Applied Geomatics最新文献

{"title":"A geospatial information-based framework of emergency services for the tourist spots of Haridwar city","authors":"Pankaj P. Tasgaonkar,&nbsp;Rahul Dev Garg,&nbsp;Pradeep Kumar Garg,&nbsp;Kavach Mishra","doi":"10.1007/s12518-026-00786-z","DOIUrl":"10.1007/s12518-026-00786-z","url":null,"abstract":"<div><p>Haridwar is a major pilgrimage destination in India that experiences substantial tourist influx, particularly during events such as the duodecennial <i>Kumbh Mela</i>, the <i>Ardh Kumbh Mela</i> in 6 years and the annual <i>Char Dham</i> and <i>Kanwar Yatra</i> pilgrimages. This large congregation of devotees and vehicles poses challenges for emergency response and urban safety. A geospatial information-based framework has been presented here to evaluate the accessibility and response efficiency of emergency services across nine major tourist locations in the city. Geographic information system-based network analysis, including closest facility and service area modelling, was employed to estimate travel time, distance, and optimal routes to hospitals, police stations, and fire stations. Results indicate that most tourist locations are within 4 km and approximately 5 min of the nearest hospital, while police and fire services provide coverage within a 10-minute response threshold. However, locations such as Maa Chandi Devi Temple and Vaishno Devi exhibit relatively higher response times, highlighting spatial disparities in emergency accessibility. The findings demonstrate that the overall emergency coverage is adequate, certain high-altitude and peripheral tourist zones require improved preparedness and strategic planning. The proposed framework provides a decision-support tool for enhancing emergency response and safety in tourism-intensive urban environments.</p></div>","PeriodicalId":46286,"journal":{"name":"Applied Geomatics","volume":"18 4","pages":""},"PeriodicalIF":2.5,"publicationDate":"2026-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148985355","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Spatio-Temporal Dynamics of Land Surface Temperature Across the Indus Plain, Pakistan: A Satellite Remote Sensing Approach","authors":"Hafsa Noor,&nbsp;Shakeel Mahmood","doi":"10.1007/s12518-026-00800-4","DOIUrl":"10.1007/s12518-026-00800-4","url":null,"abstract":"<div><p>The Indus Plain, characterized by subtropical and continental climates, experiences notable variations in land surface temperature influenced by anthropogenic activities and climate change. This study examines the trends in land surface temperature (LST) across different latitudes of the Indus Plain using available MODIS Global dataset from 2000 to 2022. Through raster-to-vector conversion, boundary delineation, and image clipping, the study preprocesses data. Utilizing the LST Model equation, temperatures are computed, and statistical analysis discerns trends. Spatial interpolation, particularly the Inverse Distance Weighted (IDW) method of spatial interpolation, aids in pattern generation. The analysis reveals a significant increase in both annual and seasonal land surface temperatures over higher latitudes, while lower latitudes in Pakistan exhibit a slight decreasing trend. The overall temperature rise amounts to 0.39 °C over the past two decades. These findings hold crucial implications for urban management and decision-making processes, particularly in land-use planning to mitigate rising surface temperatures and foster sustainable cities. Furthermore, understanding the spatial patterns of temperature variation aids in resource management and enhances weather forecasting capabilities. Leveraging land surface temperature analysis contributes to better assessments of water availability, soil moisture, and evapo-transpiration monitoring, thus informing adaptive strategies for climate resilience in the region. The LST dynamics and their implications for environmental management and urban planning, offering insights for sustainable regional development strategies.</p></div>","PeriodicalId":46286,"journal":{"name":"Applied Geomatics","volume":"18 4","pages":""},"PeriodicalIF":2.5,"publicationDate":"2026-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s12518-026-00800-4.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148985341","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Integrated field-based and geospatial evaluation of river bank erosion susceptibility mapping using AHP and BEHI: Insights from the lower Kangsabati Basin, West Bengal, India","authors":"Swapenendu Lodh,&nbsp;Ujjwal Bhandari,&nbsp;Bappaditya Koley","doi":"10.1007/s12518-026-00791-2","DOIUrl":"10.1007/s12518-026-00791-2","url":null,"abstract":"<div><p>Riverbank erosion represents a major fluvio-hydrological hazard that threatens the socio-economic wellbeing and environmental integrity of floodplain communities. This study assesses riverbank erosion susceptibility in the lower Kangsabati River Basin, West Bengal, India, using an integrated geospatial framework that combines the Analytic Hierarchy Process (AHP) and the Bank Erosion Hazard Index (BEHI). Thirteen physiographic and anthropogenic factors—including slope, geology, drainage density, soil characteristics, land use, and vegetation indices—were incorporated to construct an AHP-based susceptibility model, while BEHI scores were calculated from field investigations at 27 streambank sites based on bank height ratio, root depth and density, surface protection, and bank angle. The results indicate that 20 villages fall within high to extreme erosion-susceptibility zones according to the AHP model, whereas 19 villages are classified similarly using BEHI, with locations such as Kuchiar Mahar, Bhatpara, and Hatihalka identified as particularly vulnerable. The findings demonstrate the effectiveness of integrating AHP and BEHI for delineating erosion-prone areas and provide valuable insights for decision-makers to prioritize mitigation measures, policy interventions, and sustainable riverbank management strategies. Overall, the proposed framework underscores the importance of integrating remote sensing, GIS, and field-based assessments to enhance erosion hazard mitigation and inform future research on the socio-environmental consequences of riverbank instability.</p></div>","PeriodicalId":46286,"journal":{"name":"Applied Geomatics","volume":"18 4","pages":""},"PeriodicalIF":2.5,"publicationDate":"2026-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148985342","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Road-effect-zone monitoring of Himalayan highway expansion using Google Earth Engine: LULC transitions & PCA-based environmental quality","authors":"Bharti Bharti,&nbsp;Rajeshwar Singh Banshtu","doi":"10.1007/s12518-026-00790-3","DOIUrl":"10.1007/s12518-026-00790-3","url":null,"abstract":"<div><p>Mountain highway expansion can accelerate land conversion and alter near-surface environmental conditions in steep Himalayan valleys. This study assessed land use and land cover (LULC) transitions and environmental quality within a 2-km road-effect zone along the Kiratpur–Manali highway corridor in the western Himalaya using season-matched Landsat Collection 2 imagery for 2015, 2020, and 2025. Five LULC classes—water, built-up land, vegetation, barren land, and snow—were mapped in Google Earth Engine using a Random Forest classifier. Overall accuracy ranged from 0.82 to 0.91, with kappa values of 0.75–0.88. Between 2020 and 2025, built-up land increased by 87.02 km², while vegetation and barren land declined by 50.51 and 41.82 km², respectively. The main gross inflows to built-up land were vegetation-to-built-up (75.31 km²) and barren-to-built-up (58.82 km²) conversions. Environmental quality was evaluated using a principal-component-weighted Environmental Quality Index (EQI) integrating the normalized difference vegetation index, normalized difference moisture index, land surface temperature, slope, and elevation. EQI declined in 2020 and improved in 2025, when Good and Excellent classes covered 79.32% and 5.75% of the study area. This improvement reflects the selected satellite-derived surface indicators and does not demonstrate field-confirmed ecological recovery. The 0–250 m band consistently had the lowest mean EQI, while the broader 0–2 km response was nonlinear. Hotspot screening identified 11.48 km² of major built-up-transition hotspots during 2015–2020. Linking specific LULC transitions with EQI change provides a reproducible basis for mitigation prioritization and corridor-scale environmental planning.</p></div>","PeriodicalId":46286,"journal":{"name":"Applied Geomatics","volume":"18 4","pages":""},"PeriodicalIF":2.5,"publicationDate":"2026-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148986561","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Exploratory ecological-stress screening for regional planning: a quality-controlled Landsat-based mRSEI analysis of Sakarya Province, Türkiye (2000–2025)","authors":"Mustafa Ergen","doi":"10.1007/s12518-026-00789-w","DOIUrl":"10.1007/s12518-026-00789-w","url":null,"abstract":"<div><p>Regional averages can conceal localized ecological deterioration in rapidly transforming peri-urban landscapes. This study presents a quality-controlled remote-sensing workflow for screening ecological stress across Sakarya Province, Türkiye, using six benchmark-date Landsat composites (2000, 2005, 2010, 2015, 2020 and 2025). After raster alignment and quality control, the Enhanced Vegetation Index (EVI) was excluded because the 2025 layer differed substantially in spatial resolution and value distribution from the preceding series. A modified Remote Sensing Ecological Index (mRSEI) was therefore constructed from pooled, commonly normalized NDVI, NDMI, inverse NDBI and NBR layers using one fixed-loading principal component model. The first component explained 93.75% of pooled variance. Mean mRSEI increased from 0.609 in 2010 to 0.688 in 2015 but declined to 0.595 in 2025; the sensor transition and six-date design prevent this pattern from being interpreted as an unambiguous ecological trajectory. Pixel-wise Sen slope and Mann-Kendall tests indicated significant positive, significant negative and non-significant mRSEI trends over 4.83%, 1.10% and 94.08% of the common valid area, respectively, before multiple-testing correction. No trend remained significant after Benjamini-Hochberg false-discovery-rate control. A conservative Exploratory Ecological Stress Synthesis (EESS) classified 97.67% of the valid area as Low, 1.54% as Moderate, 0.57% as High and 0.22% as Very High stress. Because no independent field, cadastral or land-cover validation was available, these classes are modelled screening outputs rather than validated vulnerability zones. They can guide subsequent stratified field sampling, high-resolution image interpretation and planning review, but should not be used directly as statutory zoning boundaries.</p></div>","PeriodicalId":46286,"journal":{"name":"Applied Geomatics","volume":"18 4","pages":""},"PeriodicalIF":2.5,"publicationDate":"2026-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148986368","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Accuracy of satellite positioning using GNSS smartwatches","authors":"Kacper Antoń,&nbsp;Tobiasz Nosidlak,&nbsp;Amgad Abazeed,&nbsp;Kamil Maciuk","doi":"10.1007/s12518-026-00807-x","DOIUrl":"10.1007/s12518-026-00807-x","url":null,"abstract":"<div><p>The integration of Global Navigation Satellite System (GNSS) receivers into consumer-grade smartwatches has accelerated the development of location-based services and pedestrian tracking. However, the kinematic spatial accuracy of these devices remains insufficiently quantified in the scientific literature, particularly across diverse environmental conditions. This study assesses and compares the kinematic 3D positioning accuracy of six mainstream smartwatches (two Garmin 735XT, Garmin 235, Amazfit Bip U Pro, Apple Watch SE 2, and Samsung Galaxy Watch 4). To evaluate environmental susceptibility, a pedestrian field campaign was conducted across two fundamentally distinct environments: a dense urban canyon and an unobstructed open-sky baseline. The smartwatches were evaluated simultaneously alongside a survey-grade Leica GS16 geodetic receiver to establish a highly precise kinematic ground truth. Experimental results confirm severe spatial degradation within the urban canyon, where mean horizontal errors (RMSE) ranged from 6.7 m to 13 m, and vertical deviations reached up to 9 m due to extreme multipath interference. On the other hand, under open-sky conditions, horizontal accuracy improved significantly to 1.5–3 m, with the Apple Watch SE 2 achieving the highest horizontal precision (1.45 m). Furthermore, the Samsung Galaxy Watch 4 demonstrated exceptional open-sky vertical accuracy (0.17 m). The findings indicate that modern smartwatches are highly capable spatial measurement tools in unobstructed environments. However, their high vulnerability to multipath interference currently disqualifies them from applications demanding rigorous spatial accuracy.</p></div>","PeriodicalId":46286,"journal":{"name":"Applied Geomatics","volume":"18 4","pages":""},"PeriodicalIF":2.5,"publicationDate":"2026-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s12518-026-00807-x.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148986560","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Geographical inequalities in land degradation research: evidence from spatio-temporal analysis in Nepal","authors":"Niroj Timalsina,&nbsp;Motilal Ghimire,&nbsp;Nabina Maharjan","doi":"10.1007/s12518-026-00804-0","DOIUrl":"10.1007/s12518-026-00804-0","url":null,"abstract":"<div><p>Land degradation poses a critical challenge to sustainable development across Nepal’s fragile mountain systems. This study presents a spatially explicit synthesis of five decades (1970s–2024) of research, analyzing 764 peer-reviewed articles. The literature is systematically classified across thematic domains (ecological, climate–hydrological, geomorphological, socio-economic, political, and development), geographic units (physiographic zones, provinces, river basins, and regions), and temporal phases. GIS-based spatial digitization, intersection analysis, and zonal statistics are applied to map research density, while statistical tests (Welch’s ANOVA, Games–Howell, Kruskal–Wallis, and two-way ANOVA with effect sizes) evaluate spatial and temporal variations. Results reveal a 27-fold increase in research output, rising from two articles annually before 1993 to nearly 51 per year after 2015. Despite this growth, strong thematic imbalances persist: ecological and geomorphological studies dominate (≈ 60%), whereas socio-cultural, political and development-oriented research remains limited (≈ 19%). Spatial disparities are equally pronounced, with research concentrated in the Siwalik, Bagmati Province, and the Koshi basins. In contrast, western Nepal, including Karnali and Sudurpashchim Provinces, as well as the Middle and High Mountain regions, remains significantly underrepresented. An evident east-west gradient shows eastern regions receiving substantially higher research attention, driven by institutional concentration, donor priorities, accessibility, and established research hubs. Despite alignment with global sustainability and climate frameworks, critical gaps remain. Addressing these requires targeted funding in underrepresented regions, strengthened regional research capacity, long-term high-mountain monitoring, expanded collaborative research in neglected basins, and co-produced research agendas to support balanced, policy-relevant, and climate-resilient land management in Nepal.</p></div>","PeriodicalId":46286,"journal":{"name":"Applied Geomatics","volume":"18 4","pages":""},"PeriodicalIF":2.5,"publicationDate":"2026-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148986562","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Cross-regional assessment of urban flood vulnerability in climate-exposed coastal megacities: Casablanca, Morocco, and Jakarta, Indonesia","authors":"Demba Coulibaly,&nbsp;Yassine Loukili,&nbsp;Younes Lakhrissi,&nbsp;Oumar Konaté","doi":"10.1007/s12518-026-00798-9","DOIUrl":"10.1007/s12518-026-00798-9","url":null,"abstract":"<div><p>Floods rank among the most devastating natural disasters in the world, and their frequency and intensity have increased significantly due to climate change and rapid urbanization. As a result, millions of people are more vulnerable to flood risks, especially in crowded urban areas. This study proposes the development of flood susceptibility maps (FSM) for Casablanca (Morocco) and Jakarta (Indonesia) using geospatial big data and Google Earth Engine (GEE) evaluation. Multi-source geospatial datasets were integrated, including Sentinel-1 radar imagery, Sentinel-2 optical imagery, SRTM elevation data, and CHIRPS precipitation data. Two machine learning models, Random Forest (RF) and Boosted Trees (BT), were applied using flood-conditioning factors including elevation, slope, aspect, Normalized Difference Built-up Index (NDBI), distance to rivers (DTRiver), curvature, topographic wetness index (TWI), spatially distributed influence (SPI), road distance (DTRoad), Annual Max Daily precipitation (AP), and frequency of extreme precipitation events (FP). A total of 553 flood points were randomly distributed across the study areas for model training and validation. Model robustness was evaluated using random 5-fold cross-validation, and the predictive performance was assessed through precision, accuracy, and area under the ROC curve (AUROC). The results indicate approximately 66% of the area falls within the high flood susceptibility class across all models. The BT model consistently outperformed RF. Findings indicate that interregional model performance is influenced by local environmental conditions, and the resulting susceptibility maps are effective tools for flood risk management, emergency preparedness, and mitigation planning. </p></div>","PeriodicalId":46286,"journal":{"name":"Applied Geomatics","volume":"18 4","pages":""},"PeriodicalIF":2.5,"publicationDate":"2026-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s12518-026-00798-9.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148986339","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Land use/land cover classification and prediction using multispectral and hyperspectral imagery: A comparative review with explainable AI","authors":"C Saranya Devi,&nbsp;Manoharan Prabukumar","doi":"10.1007/s12518-026-00784-1","DOIUrl":"10.1007/s12518-026-00784-1","url":null,"abstract":"<div><p>Land Use and Land Cover (LU/LC) classification and prediction using remote sensing play an important role in environmental monitoring, urban planning, climate adaptation, and sustainable resource management. Recent advances in Multispectral Imaging (MSI), Hyperspectral Imaging (HSI), machine learning (ML), deep learning (DL), transformer architectures, and Explainable Artificial Intelligence (XAI) have significantly improved LU/LC analysis. However, existing studies often evaluate sensors, preprocessing strategies, classification methods, prediction models, and validation frameworks independently, limiting methodological consistency and cross-study comparability. This review presents a critical comparative analysis of MSI and HSI-based LU/LC classification and prediction frameworks by systematically examining remote-sensing platforms, preprocessing requirements, ML/DL architectures, prediction models, validation strategies, and XAI integration under heterogeneous experimental conditions. Unlike previous broad survey studies, this review comparatively analyzes how preprocessing design, validation strategy, model complexity, and interpretability collectively influence the operational reliability and transferability of MSI and HSI-based LU/LC workflows. The review demonstrates that MSI datasets are operationally suitable for large-scale and long-term monitoring because of their temporal consistency and standardized workflows, whereas HSI datasets provide improved spectral discrimination for fine-class analysis at the cost of higher preprocessing and computational complexity. The study further highlights that reported performance is strongly influenced by preprocessing design, spatial resolution, training-sample distribution, and validation protocols rather than model architecture alone. In addition, the review emphasizes the growing importance of uncertainty-aware validation and XAI-driven interpretability for improving transparency, reliability, and operational applicability in geospatial AI systems. Finally, current research gaps and future directions for scalable, transferable, and explainable LU/LC frameworks are discussed.</p></div>","PeriodicalId":46286,"journal":{"name":"Applied Geomatics","volume":"18 4","pages":""},"PeriodicalIF":2.5,"publicationDate":"2026-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148927429","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Performance analysis of smartphone gnss mobile applications for fit-for-purpose cadastral surveying","authors":"Oluibukun Gbenga Ajayi,&nbsp;Gideon Abidemi Aleji,&nbsp;Oluwatobi Olalekan Adetunji,&nbsp;Yusuf Drisu Opaluwa","doi":"10.1007/s12518-026-00785-0","DOIUrl":"10.1007/s12518-026-00785-0","url":null,"abstract":"<div><p>The growing need for affordable and accessible geospatial tools in land administration has intensified interest in mobile GNSS applications, particularly for Fit-For-Purpose (FFP) Cadastral Mapping. This study evaluates the positional accuracy of eight selected mobile GNSS applications by comparing their coordinate outputs against reference data obtained from Differential GNSS (DGNSS). Initially, ten applications were tested under similar environmental conditions; however, two were excluded from final analysis due to persistent extreme deviations and irregular positional stability across the survey stations. Their Root Mean Square Error (RMSE) values were significantly higher than those of the other applications and exceeded the acceptable accuracy threshold for FFP Cadastral Mapping. The remaining eight apps were assessed using statistical methods, including the Kruskal-Wallis test, to determine significant differences in positional accuracy. <i>The Mobile Topography application</i> demonstrated the highest accuracy, with a mean deviation of 2.83 m, a 95% confidence interval of 1.57–4.09, and a standard deviation of 1.20 m. Aside from the descriptive statistics, the RMSE was computed to provide an overall measure of positional accuracy relative to the DGPS reference coordinates. <i>Mobile Topography</i> had the lowest RMSE of 3.03 m, falling within the acceptable FFP land administration range of not more than 5 m in rural areas. <i>Polaris</i> and <i>GPS Essentials</i> applications showed moderate reliability, while other applications recorded deviations well beyond acceptable limits, making them unsuitable for boundary-sensitive cadastral tasks. The findings affirm the potential of mobile GNSS apps as cost-effective tools for FFP mapping in resource-limited contexts while also highlighting their variability and the necessity of integrating error-mitigation strategies such as post-processing and field validation. Future research will focus on improving accuracy through hardware optimization, algorithmic corrections, and robust field methodologies to enhance the viability of mobile GNSS in FFP cadastral systems.</p></div>","PeriodicalId":46286,"journal":{"name":"Applied Geomatics","volume":"18 4","pages":""},"PeriodicalIF":2.5,"publicationDate":"2026-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s12518-026-00785-0.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148903295","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}