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Geographic Information and Remote Sensing Technology (GIRST)
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Signal Processing for Telecommunications and Economics Laboratory, Economics Department, University of ROMA TRE, Via Silvio D'Amico 77, 00145 Rome, Italy
1. School of Computing and Engineering, University of West London, Room BY.03.19, St. Mary’s Rd., Ealing, London W5 5RF, UK
2. The Faringdon Centre for Non-Destructive Testing and Remote Sensing, University of West London, Room BY.GF.015, St. Mary’s Rd., Ealing, London W5 5RF, UK
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Geographic Information and Remote Sensing Technology (GIRST)

Abstract submission deadline
31 October 2025
Manuscript submission deadline
31 December 2025
Viewed by
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Topic Information

Dear Colleagues,

Remote sensing and geographic information systems (GISs) analyses involve the use of technology to gather, manipulate, and analyze spatial data to understand a range of phenomena. Remote sensing entails obtaining information about the Earth’s surface by examining the data acquired by a device which is at a distance from the surface, most often satellites orbiting the earth and airplanes. GISs are computer-based systems that are used to capture, store, analyze, and display geographic information. These two approaches are used widely, often together, to assess natural resources and monitor environmental changes.

The “Geographic Information and Remote Sensing Technology (GIRST)” Topic invites papers on theoretical and applied issues including, but not limited to, the following:

Geographic Information:

  • Geohazards and Earthquake Engineering;
  • Remote Sensing Interpretation of Geological Structure;
  • Detection and Information Technology;
  • Geographic Information Systems;
  • Global Navigation Satellite Systems;
  • Satellite Navigation and Positioning;
  • Surveying and Mapping;
  • Computer Graphics;
  • Sensor Technology.

Remote Sensing Technology:

  • Theories, Techniques and Methods related to Surveying, Mapping, Navigation, and Oblique Photography;
  • Remote Sensing;
  • Optical Remote Sensing;
  • Microwave Remote Sensing;
  • Geographic Information Science;
  • Remote Sensing Information Engineering;
  • Space Technology and Landscape;
  • Classification and Data Mining Techniques;
  • Image Processing Technology;
  • Hyperspectral Image Processing;
  • Remote Sensing Data Fusion.

This Topic will present the results of research describing the recent advances in both the remote sensing and geographic information systems fields. This Topic will collect extended versions of the best papers presented at the GIRST2024 (2024 3rd International Conference on Geographic Information and Remote Sensing Technology (GIRST 2024)), but it is also open and invites external submissions.

Dr. Francesco Benedetto
Prof. Dr. Fabio Tosti
Topic Editors

Keywords

  • remote sensing
  • geographic information systems
  • image processing
  • artificial intelligence for GIS and remote sensing
  • detection and information technology

Participating Journals

Journal Name Impact Factor CiteScore Launched Year First Decision (median) APC
Applied Sciences
applsci 		2.5 5.3 2011 18.4 Days CHF 2400 Submit
Geomatics
geomatics 		- - 2021 22.1 Days CHF 1000 Submit
NDT
ndt 		- - 2023 15.0 days * CHF 1000 Submit
Remote Sensing
remotesensing 		4.2 8.3 2009 23.9 Days CHF 2700 Submit

* Median value for all MDPI journals in the second half of 2024.


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Published Papers (2 papers)

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22 pages, 316 KiB  
Review
The Application of Earth Observation Data to Desert Locust Risk Management: A Literature Review
by Gachie Eliud Baraka, Guido D’Urso and Oscar Rosario Belfiore
Geomatics 2025, 5(1), 14; https://doi.org/10.3390/geomatics5010014 - 18 Mar 2025
Viewed by 445
Abstract
The desert locust is documented as one of the most destructive polyphagous plant pests that require preventive or proactive management practices due to its phase polyphenism, rapid breeding, transnational migration, and heavy feeding behaviour. Desert locust situation analysis, forecasting and early warning are [...] Read more.
The desert locust is documented as one of the most destructive polyphagous plant pests that require preventive or proactive management practices due to its phase polyphenism, rapid breeding, transnational migration, and heavy feeding behaviour. Desert locust situation analysis, forecasting and early warning are complex due to the systemic interaction of biological, meteorological, and geographical factors that play different roles in facilitating the survival, breeding and migration of the pest. This article seeks to elucidate the factors that affect desert locust distribution and review the application of earth observation (EO) data in explaining the pest’s infestations and impact. The review presents details concerning the application of EO data to understand factors that affect desert locust breeding and migration, elaborates on impact assessment through vegetation change detection and discusses modelling techniques that can support the effective management of the pest. The review reveals that the application of EO technology is inclined in favour of desert locust habitat suitability assessment with a limited financial quantification of losses. The review also finds a progressive advancement in the use of multi-modelling approaches to address identified gaps and reduce computational errors. Moreover, the review recognises great potential in applications of EO tools, products and services for anticipatory action against desert locusts to ensure resource use efficiency and environmental conservation. Full article
(This article belongs to the Topic Geographic Information and Remote Sensing Technology (GIRST))
16 pages, 12204 KiB  
Article
Examining Deep Learning Pixel-Based Classification Algorithms for Mapping Weed Canopy Cover in Wheat Production Using Drone Data
by Judith N. Oppong, Clement E. Akumu, Samuel Dennis and Stephanie Anyanwu
Geomatics 2025, 5(1), 4; https://doi.org/10.3390/geomatics5010004 - 10 Jan 2025
Viewed by 837
Abstract
Deep learning models offer valuable insights by leveraging large datasets, enabling precise and strategic decision-making essential for modern agriculture. Despite their potential, limited research has focused on the performance of pixel-based deep learning algorithms for detecting and mapping weed canopy cover. This study [...] Read more.
Deep learning models offer valuable insights by leveraging large datasets, enabling precise and strategic decision-making essential for modern agriculture. Despite their potential, limited research has focused on the performance of pixel-based deep learning algorithms for detecting and mapping weed canopy cover. This study aims to evaluate the effectiveness of three neural network architectures—U-Net, DeepLabV3 (DLV3), and pyramid scene parsing network (PSPNet)—in mapping weed canopy cover in winter wheat. Drone data collected at the jointing and booting growth stages of winter wheat were used for the analysis. A supervised deep learning pixel classification methodology was adopted, and the models were tested on broadleaved weed species, winter wheat, and other weed species. The results show that PSPNet outperformed both U-Net and DLV3 in classification performance, with PSPNet achieving the highest overall mapping accuracy of 80%, followed by U-Net at 75% and DLV3 at 56.5%. These findings highlight the potential of pixel-based deep learning algorithms to enhance weed canopy mapping, enabling farmers to make more informed, site-specific weed management decisions, ultimately improving production and promoting sustainable agricultural practices. Full article
(This article belongs to the Topic Geographic Information and Remote Sensing Technology (GIRST))
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