Geographic Information and Remote Sensing Technology (GIRST)

Abstract submission deadline

31 October 2026

Manuscript submission deadline

31 December 2026

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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

Participating Journals

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
Applied Sciences applsci	2.5	5.5	2011	16 Days	CHF 2400	Submit
Geomatics geomatics	2.8	5.1	2021	22.6 Days	CHF 1200	Submit
NDT ndt	-	-	2023	27.8 Days	CHF 1000	Submit
Remote Sensing remotesensing	4.1	8.6	2009	24.3 Days	CHF 2700	Submit

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Published Papers (10 papers)

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All Applied Sciences Geomatics NDT Remote Sensing

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21 pages, 6364 KB  

Open AccessArticle

Time Series Analysis of GNSS, InSAR, and Robotic Total Station Measurements for Monitoring Vertical Displacements of the Dniester HPP Dam (Ukraine)

by Kornyliy Tretyak and Denys Kukhtar

Geomatics 2025, 5(4), 73; https://doi.org/10.3390/geomatics5040073 - 2 Dec 2025

Cited by 1 | Viewed by 765

Abstract

Classical instrumental technologies still remain important among the geodetic methods of dam monitoring, but periodic observations are often insufficient for timely detection of hazardous deformations. Therefore, the integration of continuous and remote sensing technologies into a multi-level system of observation improves the assessment [...] Read more.

Classical instrumental technologies still remain important among the geodetic methods of dam monitoring, but periodic observations are often insufficient for timely detection of hazardous deformations. Therefore, the integration of continuous and remote sensing technologies into a multi-level system of observation improves the assessment of a structural condition. This research work evaluates the integrated approach that combines the GNSS data, robotic total station measurements, and satellite radar data processed by the PSInSAR technique for detecting the cyclic thermal deformations of the Dniester HPP concrete dam. The dataset includes 185 ascending and 184 descending Sentinel-1A SAR images (2019–2025, 12-day repeat cycle). PSInSAR processing was performed using StaMPS, with validation through comparison of InSAR-derived vertical displacements and GNSS data from the stationary monitoring system of the dam. The GNSS and InSAR time series have revealed consistent seasonal patterns and a common long-term trend. Harmonic components with amplitudes of 4–5 mm, peaking in late summer and declining in winter, confirm the dominant influence of thermal processes. In order to reduce noise, Fourier-based filtering and approximation were applied, thus ensuring balance between accuracy and data retention. The combined use of GNSS, robotic total station, and InSAR has increased the density of reliable control points and improved the thermal deformation model. Maximum vertical displacements of 6–13 mm were observed on the horizontal sections most exposed to solar radiation. Full article

(This article belongs to the Topic Geographic Information and Remote Sensing Technology (GIRST))

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21 pages, 9476 KB  

Open AccessArticle

The Impact on Triple/N-Way Collocation-Based Validation of Remote Sensing Products Due to Non-Ideal Error Statistics

by Rajeswari Balasubramaniam and Christopher Ruf

Remote Sens. 2025, 17(22), 3751; https://doi.org/10.3390/rs17223751 - 18 Nov 2025

Viewed by 447

Abstract

Triple/N-way collocation is a statistical analysis tool used to estimate the individual error variances of simultaneous observations of a physical quantity by three or more distinct systems. The tool is widely used to validate remote sensing data products such as ocean surface winds [...] Read more.

Triple/N-way collocation is a statistical analysis tool used to estimate the individual error variances of simultaneous observations of a physical quantity by three or more distinct systems. The tool is widely used to validate remote sensing data products such as ocean surface winds and soil moisture retrieved by satellite sensors, where simultaneous observations by different systems are common. However, the method relies on several assumptions about the statistical properties of the observations that are not always valid in a real-world scenario. We test the validity of these assumptions using a numerical simulator and assess their impact on error variance estimates. Some of these assumptions, that the errors are uncorrelated between observing systems or the reference system having a non-unity scaling factor, etc., are found to have a large impact on estimates of error variance when violated. The violation of some other assumptions is found to be less impactful. The simulator also provides corrections to the erroneous estimates of error variances that result when the underlying assumptions are violated. Additionally, we present a new, more general version of the collocation analysis tool that accommodates cases where the error variance in an observing system has a dependence on the true signal. Full article

(This article belongs to the Topic Geographic Information and Remote Sensing Technology (GIRST))

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31 pages, 30941 KB  

Open AccessArticle

Geospatial Scenario Modeling with Cellular Automata: Land Use and Cover Change in Southern Maranhão, Brazilian Savanna (2020–2030)

by Paulo Roberto Mendes Pereira, Édson Luis Bolfe, Francisco Wendell Dias Costa, Taíssa Caroline Silva Rodrigues, Marcelino Silva Farias Filho and Eduarda Vaz Braga

Geomatics 2025, 5(4), 65; https://doi.org/10.3390/geomatics5040065 - 17 Nov 2025

Viewed by 1039

Abstract

Land use and land cover (LULC) changes driven by agricultural and livestock expansion pose significant threats to the Brazilian savanna (Cerrado). This study aimed to analyze, map, and simulate LULC changes in the southern mesoregion of Maranhão State by generating geospatial scenarios projected [...] Read more.

Land use and land cover (LULC) changes driven by agricultural and livestock expansion pose significant threats to the Brazilian savanna (Cerrado). This study aimed to analyze, map, and simulate LULC changes in the southern mesoregion of Maranhão State by generating geospatial scenarios projected through 2030. LULC changes between 2015 and 2020 were analyzed using Landsat images classified with the Random Forest machine learning algorithm. A spatial model based on cellular automata was employed to simulate land use and land cover scenarios for the year 2030. When comparing the simulated map with the reference map, an overall accuracy of 70.28% and a Kappa index of 0.608 were observed. Results revealed a decrease in native savanna and grassland areas, with a corresponding increase in agricultural and pasturelands, notably in municipalities such as Balsas, Riachão, Tasso Fragoso, Carolina and Porto Franco. The 2030 simulation predicts continued agricultural expansion and a potential reduction of approximately 19% in native Cerrado vegetation cover, highlighting municipalities of Campestre do Maranhão, Porto Franco, São João do Paraíso, Feira Nova, Estreito, Balsas, Tasso Fragoso and Carolina. These findings underscore the value of integrating remote sensing and spatial modeling techniques within the framework of Geomatics to support environmental monitoring and management of land-use dynamics, including expansion, contraction, diversification, and agricultural intensification. This approach provides critical insights into anthropogenic impacts on sensitive ecosystems, informing sustainable planning in tropical savanna regions. Full article

(This article belongs to the Topic Geographic Information and Remote Sensing Technology (GIRST))

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19 pages, 4909 KB  

Open AccessArticle

Monitoring Landform Changes in a Mining Area in Mexico Using Geomatic Techniques

by Saúl Dávila-Cisneros, Ana G. Castañeda-Miranda, Carlos Francisco Bautista-Capetillo, Erick Dante Mattos-Villarroel, Víktor Iván Rodríguez-Abdalá, Cruz Octavio Robles Rovelo, Laura Alejandra Pinedo-Torres, Alejandro Rodríguez-Trejo and Salvador Ibarra-Delgado

Geomatics 2025, 5(4), 63; https://doi.org/10.3390/geomatics5040063 - 13 Nov 2025

Viewed by 868

Abstract

Mining activities are conducted to extract valuable minerals from the Earth, which are used to manufacture many objects. However, these operations generate landform alterations, such as deep excavations, artificial embankments, and landscape reshaping. In this study, landform changes were monitored in a mining [...] Read more.

Mining activities are conducted to extract valuable minerals from the Earth, which are used to manufacture many objects. However, these operations generate landform alterations, such as deep excavations, artificial embankments, and landscape reshaping. In this study, landform changes were monitored in a mining area in Mazapil, Zacatecas, Mexico, using geomatic techniques. Multitemporal Landsat satellite images and digital elevation models (DEMs) from different years were used to detect and quantify landform alterations and estimate the volumes of removed material. The results show ground depressions greater than −333 m and waste material accumulations greater than +152 m, with an average standard deviation of ±3.6 m. A total excavation volume of 413.524 million m³ and a total fill volume of 431.194 million m³ were quantified, with an estimated standard deviation of ±810 m³. The proposed methodology proved effective for the remote quantification of large-scale relief disturbances in open-pit mining areas. It can also be used for environmental monitoring and hydrological risk assessment in active and inactive mining areas. Full article

(This article belongs to the Topic Geographic Information and Remote Sensing Technology (GIRST))

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22 pages, 1373 KB  

Open AccessArticle

Global Self-Attention-Driven Graph Clustering Ensemble

by Lingbin Zeng, Shixin Yao, You Huang, Liquan Xiao, Yong Cheng and Yue Qian

Remote Sens. 2025, 17(22), 3680; https://doi.org/10.3390/rs17223680 - 10 Nov 2025

Viewed by 698

Abstract

A clustering ensemble, which leverages multiple base clusterings to obtain a reliable consensus result, is a critical challenging task for Earth observation in remote sensing applications. With the development of multi-source remote sensing data, exploring the underlying graph-structured patterns has become increasingly important. [...] Read more.

A clustering ensemble, which leverages multiple base clusterings to obtain a reliable consensus result, is a critical challenging task for Earth observation in remote sensing applications. With the development of multi-source remote sensing data, exploring the underlying graph-structured patterns has become increasingly important. However, existing clustering ensemble methods mostly employ shallow clustering in the base clustering generation stage, which fails to utilize the structural information. Moreover, the high dimensionality inherent in data further increases the difficulty of clustering. To address these problems, we propose a novel method termed Global Self-Attention-driven Graph Clustering Ensemble (GSAGCE). Specifically, GSAGCE firstly adopts basic autoencoders and global self-attention graph autoencoders (GSAGAEs) to extract node attribute information and structural information, respectively. GSAGAEs not only enhance structural information in the embedding but also have the capability to capture long-range vertex dependencies. Next, we employ a fusion strategy to adaptively fuse this dual information by considering the importance of nodes through an attention mechanism. Furthermore, we design a self-supervised strategy to adjust the clustering distribution, which integrates the attribute and structural embeddings as more reliable guidance to produce base clusterings. In the ensemble strategy, we devise a double-weighted graph partitioning consensus function that simultaneously considers both global and local diversity within the base clusterings to enhance the consensus performance. Extensive experiments on benchmark datasets demonstrate the superiority of GSAGCE compared to other state-of-the-art methods. Full article

(This article belongs to the Topic Geographic Information and Remote Sensing Technology (GIRST))

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17 pages, 5060 KB  

Open AccessArticle

Iterative Morphological Filtering for DEM Generation: Improving Accuracy and Robustness in Complex Terrains

by Shaobo Linghu, Wenlong Song, Yizhu Lu, Kaizheng Xiang, Hongjie Liu, Long Chen, Tianshi Feng, Rongjie Gui, Yao Zhao and Haider Abbas

Appl. Sci. 2025, 15(21), 11683; https://doi.org/10.3390/app152111683 - 31 Oct 2025

Viewed by 651

Abstract

Accurate terrain modeling from high-resolution digital surface models (DSM) is critical for geosciences, geology, geomorphology, earthquake studies, and applied geology. However, existing filtering methods such as progressive morphological filtering (PMF), cloth simulation filtering (CSF), and progressive TIN densification (TIN) often struggle with complex [...] Read more.

Accurate terrain modeling from high-resolution digital surface models (DSM) is critical for geosciences, geology, geomorphology, earthquake studies, and applied geology. However, existing filtering methods such as progressive morphological filtering (PMF), cloth simulation filtering (CSF), and progressive TIN densification (TIN) often struggle with complex topography and urban structures, leading to either excessive ground loss or incomplete object removal. Furthermore, some of these algorithms are only specialized for point cloud data and are not optimized for grid data. To address these limitations, we propose an iterative morphological filtering (IMF) algorithm that introduces a binary surface edge-segmentation strategy. The method refines object–ground separation by combining iterative morphological operations with block-based graph-cut stitching, thus enhancing continuity and accuracy in challenging terrain. Validation on UAV-derived DSM over the Haihe Basin in China and the ISPRS Vaihingen dataset shows that IMF achieves notable accuracy improvements: the Vaihingen test areas yielded an average Type I error of 8.93%, Type II error of 3.09%, overall accuracy of 80.85%, and Kappa coefficient of 0.7524, while the Haihe Basin test areas achieved Type I and II errors of 2.22% and 1.87%, overall accuracy of 89.32%, and a Kappa coefficient of 0.8706. These results demonstrate that IMF outperforms conventional methods by reducing both Type I and Type II errors, producing terrains highly consistent with real conditions. This innovation provides a robust and scalable solution for digital elevation models (DEM) generation from gridded DSM, offering significant value for large-scale environmental monitoring and flood risk assessment. Full article

(This article belongs to the Topic Geographic Information and Remote Sensing Technology (GIRST))

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21 pages, 1930 KB  

Open AccessArticle

Improved Multi-View Graph Clustering with Global Graph Refinement

by Lingbin Zeng, Shixin Yao, You Huang, Yong Cheng and Yue Qian

Remote Sens. 2025, 17(18), 3217; https://doi.org/10.3390/rs17183217 - 17 Sep 2025

Viewed by 1272

Abstract

The goal of multi-view graph clustering (MVGC) for remote sensing data is to obtain a consistent partitioning by capturing complementary and consensus information across multiple views. However, numerous ambiguous background samples in multi-view remote sensing data increase structural heterogeneity while simultaneously hindering effective [...] Read more.

The goal of multi-view graph clustering (MVGC) for remote sensing data is to obtain a consistent partitioning by capturing complementary and consensus information across multiple views. However, numerous ambiguous background samples in multi-view remote sensing data increase structural heterogeneity while simultaneously hindering effective information extraction and fusion. Existing MVGC methods cannot selectively integrate and fully refine both graph structure and node attribute information for consensus representation learning. Furthermore, current methods tend to overlook distant nodes, thus failing to capture the global graph structure. To solve these issues, we propose a novel method called Improved Multi-View Graph Clustering with Global Graph Refinement (IMGCGGR). Specifically, we first design a view-specific fusion network (VSFN) to extract and integrate node attribute and structural information into view-specific representation for each view. VSFN not only utilizes a global self-attention mechanism to enhance the global properties of structural information but also constructs a clustering loss through a self-supervised strategy to guide the view-specific clustering distribution assignment. Moreover, to enhance the capability of view-specific representation, a learnable attention-driven aggregation strategy is introduced to flexibly fuse the attribute and structural feature. Then, we adopt a cross-view fusion module to adaptively merge multiple view-specific representations for generating the final consensus representation. Comprehensive experiments show that IMGCGGR achieves significant clustering performance improvements over baseline methods across various benchmark datasets. Full article

(This article belongs to the Topic Geographic Information and Remote Sensing Technology (GIRST))

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30 pages, 13059 KB  

Open AccessArticle

Verifying the Effects of the Grey Level Co-Occurrence Matrix and Topographic–Hydrologic Features on Automatic Gully Extraction in Dexiang Town, Bayan County, China

by Zhuo Chen and Tao Liu

Remote Sens. 2025, 17(15), 2563; https://doi.org/10.3390/rs17152563 - 23 Jul 2025

Cited by 1 | Viewed by 1252

Abstract

Erosion gullies can reduce arable land area and decrease agricultural machinery efficiency; therefore, automatic gully extraction on a regional scale should be one of the preconditions of gully control and land management. The purpose of this study is to compare the effects of [...] Read more.

Erosion gullies can reduce arable land area and decrease agricultural machinery efficiency; therefore, automatic gully extraction on a regional scale should be one of the preconditions of gully control and land management. The purpose of this study is to compare the effects of the grey level co-occurrence matrix (GLCM) and topographic–hydrologic features on automatic gully extraction and guide future practices in adjacent regions. To accomplish this, GaoFen-2 (GF-2) satellite imagery and high-resolution digital elevation model (DEM) data were first collected. The GLCM and topographic–hydrologic features were generated, and then, a gully label dataset was built via visual interpretation. Second, the study area was divided into training, testing, and validation areas, and four practices using different feature combinations were conducted. The DeepLabV3+ and ResNet50 architectures were applied to train five models in each practice. Thirdly, the trainset gully intersection over union (IOU), test set gully IOU, receiver operating characteristic curve (ROC), area under the curve (AUC), user’s accuracy, producer’s accuracy, Kappa coefficient, and gully IOU in the validation area were used to assess the performance of the models in each practice. The results show that the validated gully IOU was 0.4299 (±0.0082) when only the red (R), green (G), blue (B), and near-infrared (NIR) bands were applied, and solely combining the topographic–hydrologic features with the RGB and NIR bands significantly improved the performance of the models, which boosted the validated gully IOU to 0.4796 (±0.0146). Nevertheless, solely combining GLCM features with RGB and NIR bands decreased the accuracy, which resulted in the lowest validated gully IOU of 0.3755 (±0.0229). Finally, by employing the full set of RGB and NIR bands, the GLCM and topographic–hydrologic features obtained a validated gully IOU of 0.4762 (±0.0163) and tended to show an equivalent improvement with the combination of topographic–hydrologic features and RGB and NIR bands. A preliminary explanation is that the GLCM captures the local textures of gullies and their backgrounds, and thus introduces ambiguity and noise into the convolutional neural network (CNN). Therefore, the GLCM tends to provide no benefit to automatic gully extraction with CNN-type algorithms, while topographic–hydrologic features, which are also original drivers of gullies, help determine the possible presence of water-origin gullies when optical bands fail to tell the difference between a gully and its confusing background. Full article

(This article belongs to the Topic Geographic Information and Remote Sensing Technology (GIRST))

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22 pages, 316 KB  

Open AccessReview

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

Cited by 2 | Viewed by 2974

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 KB  

Open AccessArticle

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

Cited by 2 | Viewed by 1949

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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