Applied Geomatics最新文献

The identification and analysis of urban roof materials using remote sensing technologies has attracted growing interest among Earth observation researchers in recent years. A georeferenced dataset has numerous applications in fields such as cadastral management, urban planning, and climate change. In this study, 907 spectral signatures were collected in June 2024 from seven roof materials and four types of urban land covers using Red Tide spectroradiometers (spectral range: 350 to 1000 nanometers) in the municipality of Puerto López, Meta, Colombia. Due to limited prior information, a stratified random sampling design was employed, with strata defined based on spectral variability obtained from an UltraCam Eagle M3 orthorectified image (50-centimeter spatial resolution). Furthermore, a subsampling strategy was also implemented to match the effective measurement area of the building roofs with the field of view of the spectroradiometer. Additionally, a feature selection analysis was performed using the Random Forest algorithm to identify the spectral bands most sensitive to material type, color, and conservation state, in relation to the spectral resolution of the orthorectified image. The results show that, within the spectral range of the instruments used, material color had the greatest influence on the spectral response. However, certain wavelengths in the field spectra exhibited greater sensitivity to material type and conservation state, although these did not align with the image’s central wavelength. Field-collected hyperspectral data is therefore a valuable resource for multispectral image classification, but its acquisition must be supported by accurate and rigorous fieldwork to ensure its effective application.

In the digital AECO (Architecture, Engineering, Construction & Operations) domain, 3D point clouds are, nowadays, an increasingly used data source to derive geometric and semantic information. The fast data collection and completeness as well as the direct digital output are the main advantages with respect to traditional 3D acquisition methods. Portable Mobile Mapping Systems (PMMS) are becoming more and more widespread in the AECO domain due to their flexibility and usability in different conditions. However, point clouds do not directly present any semantic structure making the transformation into informative models a manual and time-consuming task. Recently, Machine Learning (ML) and Deep Learning (DL) frameworks for point-cloud classification and segmentation are gaining momentum to recognize the architectural elements and to speed up the process of geometry reconstruction in the digital environment. In the range of proposed solutions, an end-user perspective analysis in terms of customization, replicability, and user-friendliness is still missing. The purpose of this paper is to partially cope with this gap by testing and comparing different existing ML/DL frameworks for the classification of the point cloud of a historic urban area. To this purpose, two open-source and two commercial software packages were selected, based on the results of the literature review. The article’s perspective was purely linked to the end user. Therefore, although various classification tests were carried out and results were carefully recorded and reported with accuracy and processing time, the analyses mainly focused on the user experience and not on evaluating the performance of the tested systems. The adopted dataset was acquired with a PMMS for the SIFET 2023 Benchmark in the downtown of Arezzo, Italy.

The application of GPS geodesy and MATLAB-based Topo Toolbox geomorphic functions revealed that variable GPS station velocities and fluvial anomalies along and across major thrusts and faults can reflect geomorphic impressions of the active tectonic regime in the Darjeeling-Sikkim Himalaya (DSH). Overall, the displacement behaviour and strain patterns indicate the possibility of future strain release (~ 6.2 to 191.0 µstrain yr^− 1) toward the eastern part of the DSH (mainly along the region of main boundary thrust (MBT), between the western Tista River and southern Rangit River, as high strain accumulation has been found. These strain rates, which represent 0.0062 to 0.191 mm of deformation per metre of rock annually, are indicative of extremely active tectonic zones that have the potential to produce moderate to large earthquakes in the near future, most likely in the Magnitude 6.0 to 7.5+ range. In the study area, K_sn (normalized channel steepness index) was considered the foremost function of recent strain accumulation along the MBT and in zones of high seismicity. The Himalayan River basins (viz., Jaldhaka, Rangit, Rani Khola, Relli Khola, Lachung Chu, Gish, Diana, etc.) exhibited noticeable deviations from the steady-state fluvial regime (χ > 3500 m) and high index values of K_sn (178–307 m^0.9), which had clusters of slope-break knickpoints in response to high uplift rates and high intensities of earthquake epicentres (M_W>5.0).

The environmental impacts caused by resource exploitation are a constant source of concern, as they affect both environmental and human safety. For better protection and management of mine environment, this article has highlighted the importance of remote sensing in pollution monitoring of mine environment. The present investigation assessed the six-year environmental effects of gold mining operations at Bekao, Adamawa, Cameroon. Remote sensing research was used to evaluate the spatiotemporal change of spectral indices and land use and land cover (LULC) by using Sentinel-2 satellite images (2016, 2019, and 2022). Spectral indices were computed and mapped to determine the different impacts of the mining activities. Analysis of land use changes in the study area that savannah increased by 61.42 and 128.34 ha in 2019 and 2022, respectively, forest decreased by 41.62 and 218.01 ha in 2019 and 2022 respectively, Bare soil decreased by around 26.16 ha in 2019 then increased by 41.88 ha in 2022. Surface water increased by 23.66 ha in 2019, then by 17.08 ha in 2022, while the exploited area, which did not exist in 2016, expanded by 10.36 ha in 2019, then by 3.91 ha in 2022. The environmental impacts of mining activities deducted from spectral indices were mainly composed of the destruction of vegetation cover, landscape modification, reduction in the water regime, soil degradation, soil salinization and loss of topsoil. These results revealed significant environmental degradation in the study area due to mining activities. And could provide useful tools for successful environmental monitoring and reclamation work.

This study explores the transformation of land surveying in Tanzania into the broader disciplines of geomatics and geospatial science, while examining the challenges posed by the National Council of Professional Surveyors (NCPS) registration system under the outdated 1977 regulatory framework. Key barriers include centralized governance, administrative inefficiencies, and a lack of recognition for modern geospatial competencies. The study highlights the mismatch between academic curricula and professional requirements, and proposes for inclusive governance, policy reform, and integration of digital geospatial tools to modernize the NCPS registration process. Drawing from global best practices, it offers strategic recommendations to modernize the NCPS and align its registration requirements and system with current land administration needs.

Accurate elevation information is essential for geomatics applications, particularly in high-latitude regions where independent validation of available digital elevation models remains limited. In this paper, absolute vertical accuracy was assessed for ArcticDEM v3, ArcticDEM v4, and Copernicus DEM along public road networks in north-western Russia, using in-situ kinematic GNSS measurements as reference. The evaluation was conducted without local DEM registration, relying on the global or wide-area registration of the datasets. ArcticDEM v4 shows the closest agreement with GNSS elevations, while ArcticDEM v3 and Copernicus DEM exhibit larger departures over complex linear structures such as bridges, dykes, and cut sections. The results confirm the suitability of these datasets for applications requiring vertical accuracy of approximately 5 meters without local registration, and highlight the effectiveness of road-based GNSS surveys for DEM validation.

Mediterranean semi-arid watersheds face severe soil erosion due to heavy rainfall and land use. This research seeks to analyze spatial erosion and sediment movement in the Wadi Cheliff basin in Algeria, with elevations ranging from 2 m to 1,953 m and an average annual rainfall of 600 mm. The Revised Universal Soil Loss Equation (RUSLE), Geographic Information Systems (GIS), and remote sensing (RS) were used to calculate rainfall erosivity (R) using Singh’s empirical formula, soil erodibility (K) from the Harmonized World Soil Database, topographic (LS) factors from ASTER GDEM data, vegetation cover (C) from Landsat 8 Images, and conservation practices (P) based on slope gradients. Sediment delivery ratios (SDR) were derived by comparing modeled soil loss with sediment yields (SY) measured at nine dams and eleven gauge stations. The modeled soil loss varies from 0 to 165 t/ha/yr, with an average of 18.3 t/ha/yr when weighted by area. The pattern is highly skewed, with 80.2% of the basin experiencing very low erosion (< 2 t/ha/yr). In comparison, approximately 9% surpasses 5 t/ha/yr, mainly on steep northern slopes with sparse or seasonal vegetation. Seasonal analysis of the C‑factor highlights that late summer and autumn are the most vulnerable periods, as over 70% of the basin has low protective cover. A comparison between predicted and observed sediment yields at dams and gauges shows strong agreement, with Kling–Gupta efficiency values above 0.75, confirming reliability. Maps that identify erosion hotspots in both spatial and temporal contexts can help prioritize soil and water conservation efforts in Mediterranean semi-arid watersheds, such as Wadi Cheliff.

The use of Unmanned Aerial Vehicles (UAVs) is rapidly increasing, making accurate localization essential for safe and reliable navigation. However, GPS signals can become unreliable or entirely unavailable in urban canyons, mountainous terrain, or environments with intentional interference. While many vision-based navigation methods rely on optical imagery, the potential of UAV-borne Synthetic Aperture Radar (UAVSAR) which performs reliably in all-weather and day–night conditions has received relatively little attention.This study presents a new three-stage geo-localization framework specifically designed for UAVSAR imagery: (1) coarse region retrieval by matching each UAVSAR image against low-resolution satellite tiles, (2) fine image alignment within the selected region using high-resolution reference imagery, and(3) final localization based on affine transformation estimation with compensation for the side-looking geometry of SAR. The feature extraction and matching process is driven by a Convolutional Multi-Scale Network with a ResNet-50 backbone, trained to learn robust, invariant features from image pairs captured under varying illumination, viewpoint, and sensor modality. Keypoints are extracted from intermediate feature maps, and mismatches are removed using adaptive distance filtering and RANSAC-based geometric verification. Experiments on a UAVSAR dataset covering more than 46,000 km² and 12 diverse flight paths demonstrate that the proposed method achieves RMSE values between 1.83 and 2.86 m (average 2.23 m) and yields a high Recall@1 in the coarse-retrieval stage.Compared with existing cross-modality matching approaches, the proposed framework consistently achieves higher retrieval accuracy and demonstrates more reliable operational performance.Training and testing across geographically distinct regions further confirm the strong generalization capability of the method.Overall, the results show that SAR-based visual navigation is a practical and robust alternative for GPS-limited or GPS-denied environments, offering a structured multi-stage pipeline and a cross-modality matching strategy that clearly distinguish it from prior work.

Mangroves provide enormous ecological and economic benefits to humankind. Despite their immense importance, such as providing a natural barrier against coastal erosion, segregating nutrients and sediment, and supporting daily life through food, fodder, building materials, huts, fences, resins, gums, and medicines, the mangrove ecosystem is among the most fragile ecosystems worldwide. In the era of global climate change and rapid population growth, with the associated problems, such as sea-level rise, coastal erosion, increasing temperature, and rapid land-use change, pose significant concern for scientists, management planners, and coastal communities. In such circumstances, mangrove research and management to safeguard these precious resources are the need of the hour. Therefore, the present review focused on geospatial studies of mangrove vulnerability around the World. 1577 mangrove and 146 mangrove vulnerability-related Scopus-indexed articles (1972- till date) were consulted, and the analysis was done with VOSviewer software. An extensive literature review was conducted, which included articles, conference papers, and reviews. Therefore, this review specifically concentrates on geospatial studies of mangrove vulnerability around the World. Major research work in this particular area has been represented graphically, highlighting thematic clusters, influential topics, and major challenging issues. The critical issues and areas necessitating vulnerability studies have been identified, which have significant implications for assessing vulnerability measures. Our findings reveal that research articles oriented from the USA, Australia, UK, China, and India are the most cited. In terms of mangrove vulnerability articles, India, the United States, Australia, and Brazil are among the top-ranked countries. Mangrove mapping and vulnerability-related research have progressed significantly since 2010, and Global-level mangrove mapping has remained a key area of research. The use of high spatial resolution satellite imagery and the application of modern techniques, such as machine learning, deep learning, fuzzy logic, support vector machines, and object-based image classification, is an evolving trend. The analysis of studies on mangrove vulnerability reveals that several commonly used vulnerability-related keywords are present, including rising sea levels, Coastal erosion, rising sea surface temperatures, Tsunamis, and Natural disasters. The results indicate a lack of studies on the causes and consequences of mangrove loss in Southeast Asian countries. The focus should be on vulnerability studies within the current global change regime, and further climate research is necessary to determine the impact of climate change on mangroves.

Classical urban economic theories, which prioritize centrality for hotel location, are often directly applied in the Global South despite fundamental differences in urban dynamics. The practice creates a crucial research gap, as the suitability drivers in rapidly urbanizing secondary cities remain poorly understood, leading to suboptimal investment and unsustainable sprawl. The study moves beyond traditional frameworks to develop and validate a geospatial model that captures context-specific factors influencing hotel development in a typical secondary city. We hypothesize that land availability supersedes central proximity as the primary determinant. The study integrated Geographic Information Systems (GIS) with the Analytic Hierarchy Process (AHP) in a multi-criteria decision analysis (MCDA) for Minna Metropolis, Nigeria. Five criteria, Land Use/Land Cover (LULC), proximity to major roads, distance from existing hotels (market proximity), slope, and elevation, were standardized and weighted based on input from a panel of urban planning and hospitality experts. A weighted overlay analysis determined the results. In ArcGIS 10.8, generate a site suitability map that validates against existing hotel locations. The model identified 54.24 km² (38.2% of the study area) as Highly Suitable, with a significant concentration in the northern sectors. The AHP analysis revealed that Land Use and Land Cover (LULC) was the dominant factor, accounting for 46.2% of the weight, significantly surpassing market proximity, which contributed 25.8%. The evidence suggests that the availability of developable land drives growth more strongly than a central location. The model’s robustness was confirmed, with 86.7% of existing hotels located in predicted suitable zones. The challenges posed by land availability limit the direct application of centrality-based models in Bid-Rent Theory and in contexts of rapid urbanization. The study provides a replicable, data-driven decision-support tool for planners and investors. Its theoretical contribution is the dynamic nature of location factor hierarchies, advocating for a strategic shift towards polycentric urban development in secondary cities of the Global South.