Search Results (1,035)

To address the scarcity of high-precision control points on planetary surfaces and the accumulated drift of conventional relative-localization methods in deep-space exploration missions, this paper proposes a visual absolute-localization method based on salient-landmark contour matching and centroid-consistency constraints. Absolute localization is defined as estimating the rover position in the landing-site North-East-Down (NED) coordinate system or a map-projection coordinate system, rather than in image-pixel coordinates. Stable natural objects, including dunes and impact craters, are treated as generalized feature points. Local terrain is reconstructed from binocular navigation imagery; LiDAR is additionally used in the ground physical-equivalent experiment for multi-source terrain fusion. Multi-class cross-scale contour matching provides homologous landmark associations, after which centroid consistency aligns the local terrain with the global DOM/DEM reference frame. For ten Tianwen-1/Zhurong camera stations, the mean planar error was 0.458 m and the RMSE was 0.491 m. For five ground-test conditions, the mean planar error was 0.494 m and the RMSE was 0.526 m; all tested errors were below 1 m. Because the in-orbit reference DOM has a ground sampling distance of 1 m/pixel, the in-orbit sub-meter values indicate agreement with the adopted reference products and should not be interpreted as absolute accuracy independent of reference-map uncertainty. The results support the feasibility of natural-landmark-based map localization for future Chang’e and Tianwen missions. Full article

This study develops and applies a GIS-based 3D visualization model for observed flooding and subsidence conditions to support preliminary spatial screening for sustainable urban land fund development in the rapidly urbanizing Binh Chanh area, Ho Chi Minh City, during the period of 2020–2024. Field-survey records, official flood records, topographic maps, InSAR-based subsidence information, rainfall and tidal records, cadastral data, urban land-use expansion data, and planning information were integrated in a GIS environment. A DEM was generated using IDW interpolation and used as the terrain base for overlaying observed flood-prone locations with elevation, subsidence, road networks, cadastral parcels, urban land-use expansion, and planned urban land fund development areas. The overlaid datasets were then visualized in ArcScene, and an integrated map of observed flooding, subsidence, and urban land-use planning to 2030 was created to support preliminary spatial screening for sustainable urban land fund development. The observed inventory shows an increase in recorded flood-prone locations from 12 in 2020 to 34 in 2024, with maximum recorded flood depth reaching approximately 1.0 m and the largest recorded inundated area approaching 56,000 m². Areas below 1.2 m elevation account for 80.30% of the study area, while the high and moderately high subsidence classes occupy 57.19%. The proposed approach provides a practical, parcel-linked 3D GIS visualization tool for organizing observed flood information, communicating spatial constraints, and supporting preliminary screening in data-limited urbanizing areas. Full article

CORINE Land Cover (CLC) is one of the most used land databases intended for pan-European-scale analysis. Despite periodic updates, the data suffer from generalization, subjectivity, and inconsistent local knowledge, leading to a distorted representation of reality. In this study, we raise awareness of the use of CLC data in local analysis, for which it was never intended. Our study investigates whether incorporating auxiliary spatial data and local geographical knowledge can yield a higher-accuracy CLC product, without departing from the official CLC definitions and standards. We critically remapped polygon by polygon the 1990, 2000, and 2006 CLC layers, for Iași County (NE Romania), using topographic maps (1972–1989), aerial imagery (1978–2008), and satellite data (1980–2006), and compared it to the original CLC, through change detection analysis. The new maps revealed several issues imposed by (a) generalization—cartographical omissions among settlements, due to the application of a 25 ha minimum mapping unit and a 100 m minimum mapping width; (b) confusions between land cover and land-use classes, such as pasture and wetlands, especially under varying climatic conditions, or imposed by landforms, where we suggest the use of complementary data, such as a Digital Elevation Model (DEM); and (c) the inconsistencies of mapping between successive CLC editions. Our results indicate that the CLC should not be used for downscaling analysis. Therefore, the authors advocate integrating multiple temporal remote sensing layers to achieve a more accurate assessment of land cover classes, thereby compensating for the data’s top-down character. Based on these findings, we propose an error classification approach to serve as a reference for risk mitigation in downscaled spatial analysis. We emphasize the need for CLC data users to validate their data against ground truth to mitigate analytical uncertainties. Full article

Digital elevation models (DEMs) and vegetation cover (FVC) are critical foundational data for ecological and environmental monitoring and land-use management; however, the application of domestically produced commercial high-resolution optical satellite stereo imagery for high-precision DEM construction and vegetation parameter inversion in areas with complex topography still requires further exploration. This study focuses on the hilly area in the northeastern part of Kunming City, Yunnan Province, and utilizes tri-view stereo imagery from the Beijing-3 (BJ3-N2) satellite to conduct research on DEM reconstruction, topographic correction, image fusion, and vegetation cover inversion. DEM products were generated by matching multiple sets of panchromatic and multispectral stereo imagery, and their accuracy was verified using ICESat-2 ATL08 laser altimetry data. The results indicate that DEMs generated from panchromatic imagery exhibit higher accuracy than those from multispectral imagery; among them, the PAN-0103 DEM demonstrated the best overall performance, with a global RMSE of 3.42 m. Topographic slope and land cover type were found to have significant effects on DEM accuracy. A terrain-constrained Gram–Schmidt fusion model was constructed based on the optimal DEM to generate the fused image MP-01, which had a spectral angle (SAM) of 3.2° and a band correlation coefficient (CC) of 0.92. Further, by integrating field RTK plot data, an improved pixel binary model was constructed to perform FVC inversion. The results showed that the Mahalanobis distance classification method yielded the best classification performance, with a model inversion RMSE of 0.074—a 41.7% reduction compared to the traditional fixed-end element model. The study demonstrates that the high-resolution stereo imagery from Beijing-3 can effectively support the refined construction of DEMs and quantitative monitoring of vegetation cover in complex hilly areas and can provide a technical reference for the application of domestically produced commercial remote sensing satellites in the field of ecological monitoring. Full article

Reliable target detection and geographic localization are critical for unmanned aerial vehicle (UAV)-assisted search and rescue (SAR) yet remain challenging in complex outdoor environments. Small targets in UAV Red–Green–Blue–Infrared (RGB–IR) imagery suffer from background clutter, occlusion, low illumination, and infrared thermal diffusion, while localization is vulnerable to unstable viewpoints and terrain-induced ray uncertainty. This study presents an integrated UAV-SAR framework coupling lightweight multispectral detection with Digital Elevation Model (DEM)-constrained geographic localization. For detection, the Asymmetric Fusion and Context-aware Detection (AFC-Det) network leverages asymmetric dual-stream encoding, cross-modal mutual prompting, and high-resolution anchored aggregation to enhance small-target representation from RGB–IR pairs. For localization, the Global Context-Regularized Huber Ray Consistency Optimization (GCR-HRCO) improves geolocation via global ray aggregation, multi-ray geometric consistency, Huber robust optimization, and DEM-based terrain constraints. Experimental results demonstrate AFC-Det achieves 45.4% average precision (AP) and 44.7% AP for small objects (APs) on the VTSaR dataset, with 1.7 million parameters, 8.0 GFLOPs, and 107.2 FPS, generalizing well to M3FD (54.6% AP). On SAR-DAG_raycast, GCR-HRCO reduces mean horizontal error from 6.85 m to 3.31 m and RMSE from 8.16 m to 4.33 m. Collectively, these results demonstrate the effectiveness of the proposed detection and localization components. Full article

Geographic information generalization underpins multi-scale spatial databases and cartography, and reliable DEM downscaling is critical to maintaining geomorphological consistency across map scales. Traditional DEM simplification and resampling methods rely on local geometric filtering and overlook global terrain structures, frequently causing structural distortions such as broken ridges and deformed slopes. This study proposes DD-GAN, a generative adversarial network constrained by terrain morphological factors for high-fidelity DEM downscaling. Built on a GAN architecture, the model embeds local relief and gradient as physical loss terms to prioritize major geomorphic skeletons and suppress trivial micro-terrain during resolution reduction, avoiding the indiscriminate over-smoothing of conventional sampling approaches. Multi-scale experiments covering downscaling factors ranging from 2× to 5× are conducted using mountainous datasets from Chongqing, Alaska, and Colorado. Quantitative and visual comparisons against raster interpolation, TIN-based simplification, and ordinary CNN show that DD-GAN mitigates terrain structural distortion and better retains elevation extremes and slope features, with more prominent strengths under large downscaling multiples. This physics-constrained deep learning paradigm provides an automated DEM downscaling solution that facilitates multi-scale terrain representation, supporting cartographic production and geomorphometric analysis. Full article

Accurate delineation of current landslide extent is important for hazard assessment, sustainable watershed management, and disaster risk reduction in tectonically active mountainous regions. This study presents a pilot machine learning framework for pixel-level landslide classification in the Laonung (Laonong) Creek Watershed, southern Taiwan, using very high-resolution digital elevation model (DEM) derivatives and SPOT-6 multispectral imagery. Thirteen geomorphometric and spectral features, including slope, curvature, and six spectral indices derived from SPOT-6 bands, were extracted from 96 landslide-containing tiles within a pilot subregion of the watershed; no landslide-free tiles were included in model training or evaluation. Landslide annotations followed a geomorphic-unit delineation protocol in which optical imagery provided the primary evidence of current activity and DEM-derived hillshade supported boundary refinement. Three classifiers were evaluated using column-quartile spatially blocked four-fold cross-validation, with each fold comprising a geographically contiguous range of columns, to reduce spatial leakage: logistic regression (LR), random forest (RF), and XGBoost. All three models substantially outperformed the no-skill baseline for the resampled evaluation dataset (average precision, AP $=0.250$), achieving mean AP values of $0.854\pm 0.040$, $0.858\pm 0.033$, and $0.846\pm 0.035$ for LR, RF, and XGBoost, respectively. The convergence of linear and nonlinear model performance suggests that the dominant discriminatory signal is largely captured by relatively simple spectral and topographic predictors within this pilot dataset, rather than reflecting a general property of landslide classification. SHapley Additive exPlanations (SHAP) analysis across all four spatial folds identified SPOT-6 Band 3 (Red) as the dominant predictor in every fold, with NDVI a robust secondary predictor, consistent with the spectral characteristics of fresh bare-soil landslide surfaces and with the optical cues used in the annotation protocol. The results are interpreted in the context of the pilot dataset’s limited spatial extent, the resampled class distribution used for model evaluation, and unquantified label uncertainty. This study provides a transferable methodological baseline for future, larger-scale landslide classification analysis in the Laonung Creek Watershed and highlights the potential contribution of spatially explicit landslide mapping to sustainability-oriented disaster management. Full article

Karst hillslope land systems are characterized by strong microtopographic heterogeneity, exposed rock–soil patches, and management-related disturbance, resulting in pronounced fine-scale variation in ecological responses. This study investigated three contrasting karst hillslopes within the Guanling–Zhenfeng Huajiang Rocky Desertification Comprehensive Control Demonstration Area in Guizhou Province, China, designated as High, Medium, and Natural sites according to their observed land cover and management characteristics, using UAV multispectral imagery and LiDAR-derived DEM data. A terrain–ecology coupling strength indicator (TECSI) framework was developed to assess the spatially transferable predictability of ecological response patterns by terrain variables. At the 5 m block scale, FVC, OSAVI, NDRE750, rock–soil exposure index (REI), and GLCM contrast were linked with LiDAR-derived microtopographic predictors using generalized additive models, residual Moran’s I, random cross-validation, and spatial block cross-validation. FVC, OSAVI, NDRE750, and GLCM contrast differed significantly among the three sites (p < 0.001), whereas REI mainly reflected patchy non-vegetated rock–soil substrate exposure within hillslopes. Across the 15 site–indicator models, deviance explained ranged from 5.5% to 35.8%. Under 25 m spatial block cross-validation, integrated TECSI ranked Medium (0.143) > High (0.086) > Natural (0.036), and this ranking remained stable across sensitivity analyses. Steep-slope units at or above 25° in the High and Medium sites were associated with poorer vegetation condition and increased substrate exposure. TECSI provides a reproducible spatial validation framework for identifying terrain-sensitive ecological response units and supporting fine-scale monitoring, soil–water conservation screening, and vulnerable land unit management in karst hillslopes. Full article

Regional development disparities remain a persistent global challenge, yet existing assessment approaches often face a trade-off between spatial detail and temporal coverage. Conventional socioeconomic statistics provide relatively reliable information but are typically limited by coarse spatial representation and low update frequency, whereas high-resolution remote sensing-based studies often focus on individual time points, making it difficult to capture the temporal evolution of regional development. Remote sensing observations provide valuable opportunities for regional development assessment by offering extensive spatial coverage and repeated observations over time. To address this gap, this study proposes a multisource remote sensing framework for characterizing the spatiotemporal dynamics of regional development in Chongqing Municipality across four temporal nodes (2014, 2016, 2018, and 2020). We first construct a county-level Development Intensity Index (DII) using socioeconomic indicators derived from statistical data. Subsequently, we integrate nighttime light, DEM, NDVI, and POI data to generate a 500 m gridded Comprehensive Spatial Development Index (CSDI), which captures spatial heterogeneity at a fine spatial scale. The CSDI exhibits strong correspondence with the DII, and its spatial validity is further corroborated through visual interpretation of Google Earth imagery. Results indicate that areas with higher development levels are predominantly concentrated in Chongqing’s central urban core, while less-developed counties are concentrated in the northeastern and southeastern peripheries. Although a general upward trend in development is observed across the study period, notable spatial disparities persist. Overall, the proposed CSDI-based framework offers an effective and replicable approach for gridded regional development assessment, with implications for targeted regional planning and differentiated policy design. Full article

Concrete readily develops cracks under service loads, which poses severe risks to the overall safety of engineering structures. In this work, the discrete element method (DEM) integrated with PFC2D 5.0 numerical software is adopted to construct a mesoscale concrete numerical model containing pre-existing internal fractures, and uniaxial compressive loading simulations are subsequently carried out. Unlike previous studies that predominantly examined isolated fracture parameters, this work systematically investigates the coupled effects of fracture inclination angle, length, and quantity on crack propagation mechanisms at the mesoscale, and for the first time establishes a quantitative relationship between microcrack spatial distribution patterns and macroscopic mechanical degradation. Parametric analyses are performed to quantify the influences of fracture geometric characteristics, including fracture inclination angle (30°, 45°, 60°), fracture length (short, long and extra-long), fracture quantity (4, 8 and 16), as well as the comparison between intact and fractured concrete specimens. The fracture quantities of 4, 8, and 16 are selected to represent low, medium, and high levels of initial defect density within the concrete matrix, corresponding to approximately 1%, 2%, and 4% of the total specimen area, respectively, thereby enabling a systematic investigation into the progressive deterioration of mechanical performance with increasing internal damage severity. The whole evolution process of crack initiation, crack propagation and ultimate failure patterns of concrete is systematically explored. Numerical results reveal that specimens with larger fracture angles exhibit higher compressive strength yet generate abundant newly formed microcracks, whereas low-angle prefabricated fractures are prone to triggering abrupt brittle failure. Specimens embedded with shorter fractures achieve superior mechanical strength and develop denser, more intensive microcrack distributions; in contrast, long pre-existing fractures drastically degrade compressive strength while limiting the generation of secondary cracks. Reducing the number of internal defects simultaneously improves compressive strength and expands the coverage range of the induced fracture network. Specimens with 16 prefabricated fractures deliver the weakest mechanical performance, owing to the excessively high initial defect density inside the matrix. In comparison with fractured samples, intact concrete without pre-set fractures achieves better comprehensive performance in terms of compressive strength, deformation compatibility and uniform microcrack development. A core conclusion drawn from this study is that the total quantity of microcracks cannot serve as a direct indicator to evaluate the damage degradation degree of concrete; instead, the spatial distribution pattern of microcracks dominates the deterioration level. Evenly scattered microcrack populations maintain relatively high residual strength, whereas the concentrated coalescence of microcracks into continuous penetrating macrocracks leads to an abrupt decline in structural load-carrying capacity. The findings of this research can provide theoretical references for stability evaluation and safety diagnosis of defective concrete structures in practical engineering. Full article

This study investigated the influence of airborne laser scanning (ALS) pulse density on the accuracy of total tree height estimates and the quality of raster products commonly used in individual tree detection (ITD) workflows. Using a high-density (36 pulses per square meter (PPM)) ALS dataset, we generated lower-density subsets and compared derived outputs using a standardized processing pipeline. Tree height estimates were validated against field measurements, and elevation products—digital elevation models (DEMs), digital surface models (DSMs), and canopy height models (CHMs)—were assessed across pulse densities. DSM and CHM quality improved with increasing density, showing reduced bias and tighter variation. In contrast, DEM accuracy remained relatively stable across densities, indicating lower-density ALS may suffice for ground modeling in forested environments. The results also revealed a strong positive relationship between pulse density and total tree height accuracy. Higher-density datasets consistently produced more accurate and less biased tree height estimates, while sparser datasets exhibited systematic underestimation due to missed canopy peaks. These findings emphasize the importance of aligning ALS pulse density with project objectives. While low-density data may be adequate for terrain modeling, higher-density acquisitions are critical for reliable canopy representation and accurate ITD outputs. This study provides operational guidance for forestry practitioners and highlights the value of investing in higher-resolution ALS data for modern forest inventories. Full article

China is rich in magnetite mineral resources, but they are generally characterized by low grade, fine dissemination size, and a high content of harmful impurities. Wet low-intensity magnetic separation (WLIMS) is an important method for processing fine-grained magnetite. However, during the separation process, fine magnetite particles are prone to magnetic agglomeration, which makes it difficult for conventional WLIMS separators to achieve high-selectivity separation. To address this issue, a novel WLIMS separator based on a rotating magnetic system was developed in this investigation, and its separation characteristics were systematically investigated through a combined approach comprising CFD–DEM–FEM multiphysics coupling simulations and experimental validation. Simulation results indicate that the rotating magnetic system significantly reduces the chain length and the structural stability of magnetic agglomerates just as magnetite particles enter the magnetic field region. Furthermore, under the rotating action of the magnetic system, the magnetic chains only enclose a portion of the intergrowth minerals, while gangue minerals remain unattached, which positively contributes to improved separation selectivity. Both laboratory-scale experimental results and industrial production data indicate that, compared to the conventional WLIMS separator, the rotating magnetic system WLIMS separator achieves significantly superior separation performance. For a magnetite ore with a grade of 57.68%, the rotating magnetic system WLIMS separator achieved an optimal concentrate grade of 65.43% (with a recovery of 94.78%), whereas the conventional WLIMS separator attained only 60.32% at a similar recovery rate. This investigation provides an important basis for the large-scale industrial application of rotating magnetic system WLIMS separators and the efficient development and utilization of fine-grained magnetite resources. Full article

This paper introduces a sophisticated stochastic methodology grounded in a lattice rule featuring an optimized generating vector, which has been rigorously developed and comprehensively analyzed. At the heart of this investigation lies the Unified Danish Eulerian Model (UNI-DEM), an extensive large-scale mathematical framework designed to accurately capture the complex physical and chemical processes occurring within the atmosphere. The proposed lattice-based approach is systematically compared against state-of-the-art techniques, including the modified Sobol sequence and the Fibonacci lattice rule. The comparative analysis demonstrates the superiority of the proposed method in the estimation of high-dimensional integrals, highlighting its enhanced robustness and computational efficiency. These characteristics render it particularly well-suited for the computation of sensitivity indices, which are crucial for ensuring the reliability of scientific models. Furthermore, the study employs variance-based sensitivity analysis methods, notably the Sobol technique, to quantify the influence of input parameters on model outputs rigorously. A comprehensive experimental evaluation is undertaken, integrating advanced Monte Carlo algorithms in conjunction with stochastic scrambling strategies to further enhance computational performance. In addition, the research examines the effects of varying emission levels on key atmospheric pollutants such as ammonia, ozone, ammonium sulfate, and ammonium nitrate, with particular emphasis on major European urban centers exhibiting diverse geographical and environmental conditions. These results underscore the critical importance of sensitivity analysis in validating model accuracy and elucidating the intricate relationships between input parameters and environmental outcomes. Full article

The compelling visualization of Digital Elevation Models (DEMs) constitutes a vital intersection between Geographic Information Science (GIS) and the digital humanities. Nevertheless, traditional Generative Adversarial Networks (GANs) frequently demonstrate a “geography-blind” characteristic, resulting in structural “topographic drift” by dissociating geomorphic complexity from cartographic constraints. To overcome this limitation, we propose Geo-InkGAN, a geo-heuristic framework that integrates geographic principles with generative processes to achieve high-fidelity ink-wash style synthesis. A key component of our approach is an adaptive optimization strategy grounded in the Slope Standard Deviation (SSD). By establishing a quantitative relationship between geomorphological entropy and the cycle-consistency loss weight (${\lambda }_{cyc}$), we effectively address the Pareto trade-off between geomorphic accuracy and esthetic representation. Our results indicate that alluvial plains benefit from low-intensity constraints to facilitate fluid ink diffusion, whereas rugged terrains require high-intensity constraints to maintain the integrity of the topological framework. Additionally, the HCEG-SE mechanism (Hillshade-Contour Edge-Guided Stroke Enhancement) narrows the semantic divide between terrain skeletons and artistic textures by combining multi-directional non-photorealistic rendering with precise edge extraction techniques. Evaluated across five geomorphologically diverse regions—from karst towers to loess plateaus—Geo-InkGAN demonstrably surpasses existing benchmarks in Geomorphological Structure Correlation (GSC). This geomorphology-aware approach advances the scientific rigor of AI-driven cartography and offers a refined methodology for the cultural representation of digital twin landscapes. Full article

Portable digital microscopy (PDM) has become standard equipment in archaeological and restoration campaigns. In recent years, several studies have proposed combining PDM and micro-photogrammetry for preliminary documentation and in situ three-dimensional (3D) digitization, testing its applicability on a variety of materials and artifacts, including stone, bone, mural paintings, and small jewelry. However, limited data is available on its use with some of the most common types of artworks, such as paintings on canvas and wooden panels. In this study, the performance of micro-photogrammetry using a Dino-Lite digital microscope (Dino-Lite Europe, Almere, The Netherlands) was evaluated on five representative artistic materials: metal, stone, and paintings, including mural, canvas, and panel works. Textured mesh models were generated from the acquired datasets, and the quality of the 3D reconstruction was assessed through analysis of dense cloud confidence and Digital Elevation Model (DEM) accuracy. The metrical reliability of the micro-photogrammetric results was validated by comparing the 3D data obtained with the digital microscope against height measurements acquired using an optical microprofilometer. The results indicate that the performance of the method is strongly influenced by the physical properties of the substrates examined. Accurate and metrically consistent reconstructions were achieved for matte and textured surfaces (e.g., fresco and stone), whereas significant limitations emerged on dark or glossy painted surfaces, such as canvas and panel paintings. Full article