Search Results (76)

Two-objective optimization requires both reliable front approximation and explainable non-dominated extraction. This study develops a theoretical and computational method that maps sampled objective vectors to rasterized objective-space images and processes their Pareto structure through supervised neural approximation, a deterministic convolutional extractor, and exploratory reinforcement search. Network I reconstructs a high-density sampled occupancy image from sparse samples, whereas Network II approximates the sampled Pareto-front boundary. The principal algorithmic contribution is a fixed cross-correlation kernel derived from the two-objective dominance quadrant and coupled with a cell archive that preserves original vectors and resolves raster collisions through exact dominance checks. Under the stated coordinate convention, central inversion relates the dominating and dominated displacement quadrants, translation-equivariant cross-correlation applies the same local relation across the grid, and minimization selects only the improvement-directed boundary. Experiments on SCH, FON, POL, KUR, and ZDT synthetic benchmarks assess front-geometry recovery and deterministic extraction on grids from 127 × 127 to 2048 × 2048; the reinforcement-learning results on SCH are interpreted as exploratory feasibility evidence. The present evidence is therefore confined to synthetic benchmarks. The method provides a benchmark-based methodological foundation for future multi-criterion model-selection and calibration research, including option-pricing model research in financial mathematics and quantitative economic analysis. Full article

Agricultural drought threatens rainfed agriculture in northeast Thailand, where variable monsoon rainfall, limited irrigation access, and extensive cropland increase vulnerability. This study developed a Google Earth Engine-based Agricultural Drought Risk Index (ADRI) for the Chi River Basin using six benchmark years (2000, 2005, 2010, 2015, 2020, and 2025). CHIRPS precipitation, MODIS-derived vegetation health, ERA5-Land soil moisture, irrigation accessibility, and agricultural land exposure were normalized and integrated by weighted linear combination. The analysis quantified risk-class areas, irrigated–rainfed contrasts, persistent hotspots, weight sensitivity, and spatial agreement with the official Land Development Department recurring-drought map. Moderate risk dominated most years, but high-risk area expanded to 60.7% in 2015, coincident with severe rainfall deficits during the 2015–2016 El Niño event. Conditions improved in 2020 and 2025 as rainfall, vegetation health, and soil moisture recovered. Rainfed areas consistently had higher ADRI values than irrigated areas, and persistent hotspots were concentrated in southeastern and downstream agricultural zones. The principal spatial and temporal patterns remained stable under ±10% weight perturbations. External validation identified ADRI > 2.90 as the optimal threshold, with raster-level precision, recall, and F1 of 0.779, 0.884, and 0.828, respectively; the 998-point sample produced an F1 of 0.832. ADRI therefore provides a practical basin-scale screening framework for drought monitoring, adaptation prioritization, and agricultural water-management planning. Full article

Rockfall hazards are frequently observed within mountainous road networks. Significant uncertainties regarding the optimal selection of evaluation units and spatial modeling scales are still being identified in this field. A comprehensive comparative framework for rockfall susceptibility mapping is presented in this study, using Wufeng County as the empirical study area. Five evaluation scenarios were constructed to systematically isolate the independent predictive contributions of the spatial domain, the mapping unit morphology, and the physics-informed engineering proxy. These scenarios included a whole-county macro-scale raster; three multi-scale road buffers with widths of 1 km, 2 km, and 3 km; and an object-oriented vector road evaluation unit (REU) framework. To parameterize localized engineering-induced risks, a physics-informed feature defined as the theoretical slope-cutting height ($H_{\mathrm{cut}}$) was structurally introduced into the vector-based assessment. Thirteen representative machine learning, deep learning, and statistical algorithms—including Random Forest, LightGBM, and TabNet—were systematically cross-examined under both unconstrained splits and strict Leave-One-Road-Corridor-Out Validation (LORCOV) protocols. The empirical multi-metric sensitivity analysis explicitly decouples the three structural effects. First, isolating the effect of the spatial domain reveals that restricting the validation extent from a broad countywide area to a narrow road corridor purges unperturbed background terrain noise, shifting the focus from easy negatives to geomorphological hard negatives. Second, evaluating the independent effect of the evaluation unit demonstrates that transitioning from continuous raster pixels to homogeneous vector REUs successfully resolves the terrain smoothing effect, precisely characterizing sharp geomechanical gradients adjacent to cut slopes. Third, isolating the effect of adding $H_{\mathrm{cut}}$ proves that this engineering indicator drives the primary descriptive gain, enabling tree-based ensembles to achieve a peak baseline AUC of 0.7763 and maintain a robust spatial validation AUC of 0.6129 under strict geographic block constraints, whereas legacy deep learning architectures exhibit an inductive bias mismatch on small-scale tabular records. Rather than asserting a single optimal paradigm, this coordinated feature–unit matching framework provides transport authorities with a highly calibrated, target-tiered decision matrix to optimize localized public works safety budgets and protect critical linear infrastructure assets. Full article

Aiming at the current SLAM (Simultaneous Localization and Mapping) algorithms in urban scenarios, which have problems such as elevation drift, odometry drift, and the appearance of false loop closures, a tightly coupled SLAM method with LiDAR and inertial guidance is proposed. In the front-end, a raster-based point cloud feature extraction method is introduced, enabling simultaneous segmentation and extraction of line, surface, and ground features. Utilizing the alignment results of line and surface features as the initial value for ground point alignment, interpolation weights are determined based on roll and pitch angle errors, effectively reducing global elevation errors through frame-by-frame constraints. The back-end employs an error state-based Kalman filter (ESKF) for GPS and IMU data fusion, enhancing the validity of true state estimation. A Scan Context loop closure detection method is designed, augmented by GPS detection as an auxiliary loop closure constraint to mitigate false loop closures. A global factor graph optimization model is also proposed. Experimental results demonstrate that, compared to existing open-source algorithms, the proposed method exhibits improved performance in structured urban scenes, reducing the average RMSE APE by 47.4% compared with LiDAR-only methods and by 22.9% compared with tightly coupled LiDAR-inertial methods. This work highlights the potential of multi-sensor fusion SLAM for achieving high-precision 3D localization and mapping in complex urban environments. Full article

The generation of reliable and up-to-date national land cover information is essential for environmental management, climate action, and territorial planning. In Colombia, the CORINE Land Cover Colombia (CLCC) framework has been the official reference for land cover monitoring since 2000, traditionally updated through expert-based Computer-Assisted PhotoInterpretation (CAPI) at a 1:100,000 scale. However, increasing demands for higher spatial resolution and more frequent temporal updates have made process optimization necessary, driving the incorporation of cloud-based processing and artificial intelligence (AI), including machine learning and deep learning algorithms. This study presents a semi-automated methodology for generating a new generation of harmonized CLCC-compatible raster land cover maps at a 1:50,000 scale—offering four times greater spatial detail than the official vector product—with the capacity for semi-automated annual updates. The approach combines legend harmonization from 55 to 23 classes, historical CORINE Land Cover (CLC) polygon-guided sample generation, spectral stability analysis, and regionalized classification across 190 homogeneous subregions, supported by a reproducible cloud-based architecture. National land cover maps were produced for 2020, 2022, and 2024 with thematic accuracies above 80% and Kappa coefficients up to 0.87, alongside change maps for the 2022–2024 period capturing key dynamics in agricultural frontier expansion, wetland variability, and urban expansion. The resulting products also provide structured inputs for expert-based CAPI workflows, supporting the continuous updating of the official 1:100,000 CLCC map. The results demonstrate the operational capacity of integrating AI, cloud computing, and expert knowledge to strengthen Colombia’s national land cover monitoring system. Full article

The AVIF (AV1 Image File Format) is a modern, royalty-free image format that leverages the AV1 video codec for superior compression efficiency, supporting both lossy and lossless modes. Its entropy encoding relies on a multi-symbol context-adaptive arithmetic coder (range coding with adaptive cumulative distribution functions (CDFs)), which is effective for general imagery but may not optimally exploit the repetitive structures common in Geographic Information System (GIS) maps/data. This paper proposes replacing AVIF’s entropy encoder with the Burrows–Wheeler Transform (BWT), a reversible preprocessing algorithm that rearranges data to create runs of similar symbols, enhancing subsequent compression. We detail the technical steps for modification, drawing from AV1’s open-source implementation, and explain why BWT is advantageous for GIS raster maps/data, which often feature large uniform areas, limited color palettes, and spatial redundancies. Empirical evidence from related studies on BWT-based image compression shows improvements in lossless scenarios, potentially considerably reducing file sizes over standard methods while preserving data integrity critical for geospatial analysis. This swap could improve storage, transmission, and processing efficiency in GIS applications, such as remote sensing and cartography. The discussion includes challenges like computational overhead and compatibility, with recommendations for implementations. The resulting BWT-AVIF hybrid produces a non-standard AV1 bit-stream that is not compliant with the AV1 or AVIF specifications and therefore requires custom decoders. It is presented here as a research prototype for GIS-specific compression rather than a compliant AVIF extension. Full article

Path planning aims to find a safe and efficient path from a starting point to an end point, and it has been well developed in fields such as robot navigation, autonomous driving, and intelligent decision systems. However, traditional path planning faces challenges in an uncertain rescuing environment due to limited sensing range and a lack of accurate obstacle information. In order to address this issue, this paper proposes an improved two-stage Theta* algorithm for handling multi-probability obstacle scenarios in unstructured rescue environments. First, a global probability raster map is constructed by integrating historical maps and expert prediction maps with probability weights quantifying the uncertainty in the spatial and temporal distribution of obstacles. Second, a probability-sensitive heuristic function (PSHF) is designed, and a Sigmoid function is used to map the probability field of obstacles, thereby enabling limited penetration in low-risk areas and enforced avoidance in high-risk areas. Furthermore, a multi-stage line-of-sight detection optimization mechanism is proposed, which combines probability soft threshold penetration and backtracking verification to improve the noise robustness. Finally, a hierarchical planning architecture is constructed to separate global probabilistic guidance from local strict obstacle avoidance, ensuring both the global optimality and local adaptability of the path. Extensive simulation results in mine rescue scenarios demonstrate that the proposed method achieves lower path cost and fewer path nodes compared to traditional A*, Dijkstra, and Theta* algorithms, while significantly reducing local replanning overhead and maintaining stable performance across multiple uncertain environments. Full article

Populus euphratica is a key constructive species in desert ecosystems and plays a vital role in maintaining their stability. However, effective automated methods for accurately delineating its distribution outlines are currently lacking. This study used the mainstream area of the Tarim River as a case study and proposed a technical solution for identifying the distribution outline of Populus euphratica using multi-source thematic classification data. First, cropland thematic data were used to optimize the accuracy of the Populus euphratica classification raster data. Discrete points were removed based on density to reduce their impact on boundary identification. Then, a hierarchical identification scheme was constructed using the alpha-shape algorithm to identify the boundaries of high- and low-density Populus euphratica distribution areas separately. Finally, the outlines of the Populus euphratica distribution polygons were smoothed, and the final distribution outline data were obtained after spatial merging. The results showed the following: (1) Applying a closing operation to the cropland thematic classification data to obtain the distribution range of shelterbelts effectively eliminated misclassified pixels. Using the kd-tree algorithm to remove sparse discrete points based on density, with a removal ratio of 5%, helped suppress the interference of outlier point sets on the Populus euphratica outline identification. (2) Constructing a hierarchical identification scheme based on differences in Populus euphratica density is critical for accurately delineating its distribution contours. Using the alpha-shape algorithm with parameters set to α = 0.02 and α = 0.006, the reconstructed geometries effectively covered both densely and sparsely distributed Populus euphratica areas. (3) In the morphological processing stage, a combination of three methods—Gaussian filtering, equidistant expansion, and gap filling—effectively ensured the accuracy of the Populus euphratica outline. Among the various smoothing algorithms, Gaussian filtering yielded the best results. The equidistant expansion method reduced the impact of elongated cavities, thereby contributing to boundary accuracy. This study enhances the automation of Populus euphratica vector data mapping and holds significant value for the scientific management and research of desert vegetation. Full article

Accurate and up-to-date data on built-up areas are crucial for urban planning, disaster management, and sustainable development, yet Romania still lacks a unified, official database. In this study we integrated the three widely used global data sources—OpenStreetMap (OSM), Microsoft Building Footprints (MSBFs), and Global Human Settlement Layer Built-up surface (GHS)—onto a 10 m resolution raster grid and applied this consistently at the national scale across 3181 settlement polygons to produce a more accurate, unified ensemble model for Romania. The methodological basis was Triple Collocation Analysis (TCA), extended with ETC/CTC to estimate per-settlement scale factors, enabling the quantification and optimal weighting of the relative errors and accuracy in the absence of independent reference data. Weight patterns vary by settlement type: OSM receives relatively higher weights in smaller rural settlements with less redundant error; in municipalities the stronger OSM–MSBF correlation reduces both of their weights and increases the GHS share; cities exhibit a more balanced weighting. At cell level, the ensemble provides uncertainty quantification via confidence intervals that typically range from 2% to 14% at settlement scale. The resulting model—like any model—does not perfectly reflect reality; however, the ensemble improves the accuracy and timeliness of the available data. The resulting model is replicable and updatable with newer data, making it suitable for numerous practical applications, especially in spatial development and risk analysis. Full article

Through highly detailed data acquisition, a precision agriculture approach leads to the optimization of inputs, improving, for instance, water and nutrient use efficiency. High-resolution vigor mapping in perennial orchards provides the spatial detail required to achieve such targeted management. This exploratory case study characterizes the spatial variability of vegetative vigor in a young SHD almond orchard in southern Sardinia by integrating high-resolution unmanned aerial vehicle (UAV) imagery and Normalized Difference Vegetation Index (NDVI) mapping with two consecutive seasons of ground measurements; the NDVI raster was subsequently used to delineate three distinct vigor zones. The NDVI was selected as a reference index because of its well-assessed performance in field-variability studies. Field measurements, during the kernel-filling period, included physiological assessments (stem water potential (Ψ_stem), SPAD, photosynthetic rates), morphological evaluations, soil properties, yield, and quality analyses. High vigor zones exhibited better physiological conditions (Ψ_stem = −1.60 MPa in 2023, SPAD = 38.77 in 2022), and greater photosynthetic rates (15.31 μmol CO₂ m⁻² s⁻¹ in 2023), alongside more favorable soil conditions. Medium vigor zones showed intermediate characteristics, and balanced soil textures, producing a higher number of smaller almonds. Low vigor zones exhibited the poorest performance, including the most negative water status (Ψ_stem of −1.94 MPa in 2023), lower SPAD values (30.67 in 2023), and coarse-textured soils, leading to reduced yields. By combining UAV-based NDVI mapping with ground measurements, these results highlight the value of precision agriculture in intra-field variability identification, providing a basis for future studies that will test site-specific management strategies in SHD orchards. Full article

Remote sensing technology provides an efficient and low-cost approach for acquiring large-scale soil information, offering notable advantages for soil organic matter (SOM) mapping. However, in recent years, the bare soil period of cultivated land in Northeast China has significantly shortened, posing serious challenges to traditional SOM prediction and mapping methods that rely on optical imagery. Meanwhile, current approaches that integrate optical imagery, radar imagery, and environmental covariates have yet to fully exploit the potential of remote sensing data in SOM mapping. To address this, this study focuses on the typical black soil region in Northeastern China, acquiring median synthetic images from different time periods (crop sowing, growing, and harvest stages) along with vegetation and radar indices. Six data groups were created by integrating environmental covariate data. Four machine learning models—XGBoost, BRT, ET, and RF—were used to analyze the SOM prediction accuracy of different groups. The group and model with the highest prediction accuracy were selected for SOM mapping in cultivated land. The results show that: (1) in the same model, incorporating radar images and their related indices significantly improves SOM prediction accuracy; (2) when using four machine learning models for SOM prediction, the RF model, which integrates optical images, radar images, vegetation indices, and radar indices from the crop sowing and growing periods, achieves the highest accuracy (R² = 0.530, RMSE = 6.130, MAE = 4.822); (3) in the optimal SOM prediction model, temperature, precipitation, and elevation are relatively more important, with radar indices showing greater importance than vegetation indices; (4) uncertainty analysis and accuracy verification at the raster scale confirm that the SOM mapping results obtained in this study are highly reliable. This study made significant progress in SOM prediction and mapping by employing a radar–optical image fusion strategy combined with crop growth information. It helped address existing research gaps and provided new approaches and technical solutions for remote sensing-based SOM monitoring in regions with short bare soil periods. Full article

For addressing the technical challenges of photon denoising and water level extraction in ICESat-2 satellite-based water monitoring applications, this paper proposes an innovative solution integrating Gaussian function fitting with Hough transform. The method first employs histogram Gaussian fitting to achieve coarse denoising of water body regions. Subsequently, a probability attenuation model based on elevation differences between adjacent photons is constructed to accomplish refined denoising through iterative optimization of adaptive thresholds. Building upon this foundation, the Hough transform technique from image processing is introduced into photon cloud processing, enabling robust water level extraction from ICESat-2 data. Through rasterization, discrete photon distributions are converted into image space, where straight lines conforming to the photon distribution are then mapped as intersection points of sinusoidal curves in Hough space. Leveraging the noise-resistant characteristics of the Hough space accumulator, the interference from residual noise photons is effectively eliminated, thereby achieving high-precision water level line extraction. Experiments were conducted across five typical water bodies (Qinghai Lake, Long Land, Ganquan Island, Qilian Yu Islands, and Miyun Reservoir). The results demonstrate that the proposed denoising method outperforms DBSCAN and OPTICS algorithms in terms of accuracy, precision, recall, F1-score, and computational efficiency. In water level estimation, the absolute error of the Hough transform-based line detection method remains below 2 cm, significantly surpassing the performance of mean value, median value, and RANSAC algorithms. This study provides a novel technical framework for effective global water level monitoring. Full article

Aiming to address the problems of fewer related studies on autonomous navigation algorithms based on multi-sensor fusion in complex scenarios in pastures, lower degrees of fusion, and insufficient cruising accuracy of the operation path in complex outdoor environments, a multimodal autonomous navigation system is proposed based on a loosely coupled architecture of Cartographer–RTAB-Map (real-time appearance-based mapping). Through laser-vision inertial guidance multi-sensor data fusion, the system achieves high-precision mapping and robust path planning in complex scenes. First, comparing the mainstream laser SLAM algorithms (Hector/Gmapping/Cartographer) through simulation experiments, Cartographer is found to have a significant memory efficiency advantage in large-scale scenarios and is thus chosen as the front-end odometer. Secondly, a two-way position optimization mechanism is innovatively designed: (1) When building the map, Cartographer processes the laser with IMU and odometer data to generate mileage estimations, which provide positioning compensation for RTAB-Map. (2) RTAB-Map fuses the depth camera point cloud and laser data, corrects the global position through visual closed-loop detection, and then uses 2D localization to construct a bimodal environment representation containing a 2D raster map and a 3D point cloud, achieving a complete description of the simulated ranch environment and material morphology and constructing a framework for the navigation algorithm of the pushing robot based on the two types of fused data. During navigation, the combination of RTAB-Map’s global localization and AMCL’s local localization is used to generate a smoother and robust positional attitude by fusing IMU and odometer data through the EKF algorithm. Global path planning is performed using Dijkstra’s algorithm and combined with the TEB (Timed Elastic Band) algorithm for local path planning. Finally, experimental validation is performed in a laboratory-simulated pasture environment. The results indicate that when the RTAB-Map algorithm fuses with the multi-source odometry, its performance is significantly improved in the laboratory-simulated ranch scenario, the maximum absolute value of the error of the map measurement size is narrowed from 24.908 cm to 4.456 cm, the maximum absolute value of the relative error is reduced from 6.227% to 2.025%, and the absolute value of the error at each location is significantly reduced. At the same time, the introduction of multi-source mileage fusion can effectively avoid the phenomenon of large-scale offset or drift in the process of map construction. On this basis, the robot constructs a fusion map containing a simulated pasture environment and material patterns. In the navigation accuracy test experiments, our proposed method reduces the root mean square error (RMSE) coefficient by 1.7% and Std by 2.7% compared with that of RTAB-MAP. The RMSE is reduced by 26.7% and Std by 22.8% compared to that of the AMCL algorithm. On this basis, the robot successfully traverses the six preset points, and the measured X and Y directions and the overall position errors of the six points meet the requirements of the pasture-pushing task. The robot successfully returns to the starting point after completing the task of multi-point navigation, achieving autonomous navigation of the robot. Full article

In the four-way shuttle system, the efficiency of path planning directly affects the overall effectiveness of logistics and warehousing operations. Traditional path planning methods for multiple four-way shuttles do not take into account the fact that the map status will change as the inbound and outbound tasks are completed. To address this issue, a path planning algorithm for dynamic environments based on an improved Conflict-Based Search (CBS) mechanism is proposed. Firstly, by introducing turning constraints and a node expansion strategy, the A* algorithm is improved, reducing the number of turns and optimizing the node expansion process. Secondly, based on the improved A* algorithm, a path planning algorithm for dynamic environments based on CBS is designed. This algorithm adopts the inbound/outbound task priority strategy and the nearby-task priority strategy to resolve conflicts. It effectively manages the changes in the map status by establishing and maintaining a “ChangeList” and revises the path set of the four-way shuttles. Based on the layout of the intelligent vertical warehouse with four-way shuttles of a certain enterprise, simulation experiments were carried out using a rasterized map. The algorithm was compared with the DCBS-PFM and RRT-A algorithms, verifying the effectiveness and superiority of the algorithm. Full article

The traditional paradigm for studying urban morphology involves the interpretation of Nolli maps, using methods such as morphometrics and visual neural networks. Previous studies on urban morphology discovery have always been based on raster analysis and have been limited to the central city area. Raster analysis can lead to fragmented forms, and focusing only on the central city area ignores many representative urban forms in the suburbs and towns. In this study, a vast and complex dataset was applied to the urban morphology discovery based on the administrative community or village boundary, and a new image deformation pipeline was proposed to enhance the morphological characteristics of building groups. This allows visual neural networks to focus on extracting the morphological characteristics of building groups. Additionally, the research on urban morphology often uses unsupervised learning, which means that the learning process is difficult to control. Therefore, we refined the NT-Xent loss so that it can integrate morphological indicators. This improvement allows the visual neural network to “recognize” the similarity of samples during optimization. By defining the similarity, we can guide the network to bring samples closer or move them farther apart based on certain morphological indicators. Three Chinese cities were used for our testing. Representative urban types were identified, particularly some types located at the urban fringe. The data analysis demonstrated the effectiveness of our image deformation pipeline and loss function, and the sociological analysis illustrated the unique urban functions of these urban types. Full article