Journal Description
Geomatics
Geomatics
is an international, peer-reviewed, open access journal on geomatic science published quarterly online by MDPI.
- Open Access— free for readers, with article processing charges (APC) paid by authors or their institutions.
- High Visibility: indexed within ESCI (Web of Science), Scopus, EBSCO, and other databases.
- Journal Rank: JCR - Q2 (Geography, Physical) / CiteScore - Q1 (Earth and Planetary Sciences (miscellaneous))
- Rapid Publication: manuscripts are peer-reviewed and a first decision is provided to authors approximately 20 days after submission; acceptance to publication is undertaken in 2.6 days (median values for papers published in this journal in the first half of 2025).
- Recognition of Reviewers: APC discount vouchers, optional signed peer review, and reviewer names published annually in the journal.
- Companion journal: Remote Sensing.
- Journal Cluster of Geospatial and Earth Sciences: Remote Sensing, Geosciences, Quaternary, Earth, Geographies, Geomatics and Fossil Studies.
Impact Factor:
2.8 (2024);
5-Year Impact Factor:
2.5 (2024)
Latest Articles
Spatiotemporal Exposure to Heavy-Day Rainfall in the Western Himalaya Mapped with Remote Sensing, GIS, and Deep Learning
Geomatics 2025, 5(3), 37; https://doi.org/10.3390/geomatics5030037 - 7 Aug 2025
Abstract
►
Show Figures
Heavy rainfall events, characterized by extreme downpours that exceed 100 mm per day, pose an intensifying hazard to the densely settled valleys of the western Himalaya; however, their coupling with expanding urban land cover remains under-quantified. This study mapped the spatiotemporal exposure of
[...] Read more.
Heavy rainfall events, characterized by extreme downpours that exceed 100 mm per day, pose an intensifying hazard to the densely settled valleys of the western Himalaya; however, their coupling with expanding urban land cover remains under-quantified. This study mapped the spatiotemporal exposure of built-up areas to heavy-day rainfall (HDR) across Jammu, Kashmir, and Ladakh and the adjoining areas by integrating daily Climate Hazards Group InfraRed Precipitation with Stations product (CHIRPS) precipitation (0.05°) with Global Human Settlement Layer (GHSL) built-up fractions within the Google Earth Engine (GEE). Given the limited sub-hourly observations, a daily threshold of ≥100 mm was adopted as a proxy for HDR, with sensitivity evaluated at alternative thresholds. The results showed that HDR is strongly clustered along the Kashmir Valley and the Pir Panjal flank, as demonstrated by the mean annual count of threshold-exceeding pixels increasing from 12 yr−1 (2000–2010) to 18 yr−1 (2011–2020), with two pixel-scale hotspots recurring southwest of Srinagar and near Baramulla regions. The cumulative high-intensity areas covered 31,555.26 km2, whereas 37,897.04 km2 of adjacent terrain registered no HDR events. Within this hazard belt, the exposed built-up area increased from 45 km2 in 2000 to 72 km2 in 2020, totaling 828 km2. The years with the most expansive rainfall footprints, 344 km2 (2010), 520 km2 (2012), and 650 km2 (2014), coincided with heavy Western Disturbances (WDs) and locally vigorous convection, producing the largest exposure increments. We also performed a forecast using a univariate long short-term memory (LSTM), outperforming Autoregressive Integrated Moving Average (ARIMA) and linear baselines on a 2017–2020 holdout (Root Mean Square Error, RMSE 0.82 km2; measure of errors, MAE 0.65 km2; R2 0.89), projecting the annual built-up area intersecting HDR to increase from ~320 km2 (2021) to ~420 km2 (2030); 95% prediction intervals widened from ±6 to ±11 km2 and remained above the historical median (~70 km2). In the absence of a long-term increase in total annual precipitation, the projected rise most likely reflects continued urban encroachment into recurrent high-intensity zones. The resulting spatial masks and exposure trajectories provide operational evidence to guide zoning, drainage design, and early warning protocols in the region.
Full article
Open AccessArticle
Dynamics of Water Quality in the Mirim–Patos–Mangueira Coastal Lagoon System with Sentinel-3 OLCI Data
by
Paula Andrea Contreras Rojas, Felipe de Lucia Lobo, Wesley J. Moses, Gilberto Loguercio Collares and Lino Sander de Carvalho
Geomatics 2025, 5(3), 36; https://doi.org/10.3390/geomatics5030036 - 25 Jul 2025
Abstract
The Mirim–Patos–Mangueira coastal lagoon system provides a wide range of ecosystem services. However, its vast territorial extent and the political boundaries that divide it hinder integrated assessments, especially during extreme hydrological events. This study is divided into two parts. First, we assessed the
[...] Read more.
The Mirim–Patos–Mangueira coastal lagoon system provides a wide range of ecosystem services. However, its vast territorial extent and the political boundaries that divide it hinder integrated assessments, especially during extreme hydrological events. This study is divided into two parts. First, we assessed the spatial and temporal patterns of water quality in the lagoon system using Sentinel-3/OLCI satellite imagery. Atmospheric correction was performed using ACOLITE, followed by spectral grouping and classification into optical water types (OWTs) using the Sentinel Applications Platform (SNAP). To explore the behavior of water quality parameters across OWTs, Chlorophyll-a and turbidity were estimated using semi-empirical algorithms specifically designed for complex inland and coastal waters. Results showed a gradual increase in mean turbidity from OWT 2 to OWT 6 and a rise in chlorophyll-a from OWT 2 to OWT 4, with a decline at OWT 6. These OWTs correspond, in general terms, to distinct water masses: OWT 2 to clearer waters, OWT 3 and 4 to intermediate/mixed conditions, and OWT 6 to turbid environments. In the second part, we analyzed the response of the Patos Lagoon to flooding in Rio Grande do Sul during an extreme weather event in May 2024. Satellite-derived turbidity estimates were compared with in situ measurements, revealing a systematic underestimation, with a negative bias of 2.6%, a mean relative error of 78%, and a correlation coefficient of 0.85. The findings highlight the utility of OWT classification for tracking changes in water quality and support the use of remote sensing tools to improve environmental monitoring in data-scarce regions, particularly under extreme hydrometeorological conditions.
Full article
(This article belongs to the Special Issue Advances in Ocean Mapping and Hydrospatial Applications)
►▼
Show Figures

Figure 1
Open AccessCommunication
Physics-Informed Gaussian-Enforced Separated-Band Convolutional Conversion Network for Moving Object Satellite Image Conversion
by
Andrew J. Lew, Timothy Perkins, Ethan Brewer, Paul Corlies and Robert Sundberg
Geomatics 2025, 5(3), 35; https://doi.org/10.3390/geomatics5030035 - 23 Jul 2025
Abstract
►▼
Show Figures
Integrating diverse image datasets acquired from different satellites is challenging. Converting images from one sensor to another, like from WorldView-3 (WV) to SuperDove (SD), involves both changing image channel wavelengths and per-band intensity scales because different sensors can acquire imagery of the same
[...] Read more.
Integrating diverse image datasets acquired from different satellites is challenging. Converting images from one sensor to another, like from WorldView-3 (WV) to SuperDove (SD), involves both changing image channel wavelengths and per-band intensity scales because different sensors can acquire imagery of the same scene at different wavelengths and intensities. A parametrized convolutional network approach has shown promise converting across sensor domains, but it introduces distortion artefacts when objects are in motion. The cause of spectral distortion is due to temporal delays between sequential multispectral band acquisitions. This can result in spuriously blurred images of moving objects in the converted imagery, and consequently misaligned moving object locations across image bands. To resolve this, we propose an enhanced model, the Physics-Informed Gaussian-Enforced Separated-Band Convolutional Conversion Network (PIGESBCCN), which better accounts for known spatial, spectral, and temporal correlations between bands via band reordering and branched model architecture.
Full article

Figure 1
Open AccessArticle
Ensemble Machine Learning Approaches for Bathymetry Estimation in Multi-Spectral Images
by
Kazi Aminul Islam, Omar Abul-Hassan, Hongfang Zhang, Victoria Hill, Blake Schaeffer, Richard Zimmerman and Jiang Li
Geomatics 2025, 5(3), 34; https://doi.org/10.3390/geomatics5030034 - 22 Jul 2025
Abstract
Traditional bathymetry measures require a large number of human hours, and many bathymetry records are obsolete or missing. Automated measures of bathymetry would reduce costs and increase accessibility for research and applications. In this paper, we optimized a recent machine learning model, named
[...] Read more.
Traditional bathymetry measures require a large number of human hours, and many bathymetry records are obsolete or missing. Automated measures of bathymetry would reduce costs and increase accessibility for research and applications. In this paper, we optimized a recent machine learning model, named CatBoostOpt, to estimate bathymetry based on high-resolution WorldView-2 (WV-2) multi-spectral optical satellite images. CatBoostOpt was demonstrated across the Florida Big Bend coastline, where the model learned correlations between in situ sound Navigation and Ranging (Sonar) bathymetry measurements and the corresponding multi-spectral reflectance values in WV-2 images to map bathymetry. We evaluated three different feature transformations as inputs for bathymetry estimation, including raw reflectance, log-linear, and log-ratio transforms of the raw reflectance value in WV-2 images. In addition, we investigated the contribution of each spectral band and found that utilizing all eight spectral bands in WV-2 images offers the best solution for handling complex water quality conditions. We compared CatBoostOpt with linear regression (LR), support vector machine (SVM), random forest (RF), AdaBoost, gradient boosting, and deep convolutional neural network (DCNN). CatBoostOpt with log-ratio transformed reflectance achieved the best performance with an average root mean square error (RMSE) of 0.34 and coefficient of determination ( ) of 0.87.
Full article
(This article belongs to the Special Issue Advances in Ocean Mapping and Hydrospatial Applications)
►▼
Show Figures

Figure 1
Open AccessPerspective
HAIMO: A Hybrid Approach to Trajectory Interaction Analysis Combining Knowledge-Driven and Data-Driven AI
by
Nico Van de Weghe, Lars De Sloover, Jana Verdoodt and Haosheng Huang
Geomatics 2025, 5(3), 33; https://doi.org/10.3390/geomatics5030033 - 22 Jul 2025
Abstract
►▼
Show Figures
Capturing the interactions between moving objects is vital in traffic analysis, sports, and animal behavior, but remains challenging because of subtle spatiotemporal dynamics. This paper introduces HAIMO (Hybrid Analysis of the Interaction of Moving Objects), a conceptual framework that combines knowledge-driven AI for
[...] Read more.
Capturing the interactions between moving objects is vital in traffic analysis, sports, and animal behavior, but remains challenging because of subtle spatiotemporal dynamics. This paper introduces HAIMO (Hybrid Analysis of the Interaction of Moving Objects), a conceptual framework that combines knowledge-driven AI for interpretable, symbolic interaction representations with data-driven models trained through self-supervised learning (SSL) on large sets of unlabeled trajectory data. In HAIMO, we propose using transformer architectures to model complex spatiotemporal dependencies while maintaining interpretability through symbolic reasoning. To illustrate the feasibility of this hybrid approach, we present a basic proof-of-concept using elite tennis rallies, where the knowledge-driven component identifies interaction patterns between players and the ball, and we outline how SSL-enhanced transformer models could support and strengthen movement prediction. By bridging symbolic reasoning and self-supervised data-driven learning, HAIMO provides a conceptual foundation for future GeoAI and spatiotemporal analytics, especially in applications where both pattern discovery and explainability are crucial.
Full article

Figure 1
Open AccessArticle
Improving Individual Tree Crown Detection and Species Classification in a Complex Mixed Conifer–Broadleaf Forest Using Two Machine Learning Models with Different Combinations of Metrics Derived from UAV Imagery
by
Jeyavanan Karthigesu, Toshiaki Owari, Satoshi Tsuyuki and Takuya Hiroshima
Geomatics 2025, 5(3), 32; https://doi.org/10.3390/geomatics5030032 - 13 Jul 2025
Abstract
Individual tree crown detection (ITCD) and tree species classification are critical for forest inventory, species-specific monitoring, and ecological studies. However, accurately detecting tree crowns and identifying species in structurally complex forests with overlapping canopies remains challenging. This study was conducted in a complex
[...] Read more.
Individual tree crown detection (ITCD) and tree species classification are critical for forest inventory, species-specific monitoring, and ecological studies. However, accurately detecting tree crowns and identifying species in structurally complex forests with overlapping canopies remains challenging. This study was conducted in a complex mixed conifer–broadleaf forest in northern Japan, aiming to improve ITCD and species classification by employing two machine learning models and different combinations of metrics derived from very high-resolution (2.5 cm) UAV red–green–blue (RGB) and multispectral (MS) imagery. We first enhanced ITCD by integrating different combinations of metrics into multiresolution segmentation (MRS) and DeepForest (DF) models. ITCD accuracy was evaluated across dominant forest types and tree density classes. Next, nine tree species were classified using the ITCD outputs from both MRS and DF approaches, applying Random Forest and DF models, respectively. Incorporating structural, textural, and spectral metrics improved MRS-based ITCD, achieving F-scores of 0.44–0.58. The DF model, which used only structural and spectral metrics, achieved higher F-scores of 0.62–0.79. For species classification, the Random Forest model achieved a Kappa value of 0.81, while the DF model attained a higher Kappa value of 0.91. These findings demonstrate the effectiveness of integrating UAV-derived metrics and advanced modeling approaches for accurate ITCD and species classification in heterogeneous forest environments. The proposed methodology offers a scalable and cost-efficient solution for detailed forest monitoring and species-level assessment.
Full article
(This article belongs to the Topic Vegetation Characterization and Classification With Multi-Source Remote Sensing Data)
►▼
Show Figures

Figure 1
Open AccessEditorial
Back to Geomatics: Recognizing Who We Are
by
Enrico Corrado Borgogno-Mondino
Geomatics 2025, 5(3), 31; https://doi.org/10.3390/geomatics5030031 - 7 Jul 2025
Abstract
Recently, geomatics-related data, products, services and applications have proven to significantly support many actions in environmental (land, water, extra-terrestrial) analysis, management and protection, often answering to political instances [...]
Full article
Open AccessArticle
Can Combining Machine Learning Techniques and Remote Sensing Data Improve the Accuracy of Aboveground Biomass Estimations in Temperate Forests of Central Mexico?
by
Martin Enrique Romero-Sanchez, Antonio Gonzalez-Hernandez, Efraín Velasco-Bautista, Arian Correa-Diaz, Alma Delia Ortiz-Reyes and Ramiro Perez-Miranda
Geomatics 2025, 5(3), 30; https://doi.org/10.3390/geomatics5030030 - 3 Jul 2025
Abstract
►▼
Show Figures
Estimating aboveground biomass (AGB) is crucial for understanding the carbon cycle in terrestrial ecosystems, particularly within the context of climate change. Therefore, it is essential to research and compare different methods of AGB estimation to achieve acceptable accuracy. This study modelled AGB in
[...] Read more.
Estimating aboveground biomass (AGB) is crucial for understanding the carbon cycle in terrestrial ecosystems, particularly within the context of climate change. Therefore, it is essential to research and compare different methods of AGB estimation to achieve acceptable accuracy. This study modelled AGB in temperate forests of central Mexico using active and passive remote sensing data combined with machine learning techniques (Random Forest and XGBoost) and compared the estimations against a traditional method, such as linear regression. The main goal was to evaluate the performance of machine learning techniques against linear regression in AGB estimation and then validate against an independent forest inventory database. The models obtained acceptable performance in all cases, but the machine learning algorithm Random Forest outperformed (R2cv = 0.54; RMSEcv = 19.17) the regression method (R2cv = 0.41; RMSEcv = 25.76). The variables that made significant contributions, in both Random Forest and XGBoost modelling, were NDVI, kNDVI (Landsat OLI sensor), and the HV polarisation from ALOS-Palsar. For validation, the Machine learning ensemble had a higher Spearman correlation (r = 0.68) than the linear regression (r = 0.50). These findings highlight the potential of integrating machine learning techniques with remote sensing data to improve the reliability of AGB estimation in temperate forests.
Full article

Figure 1
Open AccessArticle
Land Use and Land Cover (LULC) Mapping Accuracy Using Single-Date Sentinel-2 MSI Imagery with Random Forest and Classification and Regression Tree Classifiers
by
Sercan Gülci, Michael Wing and Abdullah Emin Akay
Geomatics 2025, 5(3), 29; https://doi.org/10.3390/geomatics5030029 - 1 Jul 2025
Abstract
►▼
Show Figures
The use of Google Earth Engine (GEE), a cloud-based computing platform, in spatio-temporal evaluation studies has increased rapidly in natural sciences such as forestry. In this study, Sentinel-2 satellite imagery and Shuttle Radar Topography Mission (SRTM) elevation data and image classification algorithms based
[...] Read more.
The use of Google Earth Engine (GEE), a cloud-based computing platform, in spatio-temporal evaluation studies has increased rapidly in natural sciences such as forestry. In this study, Sentinel-2 satellite imagery and Shuttle Radar Topography Mission (SRTM) elevation data and image classification algorithms based on two machine learning techniques were examined. Random Forest (RF) and Classification and Regression Trees (CART) were used to classify land use and land cover (LULC) in western Oregon (USA). To classify the LULC from the spectral bands of satellite images, a composition consisting of vegetation difference indices NDVI, NDWI, EVI, and BSI, and a digital elevation model (DEM) were used. The study area was selected due to a diversity of land cover types including research forest, botanical gardens, recreation area, and agricultural lands covered with diverse plant species. Five land classes (forest, agriculture, soil, water, and settlement) were delineated for LULC classification testing. Different spatial points (totaling 75, 150, 300, and 2500) were used as training and test data. The most successful model performance was RF, with an accuracy of 98% and a kappa value of 0.97, while the accuracy and kappa values for CART were 95% and 0.94, respectively. The accuracy of the generated LULC maps was evaluated using 500 independent reference points, in addition to the training and testing datasets. Based on this assessment, the RF classifier that included elevation data achieved an overall accuracy of 92% and a kappa coefficient of 0.90. The combination of vegetation difference indices with elevation data was successful in determining the areas where clear-cutting occurred in the forest. Our results present a promising technique for the detection of forests and forest openings, which was helpful in identifying clear-cut sites. In addition, the GEE and RF classifier can help identify and map storm damage, wind damage, insect defoliation, fire, and management activities in forest areas.
Full article

Figure 1
Open AccessArticle
An Evaluation of Static Affordable Smartphone Positioning Performance Leveraging GPS/Galileo Measurements with Instantaneous CNES and Final IGS Products
by
Mohamed Abdelazeem, Hussain A. Kamal, Amgad Abazeed and Amr M. Wahaballa
Geomatics 2025, 5(3), 28; https://doi.org/10.3390/geomatics5030028 - 27 Jun 2025
Abstract
►▼
Show Figures
This research examines the performance of the affordable Xiaomi 11T smartphone in static positioning mode. Static Global Navigation Satellite System (GNSS) measurements are acquired over a two-hour period with a known reference point, spanning three consecutive days. The acquired data are processed, employing
[...] Read more.
This research examines the performance of the affordable Xiaomi 11T smartphone in static positioning mode. Static Global Navigation Satellite System (GNSS) measurements are acquired over a two-hour period with a known reference point, spanning three consecutive days. The acquired data are processed, employing both real-time and post-processing Precise Point Positioning (PPP) solutions using GPS-only, Galileo-only, and the combined GPS/Galileo datasets. To correct the satellite and clock errors, the instantaneous Centre National d’Études Spatiales (CNES), the final Le Groupe de Recherche de Géodésie Spatiale (GRG), GeoForschungsZentrum (GFZ), and Wuhan University (WUM) products were applied. The results demonstrate that sub-30 cm positioning accuracy is achieved in the horizontal direction using real-time and final products. Additionally, sub-50 cm positioning accuracy is attained in the vertical direction for the real-time and post-processed solutions. Furthermore, the real-time products achieved three-dimensional (3D) position accuracies of 40 cm, 29 cm, and 20 cm using GPS-only, Galileo-only, and the combined GPS/Galileo observations, respectively. The final products achieved 3D position accuracies of 24 cm, 26 cm, and 28 cm using GPS-only, Galileo-only, and the combined GPS/Galileo measurements, respectively. The attained positioning accuracy can be used in some land use and urban planning applications.
Full article

Figure 1
Open AccessReview
Cloud Detection Methods for Optical Satellite Imagery: A Comprehensive Review
by
Rohit Singh, Mahesh Pal and Mantosh Biswas
Geomatics 2025, 5(3), 27; https://doi.org/10.3390/geomatics5030027 - 26 Jun 2025
Abstract
►▼
Show Figures
With the continuous advancement of remote sensing technology and its growing importance, the need for ready-to-use data has increased exponentially. Satellite platforms such as Sentinel-2, which carries the Multispectral Instrument (MSI) sensor, known for their cost-effectiveness, capture valuable information about Earth in the
[...] Read more.
With the continuous advancement of remote sensing technology and its growing importance, the need for ready-to-use data has increased exponentially. Satellite platforms such as Sentinel-2, which carries the Multispectral Instrument (MSI) sensor, known for their cost-effectiveness, capture valuable information about Earth in the form of images. However, they encounter a significant challenge in the form of clouds and their shadows, which hinders the data acquisition and processing for regions of interest. This article undertakes a comprehensive literature review to systematically analyze the critical cloud-related challenges. It explores the need for accurate cloud detection, reviews existing datasets, and evaluates contemporary cloud detection methodologies, including their strengths and limitations. Additionally, it highlights the inaccuracies introduced by varying atmospheric and environmental conditions, emphasizing the importance of integrating advanced techniques that can utilize local and global semantics. The review also introduces a structured intercomparison framework to enable standardized evaluation across binary and multiclass cloud detection methods using both qualitative and quantitative metrics. To facilitate fair comparison, a conversion mechanism is highlighted to harmonize outputs across methods with different class granularities. By identifying gaps in current practices and datasets, the study highlights the importance of innovative, efficient, and scalable solutions for automated cloud detection, paving the way for unbiased evaluation and improved utilization of satellite imagery across diverse applications.
Full article

Figure 1
Open AccessArticle
Simulation of GNSS Dilution of Precision for Automated Mobility Along the MODI Project Road Corridor Using High-Resolution Digital Surface Models
by
Kristian Breili and Carl William Lund
Geomatics 2025, 5(2), 26; https://doi.org/10.3390/geomatics5020026 - 19 Jun 2025
Abstract
►▼
Show Figures
Horizontal dilution of precision (HDOP) is a widely used quality indicator of Global Navigation Satellite System (GNSS) positioning, considering only satellite geometry. In this study, HDOP was simulated using GNSS almanacs and high-resolution digital surface models (DSMs) along three European road sections: Oslo—
[...] Read more.
Horizontal dilution of precision (HDOP) is a widely used quality indicator of Global Navigation Satellite System (GNSS) positioning, considering only satellite geometry. In this study, HDOP was simulated using GNSS almanacs and high-resolution digital surface models (DSMs) along three European road sections: Oslo— Svinesund Bridge (Norway); Hamburg city center (Germany); and Rotterdam—Dutch–German border (Netherlands). This study was accomplished as part of the MODI project, which is a cross-border initiative to accelerate Cooperative, Connected, and Automated Mobility (CCAM). Our analysis revealed excellent or good overall GNSS performance in the study areas, particularly on highway sections with 99–100% of study points having a median HDOP that is categorized as excellent (HDOP < 2) or good (HDOP < 5). However, the road section in Hamburg’s city center presents challenges. When GPS is used alone, 8% of the study points experience weak or poor HDOP, and there are study points where the system is available (HDOP < 5) less than 50% of the time. Combining GNSS constellations significantly improved system availability, reaching 95% for 99% of the study points in Hamburg. To validate our simulations, we compared results with GNSS observations from a survey vehicle in Hamburg. Initial low correlation was attributed to the reception of signals from non-line-of-sight satellites. By excluding satellites with low signal-to-noise ratios, the correlation increased significantly, and reasonable agreement was obtained. We also examined the impact of using a 10 m DSM instead of a 1 m DSM in Hamburg. While the coarser spatial resolution offers computational benefits, it may miss critical details for accurate assessment of satellite visibility.
Full article

Figure 1
Open AccessArticle
Large-Scale Topographic Mapping Using RTK-GNSS and Multispectral UAV Drone Photogrammetric Surveys: Comparative Evaluation of Experimental Results
by
Siyandza M. Dlamini and Yashon O. Ouma
Geomatics 2025, 5(2), 25; https://doi.org/10.3390/geomatics5020025 - 18 Jun 2025
Abstract
►▼
Show Figures
The automation in image acquisition and processing using UAV drones has the potential to acquire terrain data that can be utilized for the accurate production of 2D and 3D digital data. In this study, the DJI Phantom 4 drone was employed for large-scale
[...] Read more.
The automation in image acquisition and processing using UAV drones has the potential to acquire terrain data that can be utilized for the accurate production of 2D and 3D digital data. In this study, the DJI Phantom 4 drone was employed for large-scale topographical mapping, and based on the photogrammetric Structure-from-Motion (SfM) algorithm, drone-derived point clouds were used to generate the terrain DSM, DEM, contours, and the orthomosaic from which the topographical map features were digitized. An evaluation of the horizontal (X, Y) and vertical (Z) coordinates of the UAV drone points and the RTK-GNSS survey data showed that the Z-coordinates had the highest MAE(X,Y,Z), RMSE(X,Y,Z) and Accuracy(X,Y,Z) errors. An integrated georeferencing of the UAV drone imagery using the mobile RTK-GNSS base station improved the 2D and 3D positional accuracies with an average 2D (X, Y) accuracy of <2 mm and height accuracy of −2.324 mm, with an overall 3D accuracy of −4.022 mm. Geometrically, the average difference in the perimeter and areas of the features from the RTK-GNSS and UAV drone topographical maps were −0.26% and −0.23%, respectively. The results achieved the recommended positional accuracy standards for the production of digital geospatial data, demonstrating the cost-effectiveness of low-cost UAV drones for large-scale topographical mapping.
Full article

Figure 1
Open AccessReview
Review of the Problem of the Earth Shape
by
Petr Vaníček, Pavel Novák and Marcelo Santos
Geomatics 2025, 5(2), 24; https://doi.org/10.3390/geomatics5020024 - 13 Jun 2025
Abstract
►▼
Show Figures
The determination of the shape of the Earth has been one of the fundamental problems geodesy was supposed to solve; it has been and possibly still is the main geodetic problem. It is thus appropriate for geodesists to look at this problem
[...] Read more.
The determination of the shape of the Earth has been one of the fundamental problems geodesy was supposed to solve; it has been and possibly still is the main geodetic problem. It is thus appropriate for geodesists to look at this problem periodically, and this is what the authors of this paper aim to do. About 50 years ago, geodesists started using satellites as a new and very powerful tool. Many problems that were either impossible to solve or that presented almost unsurmountable hurdles to solutions have now been solved relatively simply, so much so that in the eyes of some people, satellites can solve all geodetic problems, and attempts are being made to show that this is indeed the case. We feel that the time has come to show that even satellites have their limitations, the main one being that for them to remain in their orbit, they must fly quite high, typically at several hundred kilometres. The gravitational field of the Earth (and that of any celestial body) smoother as one gets higher and higher. In other words, the gravitational field at the satellite orbit altitude loses detailed information that one can see at the surface of the Earth. In this contribution, we shall try to explain what satellites have contributed to the study of the shape of the Earth and what issues remain to be sorted out.
Full article

Figure 1
Open AccessHypothesis
Seeking More Sustainable Merger and Acquisition Growth Strategies: A Spatial Analysis of U.S. Hospital Network Dispersion and Customer Satisfaction
by
William Ritchie, Ali Shahzad, Scott R. Gallagher and Wolfgang Hall
Geomatics 2025, 5(2), 23; https://doi.org/10.3390/geomatics5020023 - 5 Jun 2025
Abstract
►▼
Show Figures
The pursuit of mergers and acquisitions (M&A) is often an acclaimed strategy for firm growth, resource sharing, and extended reach into new market segments. However, in the healthcare marketplace, there are two very different perspectives related to M&A. On the one hand, the
[...] Read more.
The pursuit of mergers and acquisitions (M&A) is often an acclaimed strategy for firm growth, resource sharing, and extended reach into new market segments. However, in the healthcare marketplace, there are two very different perspectives related to M&A. On the one hand, the American Hospital Association commends M&A activity as a tool to reduce healthcare costs, drive quality, and serve rural markets. On the other hand, a recent United States’ Presidential executive order suggests that M&A in the healthcare space is harmful to healthcare due to its restrictions on competition and adverse impacts on patients. These conflicting perspectives reflect differing M&A views in mainstream management research, as well. The purpose of the current study is twofold. First, we aim to explore these two seemingly paradoxical perspectives by examining the degree of hospital network geographic dispersion that results from M&A activity. Second, we contribute to the broader M&A literature by drawing attention to the importance of considering geographic influences on M&A performance. Using a spatial analysis of 147 nationwide hospital networks comprising 1713 hospitals, we propose and find support for the notion that the degree of network dispersion, as measured by actual driving distances in healthcare networks, are correlated with patient experiences. Using ordinary least squares (OLS) regression to examine relationships between patient experiences and overall hospital network geographic dispersion, we found support for the hypothesis that more spatially dispersed healthcare networks are associated with lower overall performance outcomes, as measured by customer (patient) satisfaction. The implications of these findings suggest that growth strategies that involve M&A activity should carefully consider the spatial influences on M&A entity selection. Our exploratory findings also provide a foundation for future research to bridge the gap between industry and governmental perspectives on healthcare M&A practices.
Full article

Figure 1
Open AccessArticle
From Meta SAM to ArcGIS: A Comparative Analysis of Image Segmentation Methods for Monitoring Refugee Camp Transitions
by
Noor Marji and Michal Kohout
Geomatics 2025, 5(2), 22; https://doi.org/10.3390/geomatics5020022 - 23 May 2025
Abstract
►▼
Show Figures
This article presents a comprehensive evaluation of image segmentation methods for monitoring morphological changes in refugee camps, comparing five distinct approaches: ESRI Landviewer clustering, K-means clustering, U-Net segmentation, Meta’s Segment Anything Model (SAM) and ArcGIS segmentation. Using high-resolution satellite imagery from Al-Azraq refugee
[...] Read more.
This article presents a comprehensive evaluation of image segmentation methods for monitoring morphological changes in refugee camps, comparing five distinct approaches: ESRI Landviewer clustering, K-means clustering, U-Net segmentation, Meta’s Segment Anything Model (SAM) and ArcGIS segmentation. Using high-resolution satellite imagery from Al-Azraq refugee camp in Jordan (2014–2023) as a case study, this research systematically assesses each method’s performance in detecting and quantifying settlement pattern changes. The evaluation framework incorporates multiple validation metrics, including overall accuracy, the Kappa coefficient, F1-score and computational efficiency. The results demonstrate that ArcGIS’s ISO clustering and classification approach achieves superior performance, with 99% overall accuracy and a Kappa coefficient of 0.95, significantly outperforming the other tested methods. While Meta SAM shows promise in object detection, its performance degrades with aerial imagery, achieving only 75% accuracy in settlement pattern recognition. The study establishes specific parameter optimization guidelines for humanitarian contexts, with spectral detail values of 3.0–7.0 and spatial detail values of 14.0–18.0, yielding optimal results for refugee settlement analysis. These findings provide crucial methodological guidance for monitoring refugee settlement evolution and transition, contributing to more effective humanitarian response planning and settlement management through integrating remote sensing and machine learning technologies.
Full article

Figure 1
Open AccessArticle
Modeling of Compound Curves on Railway Lines
by
Wladyslaw Koc
Geomatics 2025, 5(2), 21; https://doi.org/10.3390/geomatics5020021 - 12 May 2025
Abstract
►▼
Show Figures
This article addresses the issue of designing compound curves, i.e., a geometric system consisting of two (or more) circular arcs of different radii, pointing in the same direction and directly connected to each other. Nowadays, compound curves are mainly used on tram lines;
[...] Read more.
This article addresses the issue of designing compound curves, i.e., a geometric system consisting of two (or more) circular arcs of different radii, pointing in the same direction and directly connected to each other. Nowadays, compound curves are mainly used on tram lines; they also occur on railways (e.g., on mountain lines), but new ones are generally no longer being built there. Therefore, in relation to railway lines, the aim is to be able to recreate (i.e., model) the existing geometric layout with compound curves, so that it is then possible to correct this layout. An analytical method for designing track geometric systems was used, adapted to the mobile satellite measurement technique, in which calculations are carried out in the appropriate local Cartesian coordinate system. The basis of this system is the symmetrically arranged adjacent main directions of the route, and the beginning is located at the point of intersection of these directions. A number of detailed issues have been clarified and basic characteristic quantities have been determined, and the computational algorithm described in the paper leads to the solution of the problem in a sequential manner. The obtained possibilities of modeling the compound curves are illustrated by the provided calculation example.
Full article

Figure 1
Open AccessArticle
Integrating Sustainability Reflection in a Geographic Information Science Capstone Project Course
by
Forrest Hisey, Valerie Lin and Tingting Zhu
Geomatics 2025, 5(2), 20; https://doi.org/10.3390/geomatics5020020 - 9 May 2025
Abstract
Higher education institutions have played a central role in building sustainability awareness. However, current models only show an effect on students’ knowledge about sustainable development, with a large gap in transformative solutions that shift from understanding problems towards solutions. This case study explores
[...] Read more.
Higher education institutions have played a central role in building sustainability awareness. However, current models only show an effect on students’ knowledge about sustainable development, with a large gap in transformative solutions that shift from understanding problems towards solutions. This case study explores a new model that integrates sustainability reflections in a Geographic Information Science (GIS) Capstone Project course. Through collaborations with external partners and reflections on sustainability modules, students analyzed complex problems and developed sustainability competencies. The assessment tool adopted in this study combines reflective writing, scenario testing, performance observation, and self-assessment. Based on the set of key competencies in sustainability, half of the students developed systems-thinking and strategies-thinking, while a quarter of the students developed futures-thinking and values-thinking. Their development of sustainability competencies went beyond simply acquiring knowledge, also critically evaluating different perspectives and implementing or integrating the concepts when addressing the problems. Geospatial information tackles three key aspects of sustainability, which are relational, distributional, and directional, making it ideal in analyzing sustainability issues and providing insights for informed decisions. This study fills another important gap of integrating sustainability competency development in GIS education.
Full article
Open AccessTechnical Note
Analysis of Resampling Methods for the Red Edge Band of MSI/Sentinel-2A for Coffee Cultivation Monitoring
by
Rozymario Fagundes, Luiz Patric Kayser, Lúcio de Paula Amaral, Ana Caroline Benedetti, Édson Luis Bolfe, Taya Cristo Parreiras, Manuela Ramos-Ospina and Alejandro Marulanda-Tobón
Geomatics 2025, 5(2), 19; https://doi.org/10.3390/geomatics5020019 - 8 May 2025
Abstract
►▼
Show Figures
Spectral indices such as NDRE (Normalized Difference Red Edge Index), CCCI (Canopy Chlorophyll Content Index), and IRECI (Inverted Red Edge Chlorophyll Index), derived from the Red Edge band of MSI/Sentinel-2A (B05, B06, B07), are critical for coffee monitoring. To align the Red Edge
[...] Read more.
Spectral indices such as NDRE (Normalized Difference Red Edge Index), CCCI (Canopy Chlorophyll Content Index), and IRECI (Inverted Red Edge Chlorophyll Index), derived from the Red Edge band of MSI/Sentinel-2A (B05, B06, B07), are critical for coffee monitoring. To align the Red Edge band (20 m resolution) with the NIR band (10 m resolution), the nearest neighbor, bilinear, cubic and Lanczos resampling methods were used, available in the Terra package in the R software(4.4.0). This study evaluates these methods using two original B05 images from 24 November 2023, and 21 September 2023, covering the “Ouro Verde” (15 ha) and “Canto do Rio” (45 ha) farms in Bahia, Brazil. A total of 500 random points were analyzed using PSF, linear models, and cross-validation with R2, MAE, and RMSE. PSF analysis confirmed data integrity, and the cubic method demonstrated the best performance (R2 = 0.996, MAE = 0.008 and RMSE = 0.012 in the “Ouro Verde” Farm and R2 = 0.995, MAE = 0.007 and RMSE = 0.011 in the “Canto do Rio” Farm). The results highlight the importance of selecting appropriate resampling methods for precise remote sensing in coffee cultivation, ensuring accurate digital processing aligned with study objectives.
Full article

Figure 1
Open AccessArticle
Assessing the Potential of the Cloud-Based EEFlux Tool to Monitor the Water Use of Moringa oleifera in a Semi-Arid Region of South Africa
by
Shaeden Gokool, Alistair Clulow and Nadia A. Araya
Geomatics 2025, 5(2), 18; https://doi.org/10.3390/geomatics5020018 - 2 May 2025
Abstract
►▼
Show Figures
The cultivation of Moringa oleifera Lam. (M. oleifera) has steadily increased over the past few decades, and interest in the crop continues to rise due to its unique multi-purpose properties. However, knowledge pertaining to its water use to guide decision-making in
[...] Read more.
The cultivation of Moringa oleifera Lam. (M. oleifera) has steadily increased over the past few decades, and interest in the crop continues to rise due to its unique multi-purpose properties. However, knowledge pertaining to its water use to guide decision-making in relation to the growth and management of this crop remains fairly limited. Since acquiring such information can be challenging using traditional in situ or remote sensing-based methods, particularly in resource-poor regions, this study aims to explore the potential of using the cloud-based Earth Engine Evapotranspiration Flux (EEFlux) model to quantify the water use of M. oleifera in a semi-arid region of South Africa. For this purpose, EEFlux estimates were acquired and compared with eddy covariance measurements between November 2022 and May 2023. The results of these comparisons demonstrated that EEFlux unsatisfactorily estimated ET, producing root mean square error, mean absolute error, and R2 values of 2.03 mm d−1, 1.63 mm d−1, and 0.24, respectively. The poor performance of this model can be attributed to several factors such as the quantity and quality of the in situ data as well as inherent model limitations. While these results are less than satisfactory, EEFlux affords users a quick and convenient approach to extracting crucial ET and ancillary data. Subsequently, with further refinement and testing, EEFlux can potentially serve to provide a wide variety of users with an invaluable tool to guide and inform decision-making with regards to agricultural water use management, particularly those in resource-constrained environments.
Full article

Figure 1
Highly Accessed Articles
Latest Books
E-Mail Alert
News
Topics
Topic in
Electronics, Geomatics, Remote Sensing, Sensors, Smart Cities, Technologies
Smartphone Positioning, Navigation and Timing: Advances and Challenges
Topic Editors: Vincenzo Capuano, Fabio DovisDeadline: 31 August 2025
Topic in
Forests, Geomatics, Remote Sensing, Sensors
Vegetation Characterization and Classification With Multi-Source Remote Sensing Data
Topic Editors: Baoxin Hu, Linhai JingDeadline: 30 September 2025
Topic in
Geosciences, Minerals, Geomatics
Future Trends in Mapping Potential Zones of Critical Minerals Using Advanced Imagery Techniques
Topic Editors: Amin Beiranvand Pour, Mazlan Hashim, Shojaeddin Niroomand, Basem Zoheir, Jong Kuk Hong, Hojjatollah RanjbarDeadline: 31 October 2025
Topic in
Applied Sciences, Drones, Geomatics, Heritage, IJGI, Remote Sensing, Sensors
3D Documentation of Natural and Cultural Heritage
Topic Editors: Lorenzo Teppati Losè, Elisabetta Colucci, Arnadi Dhestaratri MurtiyosoDeadline: 1 December 2025

Conferences
Special Issues
Special Issue in
Geomatics
The State-of-the-Art of Critical Infrastructures (CI) Monitoring/Protection
Guest Editors: Salvatore Stramondo, Maurizio PollinoDeadline: 31 October 2025
Special Issue in
Geomatics
Environmental Features Assisted Satellite Navigation
Guest Editor: Guohao ZhangDeadline: 23 January 2026
Special Issue in
Geomatics
Advances and Innovations in Geomatics: Celebrating a New Chapter—First Impact Factor and CiteScore Received
Guest Editors: Enrico Corrado Borgogno Mondino, Caterina BallettiDeadline: 15 June 2026