies related to Earth, resource management, topographic mapping, and the promotion of a sustainable environment and community. This special edition explores the progress in geospatial data acquisition, tools, and applications within Earth observation, aiming to highlight the transformative influence of contemporary technologies on our comprehension and stewardship of Earth's resources and environment. Geospatial techniques serve as effective tools for mapping, monitoring, and assessing spatial information pertinent to Earth observation. This aids scientists, policymakers, and stakeholders in enhancing decision-making processes concerning better management, planning, and design initiatives to effectively combat against pressing global environmental problems. Consequently, the adoption of novel techniques and optimal practices in data acquisition, processing, and modeling enables the identification of the most effective solutions for applications in Earth observation. Through a diverse array of contributions from prominent experts and researchers, this Special Issue provides insights into the most recent methodologies, innovations, and practical implementations that are shaping the trajectory of Earth observation and geospatial science.

This Special Issue invites submissions focusing on inventive methodologies and novel scientific contributions concerning concepts, technologies, methodologies, and tools utilized in acquiring and processing geospatial data for diverse Earth observation applications. Topics of interest may include but not limited to the following:

  • Remote sensing platforms and various sensors such as unmanned aerial vehicles (UAVs), LiDAR, hyperspectral and multispectral imaging alongside traditional surveying methods.
  • New geospatial software and tools emerging for data processing and analysis.
  • Contributions of artificial intelligence and machine learning in geospatial analysis as well as cloud computing and big data analytics for Earth observation.
  • Integration of multisource data for environmental monitoring and assessment efforts.
  • Innovative applications in fields such as agriculture, forestry, urban planning, disaster management, climate change, and environmental monitoring.

Dr. Artan Hysa
Dr. Pai-Hui Hsu
Dr. Yongze Song
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

  • geospatial technology
  • geospatial data acquisition
  • geographic information system (GIS)
  • spatial analysis
  • earth observation
  • geospatial AI
  • big data analysis

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (2 papers)

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Research

35 pages, 29220 KiB  
Article
Towards High-Resolution Population Mapping: Leveraging Open Data, Remote Sensing, and AI for Geospatial Analysis in Developing Country Cities—A Case Study of Bangkok
by Kittisak Maneepong, Ryota Yamanotera, Yuki Akiyama, Hiroyuki Miyazaki, Satoshi Miyazawa and Chiaki Mizutani Akiyama
Remote Sens. 2025, 17(7), 1204; https://doi.org/10.3390/rs17071204 - 28 Mar 2025
Viewed by 663
Abstract
This study develops a globally adaptable and scalable methodology for high-resolution, building-level population mapping, integrating Earth observation techniques, geospatial data acquisition, and machine learning to enhance population estimation in rapidly urbanizing cities, particularly in developing countries. Using Bangkok, Thailand, as a case study, [...] Read more.
This study develops a globally adaptable and scalable methodology for high-resolution, building-level population mapping, integrating Earth observation techniques, geospatial data acquisition, and machine learning to enhance population estimation in rapidly urbanizing cities, particularly in developing countries. Using Bangkok, Thailand, as a case study, this research presents a problem-driven approach that leverages open geospatial data, including Overture Maps and OpenStreetMap (OSM), alongside Digital Elevation Models, to overcome limitations in data availability, granularity, and quality. This study integrates morphological terrain analysis and machine learning-based classification models to estimate building ancillary attributes such as footprint, height, and usage, applying micro-dasymetric mapping techniques to refine population distribution estimates. The findings reveal a notable degree of accuracy within residential zones, whereas performance in commercial and cultural areas indicates room for improvement. Challenges identified in mixed-use and townhouse building types are attributed to issues of misclassification and constraints in input data. The research underscores the importance of geospatial AI and remote sensing in resolving urban data scarcity challenges. By addressing critical gaps in geospatial data acquisition and processing, this study provides scalable, cost-effective solutions in the integration of multi-source remote sensing data and machine learning that contribute to sustainable urban development, disaster resilience, and resource planning. The findings reinforce the transformative role of open-access geospatial data in Earth observation applications, supporting real-time decision-making and enhanced urban resilience strategies in rapidly evolving environments. Full article
(This article belongs to the Special Issue Problem-Driven Geospatial Data Acquisition, Tools and Applications in Earth Observation)
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Graphical abstract

18 pages, 1623 KiB  
Article
Enhanced Stochastic Models for VLBI Invariant Point Estimation and Axis Offset Analysis
by Chang-Ki Hong and Tae-Suk Bae
Remote Sens. 2025, 17(1), 43; https://doi.org/10.3390/rs17010043 - 26 Dec 2024
Viewed by 592
Abstract
The accuracy and stability of Very Long Baseline Interferometry (VLBI) systems are essential for maintaining global geodetic reference frames such as the International Terrestrial Reference Frame (ITRF). This study focuses on the precise determination of the VLBI Invariant Point (IVP) and the detection [...] Read more.
The accuracy and stability of Very Long Baseline Interferometry (VLBI) systems are essential for maintaining global geodetic reference frames such as the International Terrestrial Reference Frame (ITRF). This study focuses on the precise determination of the VLBI Invariant Point (IVP) and the detection of antenna axis offset. Ground-based surveys were conducted at the Sejong Space Geodetic Observatory using high-precision instruments, including total station, to measure slant distances, as well as horizontal and vertical angles from fixed pillars to reflectors attached to the VLBI instrument. The reflectors comprised both prisms and reflective sheets to enhance redundancy and data reliability. A detailed stochastic model incorporating variance component estimation was employed to manage the varying precision of the observations. The analysis revealed significant measurement variability, particularly in slant distance measurements involving prisms. Iterative refinement of the variance components improved the reliability of the IVP and antenna axis offset estimates. The study identified an antenna axis offset of 5.6 mm, which was statistically validated through hypothesis testing, confirming its significance at a 0.01 significance level. This is a significance level corresponding to approximately a 2.576 sigma threshold, which represents a 99% confidence level. This study highlights the importance of accurate stochastic modeling in ensuring the precision and reliability of the estimated VLBI IVP and antenna axis offset. Additionally, the results can serve as a priori information for VLBI data analysis. Full article
(This article belongs to the Special Issue Problem-Driven Geospatial Data Acquisition, Tools and Applications in Earth Observation)
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