Jakarta has experienced a land subsidence rate of 3–10 cm per year, intensifying existingenvironmental pressures that gradually shape long-termhydrological responses in the region. Additionally,low infiltration factors, soil compaction, poor drainage systems, and rising sea levels resulted in anincrease in the quantity of flooding in Jakarta, affecting the area of inundation, height of the floodwaters,and duration of flooding in the region. Consequently, flooding continued to recur during the rainy periodwith increasing severity. Based on these facts, efforts are needed to minimize or address the areas affectedby flooding in Jakarta, one of which involves flood identification through the utilization of active remotesensing technology. We utilized Sentinel-1A to generate coherence values based on the InterferometricSynthetic Aperture Radar (InSAR) coherence method. Additionally, we applied Rapid Flood Mappingbased on backscatter values. Our results indicate that the image acquisition period significantly dictatesthe outcomes, with the image pairs of “dry_rain” proving more effective than “rain_rain” in Jakarta’sflood identification. Furthermore, integrating results from both methods improves flood identification, asthey can validate each other. Specifically, the mapped flood extent was influenced by rainfall intensityand the spatial distribution of validation points within the presumed flood zones.
Agriculture is an important sector of Vietnam’s economy, providing livelihoods for 60% of therural population, accounting for 30% of the national workforce, and contributing nearly 12% to the GDP.In the context of rapid developments in science, technology, and digital transformation, the application ofmodern technologies, including Virtual Reality Geographic Information System (VRGIS), in agriculturehas become an urgent issue in Vietnam. This paper presents a technical workflow for developing aVRGIS to support the promotion of agricultural products through immersive 3D environments. Theresearch focuses on rice, a key agricultural product of Hung Yen Province (which has now been mergedinto Hung Yen Province), in northern Vietnam. The proposed method integrates multiple technologiesinto a systematic processing pipeline: (1) aerial image acquisition using drones, (2) 3D reconstructionusing photogrammetry and Blender, (3) scene optimization for real-time rendering, and (4) immersiveenvironment deployment using Unity. The system enables users to virtually explore the ThaiBinhSeed Crop Research Institute in a simulated environment. Experimental results demonstrate that theworkflow can produce highly interactive and realistic 3D models, suitable for digital product promotionand agricultural education. This workflow proves effective in combining open-source solutions withcommercial technologies to facilitate digital transformation in the agricultural sector.
This paper introduces an innovative algorithm, The Curve of Incircles (IncirCu), designedto compute an unbranched subset of a shape’s medial axis (MA). The medial axis itself is a skeletalrepresentation that encapsulates the shape’s interior, making it invaluable in various shape analysisapplications. Traditionally, the medial axis is a branched structure, posing significant challenges forapplications that rely on a single, unbranched curve. This new algorithm effectively overcomes thesechallenges by directly computing ordered set of vertices situated on the medial axis points. Thesevertices can then be transformed into a single polygonal chain that is unbranched and free of loops,effectively eliminating the need for removal of MA branches to obtain a single curve. Consequently, thealgorithm is highly applicable in any context that requires an unbranched input curve, such as cartographyand land surveying. To validate its performance, the results of IncirCu were compared with those ofexisting algorithms such as Straight Skeleton, Medial Axis, and iCMR. Conducted tests showed theaverage distance between the IncirCu output and the reference Medial Axis to be near zero. Whilethe axis comparison against iCMR and the Straight Skeleton revealed differences greater than zero,these discrepancies remained negligible when scaled against the input objects' overall size. It offersa practical solution to a longstanding problem of MA branch elimination, broadening the scope ofpossible applications and enhancing the efficiency and accuracy of shape analysis in various technicalfields connected with civil engineering.
In recent years, there has been growing interest in the use of low Earth orbit (LEO) satellitesfor positioning, navigation, and timing (PNT) purposes. This article presents the results of simulationsof two conceptual LEO-PNT constellations – S2-1 and S2-2 – based on parameters proposed by Teng,et al. (2023), which are an extension of the existing CentiSpace constellation. The aim of the study wasto determine the impact of these constellations on positioning geometry accuracy (DOP coefficients) inopen and obstructed horizon conditions, both in moderate (Poland, latitude = 51◦) and low (Hong Kong,latitude = 22◦) latitudes. The analysis was performed for different orbital inclination angles (31.5◦ and79.1◦) and based on GNSS data and simulated TLE files for LEO-PNT. The results showed that positioningbased solely on LEO satellites is possible in open horizon conditions, while in urban environmentsthe best results were achieved using hybrid GNSS+LEO constellations, which improved DOP factorsby 3–14%. Constellations with higher orbital inclinations provided better coverage and accuracy inmid-latitude regions. Research confirms that the integration of LEO-PNT systems with GNSS cansignificantly improve the quality of positioning geometry, especially in difficult observation conditions,and that optimal orbit design and constellation density are key to achieving high positioning accuracy.
Landslides, the downslope movement of rock and debris, are short-lived but highly destructiveevents, often triggered by reduced shear strength of slope materials. Their frequency is rising globallydue to both natural and human factors that is urbanization, deforestation, and large-scale infrastructuredevelopment. This study area is the Himachal Pradesh, located in the tectonically active area of Himalayas,is among the most vulnerable regions which leads to such hazards. In this study we have applied a spatiotemporalanalysis of landslides using historical records from the Himachal Pradesh State DisasterManagement Authority and satellite imagery. The results show that over 97% of the area is susceptible,with Kullu, Chamba, and Solan districts being most at risk. The Swarghat–Bilaspur stretch of NationalHighway 21 demonstrates landslide activity linked to the Main Boundary Thrust (MBT) and GambharThrust. Both natural drivers – steep slopes, weak lithology, and heavy rainfall – and human activities suchas road building, tunneling, and deforestation intensify slope instability. This research integrates spatialmapping with temporal patterns, offering a comprehensive view of vulnerability hotspots. In HimachalPradesh, districts such as Kinnaur and Kullu have experienced above-normal rainfall correlating withincreased landslide events. Conversely, districts like Mandi, Kangra and Solan, despite below-normalrainfall, continue to report high landslide frequencies, suggesting that short-duration, high-intensityrainfall events – hallmarks of climate change – may be triggering slope failures. The findings highlightthe urgent need for disaster management for such areas and sustainable land-use planning in the region.
Hyperspectral data obtained from unmanned aerial vehicles (UAVs) provide high spectral and spatial resolution, bringing potential for an accurate identification of individual tree species. The aim of this paper was to thoroughly investigate the possibilities of forest tree classification using hyperspectral data taken by the Resonon Pika L camera. Both hyperspectral and ground reference data were collected for a heterogenous forest in the Czech Republic. Standard processing methods (radiometric, atmospheric, and geometric corrections) were applied, followed by testing the methods for reduction (spectral resampling, MNF, PCA) and PPI. The classification phase consists of both unsupervised and supervised approaches, including Maximum Likelihood, Mahalanobis Distance, Spectral Angle Mapper, Minimum Distance, Random Forest, Extra Trees, Support Vector Machines, and Naive Bayes. Within the classical classifiers, the best results were achieved using the Maximum Likelihood classifier. In terms of machine learning algorithms, the best performing classifiers were Random Forest and Extra Trees. The use of Pika L camera in forestry classification is so far minimal, therefore the results can be helpful in potential utilization of this type of camera. The findings of this research not only contribute to a better understanding of UAV-based hyperspectral remote sensing for tree classifications but also provide practical insights and recommendations for improvement.
Surface Coal Mining Areas (SCMAs) refer to regions affected by open-pit coal mining activities, including extraction sites and associated waste disposal zones. This study proposes the methodology for SCMA detecting in Cam Pha and Ha Long regions (Quang Ninh province, northern Vietnam) from remote sensing data based on Google Earth Engine (GEE) cloud computing platform. The remote sensing data used in this study consists of multispectral satellite images from Landsat 8 OLI_TIRS. Processing remote sensing data to detect SCMA includes the following steps: image segmentation using SNIC algorithm (Simple Non-Iterative Clustering), spectral index calculation from Landsat images, threshold-based classification, and spatial analysis to delineate extracting areas (EA) and stripped/dumping areas (SA-DA). The results indicate that Cam Pha has a larger EA (11.98 km2) and SA-DA (17.09 km2) compared to Ha Long (6.36 km2 and 3.59 km2), respectively. In contrast, Ha Long exhibits a higher EA/SA-DA ratio (1.77), suggesting more land reclamation, while Cam Pha has extensive mining waste accumulation (0.70). These findings highlight the severe environmental degradation caused by coal mining, emphasizing the need for sustainable land management. The SCMA detection approach in GEE provides an efficient, scalable method for real-time monitoring, long-term change detection, and environmental restoration planning in mining regions.
Cultural heritage is a non-renewable and irreplaceable resource, and using digital technologies, especially geoinformatics, has become a powerful means of preserving and promoting it. However, integrating digital heritage, which utilizes geoinformation, into sustainable development discussions remains an ongoing challenge. This paper examines geoinformatics technologies applied to critical domains of digital heritage, including documentation, representation, and dissemination. It also explores digital heritage's threefold role in cultural sustainability: how it mediates society, economy, and environment, develops alongside these pillars, and how geoinformatics becomes embedded within digital heritage. The study employs literature reviews and case studies to investigate the integration of geoinformatics into digital cultural heritage, aiming to inform policy and practice in heritage conservation. As a multidisciplinary field, geoinformatics combines geospatial technologies and information science, such as Participatory Geographic Information Systems (PGIS), to manage, conserve, and promote cultural heritage. Despite these technologies' potential, digital heritage faces challenges like data security, interoperability, cost, and accessibility, alongside issues such as application simplification and speed. Opening access to digital heritage and enhancing ease of understanding are also significant obstacles. Integrating geoinformatics into digital heritage is a milestone in cultural heritage conservation. This interdisciplinary approach strengthens documentation, spatial analysis, and public engagement, fostering greater involvement in protecting shared heritage. As geospatial technologies evolve, collaboration among stakeholders is vital to ensure that cultural heritage preservation aligns with the sustainable development of society, the economy, and the environment.
Coastal areas are essential for human welfare but are increasingly vulnerable to environmental and anthropogenic pressures. Accurate shoreline monitoring is for sustainable coastal management. This study undertakes a multi-temporal analysis of the Badagry coastline, Nigeria, from 1982 to 2022, using Landsat (MSS, TM, ETM+), Landsat 8 OLI, and Sentinel- 2A imagery. Shoreline change rates using the Digital Shoreline Analysis System (DSAS) with End Point Rate, Linear Regression Rate, and Weighted Linear Regression models. The analysis reveals a complex dynamic where a net accretionary trend masks a critical hotspot of severe erosion. This erosional pattern is in the central and western portions of the study area, particularly downdrift of the Badagry Creek inlet. The Linear Regression Rate model demonstrated a strong statistical fit to the historical data, with R2 values consistently exceeding 0.9. The LRR indicated a maximum accretion rate of +5.32 m/yr. However, the End Point Rate model captured severe localized erosion, with rates reaching up to –0.65 m/yr in specific hotspots. These findings provide a classic example of an interrupted littoral system, a phenomenon observed globally where coastal engineering structures disrupt natural sediment transport. The results strongly suggest that while natural depositional processes, driven by the regional longshore sediment transport system, are active, localized anthropogenic activities, maintenance, and jetty construction at the Badagry Creek inlet are the primary drivers of significant downdrift erosion. The study emphasizes the need to shift from uniform coastal protection policies to spatially targeted management interventions targeting erosion hotspots to prevent further degradation.
The study examined soil erosion in Ado Odo Ota local government area using the revised universal soil loss equation (RUSLE). This study assessed area vulnerable to erosion by combining five factors: rainfall erosivity, soil erodibility, slope, land use/land cover, control practice. The resultant soil loss map shows that the highest soil loss was 324,522.34t/hr/yr. Furthermore the study reveals that combining the RUSLE model with GIS technology provides the advantage of visualization and statistics which was used to create a map of Ota soil loss. The study shows the area most prone to soil erosion based on the model used. All the factors of RUSLE was taken into account. The result generated from the maps showed that areas such as Ilasa, Iju and Atan are characterized with steeper slope from North Eastern part towards the North West. It is recommended that control measures such as drainage and proper disposal of waste be put in place to avoid loss of live and destruction of property to erosion. The soil loss map can be used by decision makers know the areas susceptible to erosion and also be used as future preventive measures against erosion. The findings of this research emphasizes the need for effective land management and conservation and can be used by decision makers to mitigate the negative effect of soil erosion.
Recent advancements in remote sensing technology have facilitated the acquisitionof images with higher spatial resolution. In response to this rapid technological evolution,the paradigm of OBIA has emerged as a key approach. An essential component of OBIA isimage segmentation, where the careful selection of an appropriate segmentation algorithmand its parameters significantly influences the quality of the segmentation output. This studyaims to conduct LULC analysis on Sentinel-2 imagery and compare the accuracy of theSVM classifier across different segmentation methods produced by MRS, SLIC, Mean Shift,and Quick Shift algorithms. The selected study area is located in the Marmara region ofTurkey and characterized by seven major LULC classes. The segmentation was conductedthough four algorithms, with 60 segment features being extracted for each output, consideringspectral, textural, and geometric attributes separately. Following the classification processwith SVM, overall accuracies of 96.14% for the MRS, 91.00% for the SLIC, 89.95% for theMean Shift and 87.95% for the Quick Shift approach were estimated. These results underscorethe superior performance of the MRS algorithm with significant level of improvement. Thishigh level of accuracy holds significant potential for delivering more dependable and preciseoutcomes in planning and decision-making processes. Moreover, integrating XAI, specificallythe LIME algorithm, enhances the transparency and comprehensibility of classificationanalysis within the OBIA framework. Features associated with the NIR and SWIR bandswere found to have predominantly positive effects. This integration contributes to improvedtransparency, enabling more informed and reliable decision-making processes.
The Gravity Recovery and Climate Experiment (GRACE) and GRACE Follow-On satellite missions have been monitoring hydrological events on Earth’s surface for nearly two decades. Monthly gravity solutions from these satellites are available as Level-2 (L2) spherical harmonic coefficients or as ready-to-use Level-3 (L3) data, typically representing Total Water Storage (TWS) variations. L3 data, such as Goddard Space Flight Center (GSFC) mascon data, include essential corrections like post-glacial rebound and signal-leakage, and precisely represent TWS variations for specific regions, such as river basins, without additional corrections. However, certain geopotential changes, such as groundwater-induced vertical displacements, gravity anomalies and geoid height changes cannot be directly obtained from these data. To evaluate these geopotential changes, L3 data needs to be transformed into harmonic coefficient solutions. While this method is more computationally demanding compared to adapting L2 data with necessary corrections, the question remains: How can L3 TWS data be directly transformed into other potential changes? In this study, we propose a regression approach for the Türkiye region, using approximately hundred GSFC-mascon blocks to convert TWS into groundwater-induced vertical displacements. Transformation parameters are estimated by considering outcomes from L2 data, specifically selecting DDK2- filtered data. The ratio between vertical displacement and TWS for each mascon is modeled by a quadratic function based on TWS magnitudes. Investigating residuals reveals a timedependent pattern, which requires a second regression to model this aspect. This two-step regression approach successfully transforms TWS into vertical displacements, with a rootmean- square error of about half a millimeter, providing satisfactory results for the region.
The relative share of debris-covered glaciers strongly increased in recent decades due to climate change and amplified rock production, but despite their relevance as climate indicators, accurate demarcation of respective glaciers is challenging. Remote sensing is an important tool for glacier mapping, but most existing studies apply medium resolution sensors, which are not suitable for small, rock covered glaciers, or use semi-automatic approaches. We present a simple methodology to automatically derive debris-covered glacier areas by using multitemporal, high resolution UAV images. Thereby, standard products, such as elevation differences calculated from digital surface models and displacement rasters, combined with statistical or error thresholds, provide the basis to automatically delineate glacier areas. The comparison of the automatically derived debris-covered glacier area to the geodetically determined glacier snout showed lowest errors for the elevation-based method using yearly data (total error of 0.32 m or 8.6% of the yearly glacier retreat) and higher errors for four-year intervals (0.97 m, 34% of the yearly glacier retreat) or displacement-based methods (0.51 m, 13.6% of the yearly glacier retreat with yearly epochs). Visual evaluation also showed strong errors of the displacement-based method with many areas wrongly identified as debris-covered glacier area.We conclude that the elevation-based method allows for accurate delineation of debris-covered glaciers and pro-glacial areas, providing increased standardization of glacier monitoring using remote sensing.
The evaluation of the accuracy of generated DEMs using three remote sensing techniques on three types of forest road surfaces was performed. As a sample data, we used the forest road constructed from asphalt, concrete road slabs, and paving stones located in Víglaš, Central Slovakia.We evaluated the vertical accuracy of the DEMs produced by mobile laser scanning (MLS, Leica Pegasus, 840 pts/m2, airborne laser scanning (ALS, Leica ALS 70, 9 pts/m2, and aerial photogrammetry (AP, Leica RCD 30, 5 pts/m2. DEMs were generated in ArcGIS with a final resolution of 0.5m using the IDW method. The accuracy of DEMs was evaluated with the reference dataset on 700 check points. Regarding road surface capture quality, terrain generation, and point density, the MLS method dominates. It provides the RMSE values in range of ± 0.01 m to ± 0.03 m. The ALS method provided balanced RMSE results irrespective of surface type (RMSE ± 0.04 m to ± 0.05 m). The AP has the highest variability on all surface types (RMSE ± 0.12 m to ± 0.22 m). For AP, 0the decimeter-level accuracy is not sufficient for construction and maintenance purposes. This method provided the largest blunders at the road parts closest to the trees. ALS, with its ability to partially penetrate the forest canopy, can provide complex information about forest roads for inventory purposes. MLS provided the best spatial accuracy, enabling both construction and maintenance works. In any case, the advantage is that these data types can be combined.
This study aims to enhance damage detection methods for the agricultural sector in Ukraine, which has been severely affected by ongoing conflict. While existing approaches, such as the method by Kussul et al. (2023), are among the best for monitoring damage, they are limited by the use of static threshold coefficients that can lead to inaccurate results, particularly false positives. To address these issues, we introduce a new approach using Symbiotic Artificial Intelligence (SAI), which integrates human oversight with machine learning to enable real-time adjustments to detection sensitivity based on field-specific characteristics. The proposed SAI-based method was tested using high-resolution satellite imagery from MAXAR for fields in Donetsk and Kherson. Results demonstrated a significant reduction in false positive rates, from 8.5% to approximately 1%, while maintaining a high rate of correctly identified undamaged areas. However, a slight decrease in true positive detections was observed, indicating a necessary balance between false positive reduction and sensitivity to actual damage. The SAI method effectively minimized false detections at field boundaries and other non-damage-related anomalies. This approach showcases the potential of combining human expertise with AI to improve accuracy and adaptability in damage detection. While the results are promising, further research should focus on automating the adjustment of detection thresholds for broader application, such as developing regression models to optimize field-specific coefficients.
Recent advancements in remote sensing technology have facilitated the acquisition of images with higher spatial resolution. In response to this rapid technological evolution, the paradigm of OBIA has emerged as a key approach. An essential component of OBIA is image segmentation, where the careful selection of an appropriate segmentation algorithm and its parameters significantly influences the quality of the segmentation output. This study aims to conduct LULC analysis on Sentinel-2 imagery and compare the accuracy of the SVM classifier across different segmentation methods produced by MRS, SLIC, Mean Shift, and Quick Shift algorithms. The selected study area is located in the Marmara region of Turkey and characterized by seven major LULC classes. The segmentation was conducted though four algorithms, with 60 segment features being extracted for each output, considering spectral, textural, and geometric attributes separately. Following the classification process with SVM, overall accuracies of 96.14% for the MRS, 91.00% for the SLIC, 89.95% for the Mean Shift and 87.95% for the Quick Shift approach were estimated. These results underscore the superior performance of the MRS algorithm with significant level of improvement. This high level of accuracy holds significant potential for delivering more dependable and precise outcomes in planning and decision-making processes. Moreover, integrating XAI, specifically the LIME algorithm, enhances the transparency and comprehensibility of classification analysis within the OBIA framework. Features associated with the NIR and SWIR bands were found to have predominantly positive effects. This integration contributes to improved transparency, enabling more informed and reliable decision-making processes.
The purpose of this paper is analysing the correlation between the magnitude of the annual amplitude of seasonal changes in the coordinate components of GNSS reference stations and the height of the antenna mounting above the ground. For this purpose, the daily coordinate solutions of more than 500 GNSS reference stations that are part of the IGS (International GNSS Service) network were studied due to their distribution across the globe and long operating time, for some stations dating back to the 1990s. To minimize the impact of the tectonic plate movements authors adopted coordinates of reference stations inside each of the 21 tectonic plates. The coordinates in a topocentric reference frame were detrended in accordance with a linear model, with the objective of removing first-order trends. Subsequently, the seasonal yearly functions were calculated for each North, East and Up component. Finally, the amplitude of the seasonal factor for each station was determined. As a result of the analysis, the existence of annual amplitudes of coordinate changes was demonstrated for some of the stations, but no significant correlation between this phenomenon and the height of the GNSS antenna mounting was shown. In the case of the horizontal components, the majority of the station’s time series is characterized by the amplitude of seasonal function does not exceed 2.5–3 mm, and 5 mm for the vertical component.
Over the past decade, object-based image analysis (OBIA) has gained prominence as a widely adopted method for generating land use/land cover (LULC) maps. This study aims to evaluate the performance of various classification algorithms within the OBIA framework using SPOT-6 satellite imagery. The research methodology involved segmenting the images with the multi-resolution segmentation (MRS) algorithm, followed by the application of convolutional neural networks (CNN), random forest (RF), and support vector machine (SVM) algorithms for classification. The study was conducted in the Perpignan province, located in the Pyrénées-Orientales region of France. After the segmentation stage, CNN, RF, and SVM classifiers were employed to classify the image segments based on both spectral and spatial attributes. The accuracy of the resulting thematic maps was assessed using standard metrics, including overall accuracy (OA), the Kappa coefficient (KC), and the F��score (FS). Of the three classifiers, CNN achieved the highest overall accuracy at 91.28%, outperforming SVM, which attained an OA of 90.50%, and RF, which recorded an OA of 87.28%. Additionally, this study explored the integration of explainable artificial intelligence (AI) techniques, specifically the Shapley Additive Explanations (SHAP) algorithm, to enhance the interpretability of the machine learning models. This approach fosters greater trust, accountability, and acceptance in decision-making processes. By leveraging SHAP values, the study provides deeper insights into the decision-making processes of the CNN, SVM, and RF classifiers, ultimately enhancing the transparency and comprehensibility of these models.
The relevance of this work lies in the need to improve height monitoring methods for neotectonics processes in areas with irregular topographic environments and to develop technological requirements to ensure the necessary accuracy and reliability of the results. The purpose of this study is to control subsidence in mining fields within technogenically stressed areas influenced by the Kalush–Holyn potash deposit and to develop a comprehensive methodology for monitoring the network of observation stations. The study includes highprecision measurements of ellipsoidal heights using the Global Navigation Satellite System (GNSS), determination of orthometric height differences based on high-precision geometric leveling, and application of orthometric corrections. At the junction points of the leveling networks, known data on the geological structure of underground layers, the distribution of earth masses, and the measured value of gravity have enabled the determination of orthometric corrections. The methodology employed in the study accounts for changes in the shape of the level surface on technogenic polygons and the heterogeneity of the gravity field. Adherence to the developed technological requirements allows for additional control of monitoring results and ensures an accuracy in height difference determination of no less than 1/1000000. The results of the study demonstrate that independent measurements of orthometric and ellipsoidal height differences facilitate a more precise investigation of geodynamic processes in technogenically stressed areas by calculating vertical line deviations. Thus, the proposed approach to monitoring neotectonics processes can be used to develop effective strategies for monitoring and managing environmental risks associated with geological hazards.
Functioning of maps in different areas of human activity provides the right context for the formation of cartographic functional styles. The community that participates in this map-related activity gains knowledge. When developing a system of cartographic functional styles, it is important to consider how users value cartographic functional styles. The purpose of the research is to evaluate cartographic functional styles based on the results of the target group survey. In order to achieve the goals of the research, the framework of the system of cartographic functional styles has been prepared, consisting of the main components that determine the cartographic functional style. Maps illustrating characteristic features of different styles are included. A questionnaire for the survey was prepared. Stylistic analysis of modern maps, analysis of information sources was carried out during the research. The maps are produced using data from different data sources and GIS software. 4 maps on the current topic of wind energy are the results of this activity. 104 respondents from different fields of activity took part in the survey. The results of the survey show that as many as 85 percents of the respondents basically agree with the statement that maps in general are characterized by a variety of cartographic functional styles. The respondents give high priority to the target audience for which the map is intended in the system of functional styles. Based on the results of the survey, the framework of the system of cartographic functional styles has been specified.
