Applied Geomatics最新文献

Point bars, essential geomorphic elements in meandering rivers, influence sediment dynamics and ecological processes but face escalating threats from climate variability and human activities in the Niger Delta. This study investigates the 50-year evolution (1974–2024) of point bars along the Niger River to assess their spatiotemporal changes and driving mechanisms using remote sensing and machine learning. Using Landsat and Sentinel-2 satellite imagery, digital elevation models (DEMs), and rainfall data, we applied Object-Based Image Analysis (OBIA) and Support Vector Machines (SVMs) for automated classification and mapping of river features. Temporal trends were evaluated through decadal statistical metrics, spatial autocorrelation (Moran’s Index), and correlation analyses between point bar morphology, rainfall, and elevation. Results reveal a fluctuating Niger River area, declining from 46,376.54 km² (1974) to 44,796.47 km² (2024), with intermittent expansion (49,601.2 km² in 2014). Point bar area surged from 1,945.63 km² (1974) to 8,087.89 km² (2024), peaking at 7,026.33 km² (2004) before contraction, linked to sediment supply shifts. Morphological analysis highlighted elongated bars (aspect ratio 54.22) indicating lateral accretion, contrasting with rounded forms (ratio ~ 20.8) in depositional zones. Spatial autocorrelation confirmed clustering with elevation (Moran’s Index = 1.06, p < 0.001), while rainfall exhibited a dominant correlation (R² = 0.9921) with bar dynamics. These patterns mirror global systems like the Mississippi and Amazon, underscoring synergistic climatic and anthropogenic impacts. The study provides a framework for adaptive management of the Niger Delta and analogous fluvial environments, emphasizing the critical role of hydrological and sedimentological monitoring.

A 3D layout provides a comprehensive representation of spatial arrangements within a room, offering insights critical for architectural planning, augmented reality, and various computer vision applications. Panorama images, capturing a 360-degree view, hold immense potential for scene understanding due to their immersive nature. However, extracting detailed 3D layouts from such images remains a complex task. To overcome this, we propose a novel algorithm for the estimation of 3D indoor layouts using a single panorama image. Our contributions are dual-fold. Firstly, we introduce the ResNet-50 based corner detection algorithm, which empowers precise corner localization within the confines of a single panorama image. This algorithm’s versatility is evident in its effectiveness across a wide spectrum of indoor layouts, accommodating diverse architectural designs such as cuboid and non-cuboid rooms. Secondly, through comprehensive experimental validation, we highlight the algorithm’s superior performance in terms of accuracy, underlining its potential in refining scene comprehension within panorama images. Furthermore, by integrating the corner detection algorithm with a 3D ray tracing technique, we establish an integrated solution for reconstructing 3D indoor layouts. This integrated approach offers a holistic solution for translating the detected corners into an accurate 3D layout reconstruction. Our results validate the algorithm’s impressive accuracy and speed, advocating for its applicability in practical scenarios. With its good accuracy and speed, this approach holds great promise for advancing both scene understanding and 3D modeling within the exciting realm of panorama-centric indoor environments.

Bridges require data-driven management systems to ensure their prolonged operation, as such frameworks support the maintenance of transportation infrastructure. The entire lifecycle of a bridge depends on Geographic Information Systems (GIS), which utilize spatial analysis to perform essential functions. This review scrutinizes 600 publications indexed in Scopus from 2006 to 2023, aiming to investigate GIS applications in bridge management. The selection process was designed to be reproducible, utilizing searches within the Scopus database in January 2024 with the query TITLE-ABS-KEY ((“geographic information system*” OR GIS) AND bridge*), to retrieve relevant publications from titles, abstracts, and keywords. The focus was on peer-reviewed articles, reviews, and conference papers written in English within pertinent fields. The timeframe from 2006 to 2023 was selected due to its reflection of GIS technology’s evolution into an indispensable tool for civil infrastructure operations. VOSviewer software generated two types of networks, unveiling five thematic clusters: spatial data collection methods, geospatial analysis and decision-support systems, visualization techniques, Building Information Modeling (BIM), Structural Health Monitoring (SHM), and remote sensing integration. The results indicate a growing scholarly interest in bridge management, as evidenced by an increase in publications, and highlight leading researchers and institutions, alongside the expansion of international research collaborations. Moreover, the study identifies three significant knowledge gaps: AI-based spatial modeling, GNSS-based real-time monitoring, and IoT-based predictive maintenance systems. Currently, the literature lacks a dedicated bibliometric or scientometric analysis of GIS applications in bridge management, as most reviews concentrate on BIM–GIS or SHM systems rather than GIS-based workflows for bridge management. This research provides valuable insights into the intellectual landscape, while also emphasizing unexplored areas and proposing practical pathways to enhance bridge asset management through data-intensive methodologies.

Spatial analyses of geographic data and information provide valuable insights for decision-making and enable understanding of the distribution of geographically localized phenomena. This research aims to explore the spatiotemporal dynamics of burned areas in the Caatinga Biome and their relationship with land use and land cover from 1985 to 2023. The analysis is based on Geographic Information Systems (GIS), integrating burn scar data with land use and land cover classifications. The main results revealed that fires occurred annually in natural vegetation formations, Savanna Formation, Forest Formation, and Grassland Formation (in this order), followed by land use classes such as Mosaic of Uses and Pasture Areas. However, it is not possible to conclude that the fires originated in natural vegetation. Burned areas within natural regions may have originated from anthropized areas, such as pastures in their immediate surroundings. The recurrent occurrence of fires in natural vegetation areas, potentially triggered by adjacent anthropized zones, highlights the need for preventive actions in transition areas between human-modified landscapes and native ecosystems. The annual variation patterns of burned areas remained consistent and persistent, although fluctuations were observed in pasture areas across all analyzed periods.

This study semi-automatically detects road lines using object-based classification methods from orthophoto produced from UAV images. Three studies were carried out on images with visible and infrared wavelengths. The study's major aim is to examine the effect of spectral bands and different areas on classification accuracy. First, orthophotos were produced from UAV images of different areas and an object-based classification algorithm was used to detect roads. Then, statistical comparisons were made and, user accuracy in the three study regions ranged from 85 to 91%. Overall accuracies were calculated between 0.819 and 0.889, and the results were within the confidence interval.

Vertical Control Networks are critical in establishing Geodetic Reference Frames at both local and regional levels. The fundamental inputs for propagating geopotential differences, which help determine physical heights essential for the vertical components of these frames, are geometric leveling and gravity observations. In this study, we apply orthometric and normal corrections to the Vertical Control Network of Ecuador to obtain height differences that account for the effects of gravity. Since not all level references within the Vertical Control Network have corresponding gravity observations, we derive gravity values for calculating orthometric corrections through interpolation and Global Geopotential Models. Finally, we analyze the results by calculating the misclosure errors of the network loops. This analysis considers the impact of orthometric and normal corrections and variations in the gravity sources.

Urban expansion in the aftermath of natural disasters presents critical challenges to sustainable land use planning and environmental resilience. This study models post-disaster urban growth trajectories by analysing land use and land cover (LULC) changes from 2017 to 2025 and simulating future development scenarios up to 2040 using an Artificial Neural Network (ANN) integrated within the QGIS MOLUSCE module. Sentinel-2 satellite imagery and digital elevation models were employed to classify six LULC categories: built-up areas, croplands, rangelands, bare land, forested areas, and water bodies. The methodology was applied to the Central District of Elazığ, Türkiye a region significantly affected by the 2020 earthquake. Results reveal a substantial 60.89% increase in built-up areas, primarily driven by rapid post-disaster reconstruction. This expansion has coincided with notable reductions in cropland and rangeland coverage, while forest and water body areas have increased due to afforestation projects and water-related infrastructure investments. Scenario-based projections indicate that, if current urbanisation trends persist, pressure on ecologically sensitive and agriculturally valuable lands will likely intensify by 2040. The ANN model demonstrated high predictive accuracy, with an overall correctness of 97.52% and a Kappa coefficient of 0.96. Based on these findings, the study recommends integrated planning strategies that: (i) prevent development on fertile agricultural plains, (ii) incorporate ecological thresholds in urban site selection, (iii) avoid construction near seismic fault lines and water bodies, and (iv) promote green infrastructure solutions including ecological corridors, urban forests, and sustainable stormwater systems within post-disaster urban development frameworks.

Spatial information is critical to supporting the “Precision Village Data” concept, as accurate village-level spatial data enhances governmental decision-making and delivers various benefits. However, acquiring precise spatial data in developing countries such as Indonesia remains challenging due to outdated base maps and inconsistent reference frameworks. In response, Indonesia’s One Map Policy aims to resolve these issues. Unmanned Aerial Vehicles (UAVs) have emerged as a promising alternative for photogrammetric mapping. Modern UAVs can perform direct georeferencing to attain high levels of mapping accuracy. This study evaluates UAV-based georeferencing techniques for precision mapping in village-level spatial planning as part of implementing Indonesia’s One Map Policy. The novelty of this research lies in the comparative assessment of two UAV-based georeferencing systems (Emlid and Pixhawk) across a village-scale area of 140 hectares—larger than most comparable studies (< 100 hectares)—under identical operational conditions. Specifically, this study compares and evaluates the accuracy of two widely used georeferencing systems: Emlid and Pixhawk. The results indicate that while direct georeferencing without ground control points (GCP) achieves acceptable accuracy for medium-scale mapping, only the Emlid + GCP configuration meets Class I precision standards (< 0.1 m Circular Error at 90%). While using all available GCPs remains critical for high-precision applications, Emlid provides a more practical solution in contexts in which installing numerous GCPs is not feasible. This research contributes to optimizing UAV mapping techniques to meet Class I precision standards (1:5,000-scale maps), thereby supporting the achievement of the Sustainable Development Goals (SDGs) in rural areas through the implementation of “Precision Village Data.”

Augmented Reality (AR) offers new opportunities for engaging audiences with cultural and scientific heritage. This study investigates the use of AR in astronomy education through the Copernicus Garden mobile application, developed for a weeklong event at the Olsztyn Planetarium. The research aimed to assess user engagement, perceived usefulness, and interface usability when interacting with AR-based representations of historical astronomical instruments. Data were collected from 767 participants using a mixed-methods approach: telemetry logs from the application, post-visit surveys, and in-depth interviews. Telemetry revealed that 43% of total usage time was dedicated to AR scenes, indicating high levels of interaction with 3D models such as the aerial telescope and solar quadrant. Survey responses confirmed positive perceptions of AR’s usefulness for learning, while interviews highlighted usability challenges related to device performance, scanning instructions, and connectivity. The findings suggest that AR can enhance visitor engagement and support informal science education, although evidence of direct learning outcomes remains limited. We also identify key design considerations for scalability, including simplified modes for low-spec devices, offline caching, and improved onboarding tutorials. This study contributes to research on immersive heritage interpretation by providing empirical evidence from a real-world deployment. Limitations include the short-term scope of the event and the reliance on engagement metrics rather than controlled learning assessments. Future work should extend to longitudinal studies and integration of AR into permanent cultural programming.

The expansion of the raft and aquaculture has substantially impacted worldwide seafood production in the 21st century. Aquaculture monitoring serves to identify both the progress of aquaculture development and water quality status while preventing environmental issues related to pollution or habitat destruction. Nowadays, deep learning models using optical and synthetic-aperture-radar remote sensing images makes monitoring and identifying raft aquaculture possible. This study aims to test DL models based on U-shaped and DeepLab architectures for detecting different types of raft aquaculture using optical and SAR data obtained from Sentinel sensors. Various options to optimize both models related to input fusion data, sub-input sizes, and model structure were applied. As a result, the U- shaped models using the Sentinel-2 images have higher performance in detecting rafts and floating rafts than the models using DeepLab and Sentinel-1 data. Optimal models can detect rafts with accuracy higher than 94% and F1 higher than 80%. The models using Sentinel-1 can only recognize integrated rafts, whereas Sentinel-2 models can distinguish both integrated and bamboo ones. The Sentinel-2 has a high level of detail in spatial resolution, allowing for highly accurate identification. On the other hand, Sentinel-1’s SAR technology is very effective in any weather conditions, making it highly important for uninterrupted surveillance and suitable for real-time monitoring. Integrating these data types requires careful consideration of operational and environmental factors to identify drifting fish rafts. The best model was used to track Vietnamese aquacultural zones throughout seasons and years and may be valuable for coastal managers.