Search Results (275)

The “stereo height assignment method” developed for NASA and NOAA for GOES-R ABI Atmospheric Motion Vectors (AMVs), a purely geometric method using the parallax observed from different satellites, has been included in the NWCSAF AMV product (NWCSAF GEO-I HRW, High Resolution Winds) as an additional height assignment option for AMVs in the region jointly observed by MTG-I and GOES-East over the Atlantic Ocean. Stereo and the alternative non-stereo height assignment (“Cross Correlation Contribution (CCC)”) are compared with ECMWF ERA5 reanalysis winds and EarthCARE ATLID Mie Attenuated Backscatter (MAB) curtains. For low-level clouds, both methods generally conform well with apparent cloud tops in MAB curtains. For higher clouds, more differences are seen between stereo and non-stereo heights. Compared with ERA5 winds, stereo shows the most improvement above 6 km. However, the stereo method produces 70–90% less AMVs since additional high-quality matches are needed from both FCI and ABI imagery. An updated HRW will be released to NWCSAF users in 2027 as version “NWCSAF GEO-I v2027.” The combined provision of both stereo and non-stereo CCC height assignment methods will enable further studies (already planned) of the AMV best-fit level for different satellite channels and cloud types, heights and depths. Full article

In Deep Learning Remote Sensing, data quantity is rarely the limiting factor. A single high-resolution satellite image can yield thousands of training patches. What determines model performance, yet remains largely overlooked, is the quality of those patches. To date, the choice of sampling method has rarely been treated as a methodological decision. Conventional approaches, namely sliding-window and random sampling, introduce two compounding data-quality problems: severe class imbalance caused by the overproduction of background-only patches and negative learning arising from incomplete annotations, where unlabeled objects are implicitly treated as negative examples during training. To address these limitations at the data construction stage, we propose object-centric patch sampling, a model-independent strategy that anchors each training patch to the geometric centroid of an annotated object. This design ensures that every object-anchored patch contains at least one target instance and substantially reduces exposure to unlabeled regions that generate false-negative supervision signals; only a small, deliberately controlled proportion of background-only patches is retained to preserve contextual variety without reinstating background dominance. The method is evaluated on three heterogeneous remote sensing datasets spanning satellite (Sentinel-2, 10 m), aerial (NAIP, 1 m), and UAV (0.25 m) imagery, covering cotton field segmentation, rural building extraction, and water body delineation, respectively. Using a U-Net architecture under identical training conditions, the proposed approach achieves IoU scores of 0.929, 0.896, and 0.912 on the three datasets, respectively, outperforming sliding-window sampling by up to 19.6 percentage points in IoU and consistently delivering higher F1-scores across all experimental configurations. Evaluation under DeepLabV3+ gives a mean IoU of 0.9504 and a mean F1-score of 0.9772 in a multi-class segmentation task, indicating that the gains are not specific to a single architecture. Unlike model-level solutions such as focal loss or class reweighting, the proposed method improves training data quality at its source and integrates seamlessly into any deep learning pipeline without architectural modifications. Full article

Remote sensing is widely recognized as a key technology across a wide range of technical and scientific domains, especially in agriculture. Although satellite data have long supported crop monitoring, their limitations in spatial resolution, revisit frequency and cloud coverage have often constrained their applications. High-resolution satellites, available from the beginning of the 2000s, have improved performance, particularly in the field of precision agriculture, but they remain expensive and inflexible. Unmanned Aerial Vehicles perform better in precision agriculture, offering flexibility and high levels of detail; however, their limited operational areas and short endurance flight times constrain their effectiveness. In this evolving landscape, High Altitude Pseudo Satellites (HAPSs), particularly high-altitude balloons, are emerging as a promising new technology that could fill the gaps between satellite and drone remote sensing. These platforms provide large area coverage with high-resolution imagery and long endurance flights at low operational expenses and ease of deployment. This study investigates the operational characteristics, strengths, and geometric limitations of data acquired by the CubeHAPS^® platform, a high-altitude pseudo-satellite system, as a prerequisite for its application in precision agriculture. Focusing on experimental campaigns conducted in northern Italy in summer 2024 and 2025, the research characterizes platform stability, image block consistency, and photogrammetric quality through internal metrics. The results demonstrate measurable improvements between the two campaigns, attributed to the introduction of a stabilization system in 2025 and establishing the conditions under which the platform can support reliable photogrammetric reconstruction. Full article

High-resolution satellite imagery has substantially improved the monitoring of vegetation dynamics; however, the influence of spatial resolution and pixel aggregation on NDVI time series consistency remains insufficiently quantified, particularly across multiple native resolutions within a single sensor platform. This study evaluates how Sentinel-2 spatial resolutions (10 m, 20 m, and 60 m) and two pixel aggregation methods (pure-pixel and centroid) affect NDVI time series in 14,031 grassland plots across mainland Spain over the period 2018–2023. High-quality NDVI time series were selected using the Interpolation Efficiency Indicator (IEI), and discrepancies relative to a 10 m pure-pixel baseline were quantified through the Time Series Angle Distance (TSAD) and Root Mean Square Error (RMSE). A sensitivity check confirmed that the radiometric differences between Band 8 (10 m) and Band 8A (20/60 m) introduce negligible bias compared with genuine spatial-resolution effects. Formal non-parametric statistical testing—omnibus Kruskal–Wallis with epsilon-squared (${\epsilon }^2$) effect sizes and pairwise Cliff’s Delta comparisons—was applied to assess the magnitude and practical significance of the observed differences across plot area categories and Köppen climate groups (B, Cs, Cf). Results show that coarser resolutions (60 m) substantially reduce NDVI reliability, excluding more than half of the plots under the pure-pixel criterion and smoothing temporal variability, whereas 10 m and 20 m resolutions preserve most spectral and temporal information. The 20 m resolution introduces moderate but non-severe phenological distortion (median TSAD ≈ 0.05 rad, RMSE ≈ 0.026) with a 78% reduction in data volume and 72% reduction in processing time. The choice between pure-pixel and centroid sampling has negligible impact at 10–20 m but becomes relevant at 60 m, where pure-pixel selection reduces errors from spectral mixing at the cost of severe sample attrition. Parcel area strongly conditions the error metrics, with large effect sizes (${\epsilon }^2=0.273$) in the smallest plots, while Köppen climate classification decisively shapes TSAD (up to ${\epsilon }^2=0.447$), indicating that spatial degradation distorts phenological patterns differently across climate classes. These findings support a multi-scale monitoring strategy: 10 m for fragmented, heterogeneous grasslands (<3 ha), 20 m as a computationally efficient alternative for homogeneous areas (>10 ha), and outline potential implications for policy frameworks such as the Common Agricultural Policy (CAP). Full article

Satellite remote sensing technology, characterized by high spatiotemporal resolution and long-term continuous observations, has become a critical tool for water quality monitoring. Excessive inputs of nutrients, particularly nitrogen and phosphorus, into marine environments can induce eutrophication and a range of associated ecological problems. Dissolved Inorganic Nitrogen (DIN) and Soluble Reactive Phosphorus (SRP) are key parameters governing water quality. However, their inherently weak spectral response poses a persistent challenge for the construction of accurate satellite-based inversion models. Incorporating environmental variables into model input features alongside spectral reflectance represents a promising approach to improving inversion performance. Taking the Bohai Sea as a case study, multi-source environmental variables were integrated with remote sensing spectral features, and the optimal feature combination and corresponding model were identified using Bayesian optimization and an iterative feature-importance screening strategy. Bayes-CatBoost demonstrated superior performance in estimating DIN and SRP, achieving R2 values of 0.82 and 0.68, respectively. After incorporating multi-source environmental variables, inversion errors decreased by 17.41% for DIN and 16.79% for SRP relative to models based solely on multispectral remote sensing data. Application of the proposed model to Sentinel-3 OLCI imagery enabled the generation of daily DIN and SRP distributions for the Bohai Sea. The results indicate that from 2018 to 2023, DIN and SRP concentrations in the Bohai Sea exhibited an overall stable but slightly declining trend with pronounced seasonal variability, accompanied by a gradual improvement in water quality. Full article

Geometric accuracy assessment is a fundamental prerequisite for the application of high-resolution optical satellite imagery, yet it remains particularly challenging in the absence of ground control points. This study proposes a consistency-driven accuracy assessment framework for ZY-3 satellite imagery that systematically bridges the conventional separation between block adjustment and accuracy evaluation. The framework is built upon a unified geometric error model that accommodates both inter-image consistency assessment and absolute accuracy evaluation within a common mathematical formulation. Recognizing the inherent uncertainties of publicly available reference data, a hierarchical validation chain is established: the Google Earth (GE) and Shuttle Radar Topography Mission (SRTM) datasets are first validated against WorldView imagery to bound their intrinsic errors, and subsequently employed as references for ZY-3 accuracy assessment. This design enables the simultaneous evaluation of planimetric accuracy, vertical accuracy, and inter-image consistency without reliance on ground control points. Experimental validation conducted on 1368 ZY-3 scenes covering a study area of 1.9 million km² yields a planimetric RMSE of 3.33 m and a vertical RMSE of 4.27 m. The results demonstrate that higher inter-image consistency empirically correlates with improved absolute positioning accuracy, and that the proposed framework not only reliably evaluates geometric quality but also provides diagnostic insights that can inform proactive adjustment strategies. The proposed method thus offers a practical and transparent solution for the geometric accuracy detection and enhancement of large-area ZY-3 satellite imagery. Full article

Satellite-based remote sensing has become an essential tool for territorial monitoring and environmental analysis, particularly in the context of extreme events. The European Union’s Copernicus Programme provides open-access satellite data that support near-real-time environmental assessment. Among its missions, the Sentinel-2 satellites, equipped with the Multispectral Instrument (MSI), offer high spatial and spectral resolution through 13 spectral bands, enabling the detection and monitoring of water quality parameters in inland and coastal water bodies. In early May 2024, intense rainfall events in Rio Grande do Sul, southern Brazil, triggered one of the most severe flooding episodes recorded in the region over the past 40 years. The objective of this study is to assess the environmental impacts of this event by mapping the flooded areas and analysing changes in water quality using Sentinel-2 imagery. Sentinel-2 Level-1C (Top-of-Atmosphere) products were processed using the Copernicus Browser to evaluate variations in coloured dissolved organic matter (CDOM) and dissolved organic carbon (DOC). Temporal analysis based on true-colour composite images allowed the observation of a significant increase in water extent over a seven-day period. Temporal comparison of Sentinel-2 images acquired before, during, and after the flood consistently showed a progressive expansion of inundated areas, accompanied by spatially coherent increases in satellite-derived CDOM and DOC indicators during the flood peak, followed by a partial decrease after flood recession. Although no simultaneous field measurements were available for quantitative validation, the observed spatial–temporal patterns consistently indicate flood-induced deterioration of water quality. Elevated CDOM and DOC levels are commonly associated with increased organic matter inputs and may have implications for water availability and suitability for human and animal consumption. Given the expected increase in the frequency and intensity of extreme weather events driven by climate change, this study highlights the importance of satellite-based remote sensing for rapid environmental monitoring. The Sentinel satellite constellation demonstrates strong potential for near-real-time assessment, offering timely information and broad spatial coverage to support environmental management and decision-making. Full article

Forests play an important role in timber and fiber production, carbon storage, biodiversity conservation, and various other ecosystem services, necessitating accurate and scalable inventory methods. Recent advances in remote sensing have enabled large-scale forest monitoring; however, challenges remain in extracting reliable information across varying spatial, temporal, and environmental conditions. Deep learning has emerged as a promising tool for addressing these limitations by learning complex patterns from diverse remote sensing data sources. This review synthesizes deep learning applications in forest inventory methods across three tasks: tree counting and localization, tree species identification, and tree measurement. In total, we evaluated 122 unique primary studies (37 for tree counting and localization, 57 for species identification, and 29 for tree measurement, with one study contributing to both the counting/localization and measurement tasks) spanning terrestrial, unmanned aerial vehicle (UAV), airborne, and satellite platforms, with a primary focus on optical imagery, Light Detection and Ranging (LiDAR) data, and their fusion. Across these studies, deep learning models frequently outperformed conventional machine learning and statistical baselines, with reported gains including up to 18% improvements in biomass estimation accuracy from data fusion and individual-tree species classification accuracies exceeding 90% for select architectures. However, performance differences were influenced strongly by forest structure, species complexity, sensor capability, and validation design. Counting and localization were generally more reliable in plantations than in complex natural or urban forests, while LiDAR was particularly valuable in dense, multilayer canopies. Species-identification accuracy was highest in studies with small, distinctive species sets, whereas mixed stands with many species showed lower accuracy. Only about a third of the reviewed studies (42 of 122) were externally validated on data or sites independent of model training, and reference data for tree measurement tasks were rarely based on direct destructive sampling. External validation often revealed lower performance than within-study testing, suggesting that reported accuracies may overestimate performance in new locations or conditions. Major advances are evident in the growing use of high-resolution UAV and smartphone-based imagery for tree-level analysis, the continued value of LiDAR for structural characterization, and the increasing integration of multimodal data fusion to improve detection, classification, and measurement accuracy. Persistent challenges include the limited availability of high-quality reference data, class imbalance and inconsistent species coverage, and weak model transferability across forest types, environmental conditions, and geographic regions. Future progress will likely depend on three priorities: development of larger and more standardized labeled datasets, stronger integration of structural, spectral, and phenological information, and the design of more transferable and application-oriented deep learning frameworks. Overall, this review provides a comprehensive, quantitatively grounded overview of deep learning-driven forest inventory methods and outlines future directions for improving scalability and applicability in forest monitoring and management. Full article

Effective monitoring of inland waters requires approaches capable of capturing high spatial and temporal variability. Traditional in situ sampling provides accurate point measurements but lacks spatial coverage, while satellite remote sensing is often limited by coarse spatial resolution and cloud cover. To address these limitations, this study developed and validated an integrated monitoring platform combining an Autonomous Surface Vehicle (ASV) and a drone-based multispectral imaging system for flexible, high-resolution water quality monitoring. The study was conducted in two contrasting aquatic environments in Alabama: the North River–Lake Tuscaloosa system and the Sardine Pass and Duck Skiff Pass tidal inlets in Mobile Bay. A HyCAT ASV equipped with a YSI EXO2 multiparameter sonde collected continuous in situ measurements of turbidity, chlorophyll-a (Chl-a), and fluorescent dissolved organic matter (fDOM), which served as water-truth for a MicaSense Dual multispectral camera onboard a DJI Inspire-2 drone platform acquiring imagery in 10 spectral bands at ~8 cm spatial resolution. Machine learning models, including ensemble and Random Forest approaches, were developed and compared with traditional empirical algorithms. Ensemble models consistently outperformed empirical approaches, while Random Forest models achieved the highest accuracy and best generalization across variable environmental conditions. Compared with Sentinel-2 and Landsat-8 imagery, the drone-derived maps resolved fine-scale spatial variability, including sediment plumes and near-shore gradients, that could not be detected by satellite sensors. To facilitate operational implementation, the RS-WaterQuality Mapper software tool was expanded to support ensemble and Random Forest analyses for MicaSense imagery. Overall, the integrated ASV–drone system demonstrated substantial advantages over traditional sampling and satellite remote sensing, including rapid deployment, user-controlled acquisition timing, high spatial resolution, and improved monitoring of small and optically complex water bodies, highlighting its potential for adaptive water resource management and early warning applications. Full article

Rapid global urbanization is increasing the need for accurate, large-scale traffic monitoring to support sustainable transportation and city governance. Satellite video remote sensing offers a unique way to continuously observe urban road networks over large areas. It provides high-resolution spatio-temporal data that is essential for traffic flow analysis, infrastructure assessment, and dynamic urban planning. Moving vehicle detection in satellite video sequences is a basic task that turns raw imagery into useful traffic-state information, supporting these applications. Despite the advantages of satellite video data, detecting moving vehicles in practice remains a tough problem. Objects are extremely small and lack clear appearance details, while low local contrast makes them hard to separate from complex backgrounds. Satellite platform motion also introduces background misalignment and intensity fluctuations, resulting in missed detections and false alarms that hurt monitoring reliability. Furthermore, current methods do not fully exploit temporal motion cues or transform-domain priors, creating a performance bottleneck that restricts their practical use. To solve these problems, this paper proposes a Channel-Attentive Spatio-Temporal-Frequency Alignment (CASTFA) framework to effectively use and combine multi-dimensional features for moving vehicle detection in satellite videos, with the goal of providing high-quality traffic monitoring data to help smart city planning. Specifically, a State Space-Guided Temporal Compression (SSGTC) module first collects information along the time dimension with linear computational complexity, greatly reducing overhead while keeping motion cues that are critical for traffic-state estimation. The compressed temporal features are then processed with a multi-scale Haar wavelet transform to get hierarchical time-frequency representations that capture subtle motion dynamics across different frequency bands. At the same time, a pre-trained backbone network extracts multi-scale spatial features. To allow these different domains to work together, a Cross-Domain Feature Alignment (CDFA) mechanism aligns and combines spatial and time-frequency features through channel-attentive operations. Experimental results on the publicly available satellite video moving vehicle detection dataset show that the proposed CASTFA method consistently outperforms existing approaches, with better precision, recall, and F1-scores across diverse urban scenarios. These results show that CASTFA can provide reliable moving vehicle detection performance under difficult real-world conditions, supporting accurate traffic-flow monitoring and providing valuable geospatial intelligence for smart urban planning, transportation management, and sustainable city development. Full article

Accurate estimation of crop water requirements is essential to improve irrigation efficiency for forage maize production. This study compared satellite- and UAV-derived normalized difference vegetation index (NDVI) models for estimating crop coefficients (K_c) and evaluated their operational performance for irrigation scheduling. K_c–NDVI models were developed during the 2023 growing season and subsequently validated under field conditions during the 2024 season in two forage maize hybrids (N83N5 and Matador) under three irrigation strategies: conventional producer irrigation (ID1), satellite-based irrigation scheduling (ID2), and UAV-based irrigation scheduling (ID3). Both NDVI sources exhibited strong relationships with K_c, with higher calibration accuracy for the UAV model (R² = 0.9414) than for the satellite model (R² = 0.8278). The UAV-based model applied 23–30% less irrigation water, maintaining high water productivity but also reducing crop growth, forage yield, and nutritional quality. In contrast, satellite-based irrigation scheduling promoted greater crop growth and produced the highest forage yield, reaching 59.8 t ha⁻¹ in hybrid N83N5 while maintaining efficient water use. This treatment also improved forage quality by increasing dry matter and starch concentrations while reducing fiber fractions. The findings highlight the complementary potential of satellite and UAV imagery in precision irrigation and underscore the trade-offs between spatial detail, temporal resolution, and operational scalability. Furthermore, the results demonstrate that a stronger K_c–NDVI relationship does not necessarily translate into improved irrigation scheduling performance. Under the conditions evaluated, the satellite-based model provided the best balance between water use, forage yield, and nutritional quality. Full article

Landslide susceptibility mapping (LSM) serves as a fundamental technical support for geohazard prevention and mitigation across mountainous terrains. This research constructs a multi-scale terrain unit integrated modeling framework targeting complex mountainous geomorphic settings, taking Zhenping County as the research object. Multi-resolution digital elevation model (DEM) datasets, multi-source satellite remote sensing imagery (GF-2), geological vector datasets and hydrological survey data are jointly adopted as the basic data source. The r.slopeunits module embedded in GRASS GIS is utilized to automatically segment slope units, and a comprehensive composite index S, coupling slope partition quality indicator F and model prediction accuracy metric R, is proposed to adaptively optimize two critical slope-unit hyperparameters: circular variance (c) and minimum unit area (a). Four DEM spatial resolutions (15 m, 25 m, 50 m, 100 m) are systematically calibrated with 42 groups of c–a parameter combinations to screen out the optimal slope-unit segmentation scheme (c = 0.1, a = 200,000 m²). Twelve landslide predisposing covariates covering topography, hydrology, lithology, human engineering activities and land cover are selected after multicollinearity diagnosis via Variance Inflation Factor and mean utility factor contribution evaluation. Logistic regression tree (LMT), LMT-Adaboost and LMT-Random Subspace are compared by random cross-validation and spatial block cross-validation. Parameter sensitivity analysis is further carried out to quantify the stability of model outputs against DEM resolution and slope-unit parameter perturbations. The LMT-RSM ensemble achieved the highest spatial cross-validation AUC (0.954 ± 0.019), outperforming LMT (0.925 ± 0.023) and AdaBoost-LMT (0.934 ± 0.021). The DeLong test confirmed that LMT-RSM’s superiority over LMT is statistically significant (p < 0.0001). The proportion of landslides in the very high and high susceptibility zones under the LMT-RSM model reached 95.98%, demonstrating relatively excellent spatial discrimination. This study provides an operational framework combining optimized slope units, ensemble learning, and spatially explicit validation for robust LSM in complex terrain, and offers a reproducible technical pathway for landslide risk prevention in mountainous regions. Full article

Atmospheric correction of high-spatial-resolution (HSR) optical satellite imagery is strongly affected by the adjacency effect (AE). Conventional Atmospheric Point Spread Function (APSF)-based AE correction methods are often based on simple local averaging or distance-weighted background approximations. These are often insufficient for highly heterogeneous HSR scenes and become computationally expensive when the AE’s influence range spans far more pixels. To address these issues, this study proposes a fast AE correction algorithm with adaptive local surface constraints. The method first introduces a surface-atmosphere coupling correction based on effective reflectance. It then constructs downward- and upward-weighting kernels and incorporates local reflectance constraints into the estimation of environmental reflectance to better characterize AE intensity in HSR scenes. Since environmental reflectance estimation is retained in a kernel-weighted form, the infinite-domain integration is reformulated as a finite-window computation with truncation compensation and accelerated via fast Fourier transform (FFT) convolution, followed by a few iterations for reflectance retrieval. Validation with GaoFen-2 (GF-2) panchromatic imagery shows that, at an aerosol optical depth of about 0.4, the proposed method achieves the best performance among the compared methods, with a mean absolute error (MAE) below 0.006 relative to in situ measurements, sharpness and contrast increases of approximately $99.0\%$ and $97.9\%$, respectively, and a National Imagery Interpretability Rating Scale (NIIRS) increase of more than 0.3. For a $1024\times 1024$ image with a 501-pixel AE window diameter, the running time is below 4 s, substantially lower than that of previous APSF-based AE correction methods. The FFT implementation also avoids the quadratic dependence on window size in direct spatial convolution. Additional experiments on multiple GF-2 and Gao Fen Duo Mo scenes show that the proposed method provides stable AE correction and achieves higher image quality and visual interpretability than the compared methods in HSR imagery. Full article

Accurate, large-scale maritime traffic monitoring supports marine spatial planning, fishery regulation, and ecological conservation. Medium-resolution optical satellite imagery, such as Sentinel-2A/B, provides a cost-effective complement to incomplete Automatic Identification System (AIS) data. Detecting small vessels in coastal waters remains challenging due to target size, complex backgrounds, and class imbalance. This study presents a robust end-to-end framework for small vessel detection and traffic density mapping using optical remote sensing imagery. A high-quality dataset of 8123 manually annotated vessels was constructed from 14 Sentinel-2 scenes across three marine environments in Fujian, China. An overlapping sliding-window cropping strategy, area truncation filtering, and negative sample preservation improved training efficiency and balance. Experiments compared top-of-atmosphere reflectance with surface reflectance (L2R) from the ACOLITE atmospheric correction (AC) algorithm, showing L2R mitigates aerosol scattering and nearly doubles vessel edge sharpness. YOLO-based detectors were evaluated, with YOLO26m achieving the best localization: F1-score 0.8461, mAP50 0.8979, mAP50–95 0.5536. Using this framework, 1 × 1 km traffic heatmaps for the Fujian coast in 2025 captured seasonal variations influenced by logistics and fishery moratoriums. Results demonstrate that integrating atmospherically corrected imagery with optimized deep learning strategies enhances sub-pixel ship detection, offering a scalable solution for intelligent maritime governance. Full article

The accelerated pace of urbanization across the Global South calls for precise, real-time building footprint data to underpin effective urban governance and enhance disaster resilience. Conventional mapping approaches, however, suffer from inefficiency in data acquisition and updating. Although Volunteered Geographic Information (VGI) provides a crowdsourced solution for geospatial data collection, it is commonly hindered by significant heterogeneity—manifested in inconsistent data completeness, positional inaccuracies and poor topological consistency across different datasets. To address these critical limitations, this study proposes an intelligent geospatial agent framework designed to autonomously fuse building data from multiple heterogeneous sources, including VGI, Very High-Resolution (VHR) satellite imagery, and Light Detection and Ranging (LiDAR) data. This study’s core innovative points are embodied in three key modules: a supervised VGI quality verification module that leverages the Random Forest model to evaluate the reliability of individual building feature elements; a hybrid building extraction engine which integrates LiDAR data with the Segment Anything Model (SAM) to realize zero-shot building extraction; and a cognitive rule engine that adopts Multi-Criteria Decision Analysis (MCDA) for the intelligent resolution of spatial conflicts. Comprehensive validation experiments were conducted in two African cities experiencing rapid urbanization—Kigali and Dar es Salaam. The results show that the proposed framework boosts data completeness by more than 29% and attains a fused dataset F1-Score of 0.919, effectively converting incomplete VGI data into a geospatial resource with near-official authoritative quality. Full article