Search Results (18,463)

Rapid and reliable assessment of structural damage following disasters is critical for prioritizing rescue operations. In this study, we present a unified deep learning framework for building damage assessment from satellite imagery. The proposed approach integrates segmentation-driven feature construction, morphological processing, cost-sensitive learning, and cross-disaster evaluation to support robust performance under limited and imbalanced data conditions. The framework combines an adapted U-Net for building localization with a hybrid convolutional neural network (CNN)-deep neural network (DNN) classifier for damage-level prediction and evaluates transferability across disaster events, geographic regions, and sensing conditions. The proposed method is evaluated on selected events from the xView2 Building Damage Assessment (xBD) and BRIGHT datasets, using optical imagery from xBD and pre-disaster optical and post-disaster Synthetic Aperture Radar (SAR) imagery from BRIGHT. Despite the limited and highly imbalanced event-specific samples, the framework achieves a mean cross-validation macro-F1 score of 70% and a maximum fold-level score of 77% on the Mexico earthquake subset of xBD and up to 98% on earthquake-related events in BRIGHT. Cross-validation characterizes performance variability across source-image-grouped data partitions, while cross-disaster evaluation reveals event-dependent transferability and provides a preliminary indication that structural domain similarity may be related to transfer performance. Although the evaluation is constrained by data availability, the results indicate that lightweight, cost-sensitive deep learning frameworks may support auxiliary post-disaster screening and decision support in resource-constrained scenarios. This study highlights both the potential and the remaining challenges of deploying artificial intelligence (AI) for rapid post-disaster assessment. Full article

Forest inventory compartments provide extensive and management-relevant reference information for satellite-based tree species mapping, but their dominant-species attributes are defined at the stand level rather than for individual image pixels. Existing applications commonly derive training samples from compartment centres or assign polygon labels to enclosed pixels, which may introduce boundary effects, uneven class representation, and disproportionate contributions from individual compartments. However, the intermediate step of converting inventory polygons into spatially controlled pixel-level reference samples has received comparatively limited attention. Here, we developed a geometry-constrained reference sample construction framework that integrates interior-position screening, class balancing, source compartment contribution control, and spatial-spacing constraints. The framework was evaluated for mapping Korean pine, larch, white birch, and spruce in a temperate mixed forest in northeastern China using Sentinel-1/2 time series and ancillary predictors. Predictor–classifier combinations were selected using compartment-grouped out-of-fold evaluation, sampling workflows were compared on 60 independently withheld compartments, and the final map was further assessed using 306 independent reference points. Relative to centroid sampling, the geometry-constrained workflow increased compartment-level macro-F1 from 0.612 to 0.709. XGBoost with optical time series and ancillary predictors achieved the best development-set performance, while inclusion of the complete Sentinel-1 time series provided no further gain. The final model achieved an overall accuracy of 0.827 and a macro-F1 of 0.820 on the independent reference points. Aggregation of 10 m predictions further enabled compartment-level characterization of mapped dominant species, dominance strength, and mixing intensity. These results demonstrate that reference sample construction is a consequential step in tree species mapping from polygon-based forest inventories and provide a practical approach for linking pixel-level remote sensing classification with forest management units. Full article

Accurate assimilation of satellite-derived precipitation data remains a critical challenge in regional numerical weather prediction (NWP), particularly for convective-scale rainfall. Conventional observation operators rely on radiative transfer models or simplified moist physics, introducing substantial uncertainty at convective scales. This study develops a machine learning-based observation operator and implements a “one-dimensional variational (1D-Var) + three-dimensional variational (3D-Var)” framework to assimilate FY-3G/PMR precipitation data into the CMA-MESO. Results show the machine learning-based operator demonstrates high accuracy for light, moderate, and heavy rain (BIAS < 0.5 mm/h, RMSE < 2.5 mm/h) and exhibits good generalization capability across different weather systems. Through the two-step assimilation, the retrieved humidity profiles improve initial moisture fields. Both the single case study and the one-month continuous cycling experiment consistently show that the assimilation yields measurable improvements in short-term precipitation forecasts, particularly for extreme precipitation events, with a maximum TS improvement of 19.9% for severe torrential rain. This work demonstrates that the machine learning-based observation operator exhibits potential in precipitation data assimilation, offering a viable pathway to enhance NWP. Full article

Driven by the escalating global agricultural workforce shortage and the urgent need to meet the “Zero Hunger” mandate, the automation of harvest–transport workflows has emerged as a cornerstone of Agriculture 4.0. This paper highlights the latest research progress in multi-robot collaborative logistics scheduling across highly unstructured farming environments, underpinned by cutting-edge spatial perception and digital twin frameworks. Initially, we summarize the technological leap from conventional 2D geometric mapping to multi-modal semantic 3D reconstruction—fusing light detection and ranging (LiDAR), unmanned aerial vehicle (UAV) imagery, and spatial data—to enable high-fidelity forward-looking predictions. The discussion then transitions to algorithmic advancements, emphasizing the shift from traditional centralized operations research to decentralized, data-driven approaches such as Multi-Agent Reinforcement Learning (MARL). We also explore micro-kinematic predictive control mechanisms and the growing integration of ecological sustainability metrics into routing models. To demonstrate practical engineering progress, multi-agent implementations are analyzed across three typical spatial settings: high-throughput continuous relays in open fields, global navigation satellite system (GNSS)-denied discrete routing in dense orchards, and close-proximity human–robot collaboration (HRC) in smart greenhouses. Finally, we identify the remaining barriers to the large-scale commercialization of Agricultural Cyber-Physical Systems (ACPS), such as the “Sim-to-Real” gap restricted by edge-computing capacities, unclosed economic loops, and HRC ethical dilemmas, offering a forward-looking roadmap for next-generation resilient agricultural networks. Full article

Owing to their geographical dispersion and high deployment costs, distributed photovoltaic (DPV) stations often lack access to high-precision meteorological data, which degrades power forecasting accuracy and threatens grid operational stability. Accordingly, developing a low-cost ultra-short-term power forecasting model is critical for power system dispatching. This study proposes a site-level ultra-short-term power forecasting method incorporating satellite cloud imagery (SCI) for DPV systems. First, three core forecasting challenges are analyzed: cross-modal data correlation establishment, spatial alignment between heterogeneous data structures, and spatiotemporal correlation extraction among dispersed stations. Second, a forecasting model taking satellite imagery and multi-station power outputs as inputs is constructed. It aligns multimodal data via a data embedding layer, and integrates multi-source features through cuboid self-attention modules and an encoder–decoder architecture. Third, a Multi-Scale Correlation Mechanism (MSCM) is proposed to capture spatiotemporal associations across geographically dispersed stations. Experimental results based on a real-world dataset from Hebei Province show strong performance. The full model with satellite cloud imagery achieves a Root Mean Square Error (RMSE) of 0.112 and a Mean Absolute Error (MAE) of 0.059, outperforming baseline models including the backpropagation (BP) neural network, long short-term memory (LSTM) network, and Transformer model. Full article

The use of remote sensing data in hydrological applications has increased, especially in regions with limited ground-based observations. Satellite precipitation products (SPPs) provide extensive temporal and spatial coverage but may contain biases that can affect their performance in hydrological simulations. This study evaluates the performance of four near-real-time SPPs for daily streamflow modeling in the Tulijá River Basin (TRB), Mexico: Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN)-Cloud Classification System (CCS), PERSIANN-Dynamic Infrared Rain Rate near real-time (PDIR-Now), and the Early Run and Late Run products of the Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (GPM) (IMERG). The SPPs were first compared with meteorological station precipitation data and subsequently bias-corrected using the Linear Scaling (LS) method. The CEQUEAU hydrological model simulated streamflow using three precipitation datasets: meteorological stations, original SPPs, and bias-corrected SPPs. For simulations using observed precipitation, the model was calibrated for 1991–2014 and validated for 1968–1990; for SPP-based simulations, calibration and validation were performed for 2003–2011 and 2012–2014, respectively. Model performance was assessed using the Nash–Sutcliffe efficiency (NSE), percent bias (PBIAS), and coefficient of determination (R²). The results show that CEQUEAU performance varies by precipitation dataset. Simulations using observed precipitation yielded NSE values close to 0.70 during both calibration and validation, whereas the original SPPs yielded NSE values below 0.18, including negative values. After bias correction, IMERG-Early and IMERG-Late yielded NSE values of approximately 0.55 during both periods. These findings highlight the importance of analyzing the performance of near-real-time SPPs in hydrological applications, especially in tropical regions with complex topography. Full article

To address Global Navigation Satellite System (GNSS) spoofing threats to Inertial Navigation System (INS)/GNSS integrated navigation systems, this paper analyzes the internal error propagation mechanisms and quantifies perturbation patterns within the Kalman filter (KF) architecture. Mathematical models for step-type, linear ramp, and nonlinear smooth ramp spoofing are established, and the Anomaly Signal-to-Noise Ratio (ASNR) is adopted to quantify disturbances across four core filtering dimensions based on real-world vehicular test data. The results demonstrate that filtering innovations at the forefront of information fusion respond most directly and sensitively (peaking at an ASNR of 341.4181) with a standard zero-mean Gaussian baseline, serving as the optimal metric for spoofing detection; in contrast, error states exhibit marked amplitude attenuation (maximum ASNR of 116.3291), while filter gains and state covariance show negligible variations (maximum ASNRs of 22.6023 and 19.3014, respectively). Further evaluation of Inertial Measurement Unit (IMU) accuracy constraints reveals that under step spoofing, position innovations remain robust (ASNR: 260–510), whereas velocity innovation ASNR drops by approximately 50% with IMU degradation; under linear ramp spoofing, velocity innovations dominate the response (ASNR: 41.16–70.46) while position innovations decay markedly; and under nonlinear smooth ramp spoofing, overall innovations are suppressed, and low-grade IMUs suffer severe noise masking (peak horizontal ASNRs dropping below 10), significantly enhancing attack stealthiness. The findings provide quantitative empirical evidence and theoretical guidance for anti-spoofing design in integrated navigation. Full article

With the rapid growth of the New Space era, the increasing number and diversity of Low Earth Orbit (LEO) satellites demand generic and reusable antenna control software capable of stable and accurate tracking across heterogeneous orbital and antenna environments. Previous works have primarily focused on two-axis antenna systems, relying on step-based tracking or Two-Line Element (TLE)-based orbit prediction. However, such approaches suffer from manual initialization requirements and gimbal lock at high elevation angles, limiting continuous tracking performance. Although three-axis antenna structures have been proposed to address these issues, their lack of software-level generality and experimental validation restricts practical reuse across antenna platforms. This paper presents a generic three-axis parabola antenna control software that integrates TLE-based orbit prediction, a gimbal lock avoidance algorithm, and an antenna specification reflection function. By decoupling antenna-specific parameters from the core tracking logic, the proposed system enables flexible adaptation to diverse antenna configurations. Experimental validation using a real three-axis antenna and simulations demonstrates high tracking accuracy, achieving an average gain error within 0.466 dBm for GEO-KOMPSAT-2A and stable signal strength above 55 dBm for the LEO satellite ARIRANG-5. Furthermore, analysis of 11,469 gimbal-lock-prone satellites confirms robust avoidance performance across diverse orbital conditions. Full article

Networks of permanent GNSS (Global Navigation Satellite System) reference stations generate large volumes of observation and ephemeris files, as well as the position estimates and quality indicators derived from them. These data are still typically stored as structured files, which makes them difficult to query, trace and integrate with geographic information systems (GIS). To provide an alternative, this article presents GeoGNSS-PS, a PostGIS-based information system for permanent GNSS stations that treats positioning results, processing origin and spatial geometry as first-class entities within a normalised relational schema. Estimated positions are stored as three-dimensional geometries in the ECEF (Earth-Centered Earth-Fixed) reference frame. Each record is identified by station, date, constellation and estimation method, whilst also storing other data such as the equipment at each station, the reference frame and epoch of its published coordinates, and the software and ephemeris product used in each run. The schema and its analytical functions in SQL (Structured Query Language) are initialised from a single declarative description. The system is evaluated using twenty-eight stations from the Spanish Network of GNSS Reference Stations (ERGNSS, as it is known in Spanish) every day throughout the year 2025, producing 190,725 daily positions in 74 MB. In addition, five compact SQL queries express patterns for analysing thes data. The system is compared against a file-based baseline, another alternative spatial database hosting the analytical core of the same schema, and against synthetic datasets of up to ${10}^7$ rows. The proposed system excels in aggregations built on spatial primitives and in client memory, whilst the alternative spatial database matches its performance times but is unable to express several of the patterns in SQL. Full article

Pasture quality monitoring is essential for optimizing grazing management and reducing the incidence of nutritional and metabolic disorders in ruminants, yet conventional field-based measurements remain labor-intensive and limited in spatial coverage. This review examines how remote sensing (RS) technologies can support pasture-based livestock systems by providing timely, scalable assessments of biomass, botanical composition, and nutritive attributes. Data from multispectral, hyperspectral, radio detection and ranging (RADAR), and light detection and ranging (LiDAR) sensors, acquired via satellite, unmanned aerial vehicle (UAV), and proximal platforms, are combined with machine learning (ML) methods and radiative transfer models to derive pasture biophysical and quality indicators. The reviewed evidence shows that RS reliably estimates pasture biomass and structural traits, while advances in spectral unmixing, data fusion, and artificial intelligence (AI) improve the characterization of heterogeneous swards and support emerging indicators related to forage quality. Integrating these remotely sensed metrics into grassland decision-support frameworks can enhance grazing allocation, inform fertilization and irrigation decisions, and help detect conditions associated with nutritional imbalances. Overall, the synthesis demonstrates that RS, particularly when combined with advanced modelling and cloud-based processing, offers a robust pathway for improving pasture monitoring and strengthening the nutritional management of ruminants, thereby supporting more sustainable and animal welfare-focused grazing systems. Full article

Low Earth Orbit (LEO) satellite systems provide wide-area Earth coverage and constitute an important component of the integrated space–air–ground network. With the increasing demand for near-data real-time task processing at the near-data end, constructing a reliable resource management solution is crucial to guaranteeing efficient task execution. Considering the frequent networking due to high-speed movement, unbalanced dynamic task allocation, and unknown resource requirements, we propose a hierarchical dynamic cross-domain collaborative resource management architecture considering satellite access. To enable low-cost access authentication over narrowband inter-satellite links, this work proposes a non-interactive privacy-preserving authentication scheme. Each satellite aggregates four committed orbital attributes into one compact proof, which can be verified by multiple authorized satellites within a domain and epoch. Concurrent access requests are handled via verifier-side batch verification of independent proofs. Additionally, considering the imbalance and dynamics of satellite task allocation, a hierarchical resource management scheme combining multi-agent reinforcement learning intra-domain resource allocation and inter-domain early-exit matching game task collaboration is proposed to alleviate the differences in inter-domain task allocation and enable efficient task execution under limited satellite resources. Simulation results show that the proposed scheme has lower overall communication overhead and an improved task completion rate in dynamic scenarios. Full article

Sedimentation is eroding the water security of reservoirs in hydrologically active basins: the Poechos reservoir (Peru) has lost 62% of its original 887.7 hm³ capacity in 48 years, yet its Elevation–Area–Volume (EAV) curve is refreshed only by bathymetric surveys at decade-plus intervals, compromising flood regulation and the water supply for over 100,000 ha of farmland. To close this gap, we propose an integrated, low-cost, fully reproducible framework that reconstructs the EAV curve from freely available satellite data: Sentinel-1 SAR (287 acquisitions, 2021–2026), PlanetScope imagery as ground truth (23 dates), and Surface Water and Ocean Topography (SWOT) altimetry (53 validated passes, 2023–2026). Water surfaces were delineated with a deep learning segmentation model (Feature Pyramid Network with an InceptionV4 encoder), selected among nine architecture–encoder combinations and calibrated to a 0.64 decision threshold, achieving a 90.66% Intersection over Union (IoU) and a 95.10% F1 score; a stochastic quantile mapping algorithm then asynchronously coupled the area and elevation series. The resulting EAV curve matched daily operational records from Peru’s National Water Authority (ANA) with high precision (NSE = 0.94, R² = 0.96, and RMSE = 25.93 hm³); the residual bias (BIAS = −11.23 hm³) reflects active sedimentation unaccounted for in the official curve. This bias peaked at an accumulated deficit of 24.5 hm³ during the 2023–2024 hydrological year (3.5 hm³/year), of which up to 19.6 hm³ is attributed to the 2023 Yaku cyclone as a phenomenologically scaled upper-bound estimate (9.8–19.6 hm³ across 40–80% attribution fractions), since SWOT was not yet operational during the event. Updating every 21 days under any weather and requiring no new field campaigns beyond the baseline bathymetric anchor, the trained ensemble was further transferred zero-shot to three additional reservoirs (San Lorenzo, Tinajones, and Gallito Ciego), demonstrating a scalable path from infrequent static assessments to near-continuous, dynamic monitoring of water storage. Full article

The sustained decline in Japanese Manila clam landings has increased interest in complementary bivalve food resources. This descriptive study compared one pooled soft-tissue analytical sample from each of four sources: Ruditapes philippinarum from Akkeshi, Mangoku-ura, and a commercial Chinese-origin source, and the warm-water clam Ruditapes variegatus from Ishigaki Island. Each pooled sample comprised approximately 250 g of soft tissue from at least 40 individuals; independent lot-level biological replication was not available, so no inferential statistical comparisons were made. Proximate composition, amino acids, taurine, vitamin-related compounds, minerals, and fatty acids were analyzed, and mitochondrial cytochrome c oxidase subunit I (COI) sequencing together with morphology confirmed the Ishigaki material as R. variegatus. Wet-weight total amino acid contents were 8.670, 6.800, 6.135, and 5.555 g/100 g in the Akkeshi, Ishigaki, Mangoku-ura, and Chinese-origin pooled samples, respectively. After normalization, total amino acid contents ranged from 507.0 to 559.4 mg/g dry matter and from 829.1 to 850.0 mg/g protein, indicating that much of the wet-weight contrast reflected differences in tissue moisture and protein concentration. Adult-reference amino acid scores ranged from 97.1 to 100 across the four composites. The Akkeshi pooled sample also showed comparatively high taurine and vitamin A-related values. Sea surface temperature and satellite chlorophyll-a data were used only to document source-region environmental context and were not treated as causal predictors of composition. Within the limits of this pooled-sample survey, the food-composition profile of taxonomically verified Ishigaki R. variegatus supports further replicated evaluation of this species as a complementary clam-like food resource. Full article

Accurate quantification of forest biomass in the karst mountainous region of Southwest China is critical for regional carbon sink accounting. However, remote sensing-based inversion of forest biomass in this region is subject to the dual constraints of scale effects and spatial heterogeneity. Taking two typical karst landforms—plateau karst and peak-cluster depression karst—as case studies, this study developed an inversion framework that integrates multi-scale synergy (plot—small watershed—region) with geomorphological zoning modeling. Specifically, the high-precision forest aboveground biomass (AGB), retrieved by integrating high-resolution satellite imagery (2 m) of small watersheds with field plot data, was used as the scale-conversion bridge. The dominant class variability-weighted method was applied to upscale the spatial resolution from 2 m to 30 m. Subsequently, Landsat-8 OLI imagery, land use/land cover data, and topographic factors were integrated to construct landform-specific neural network models, namely BPANN-GY for plateau karst and BPANN-FC for peak-cluster depression karst, with validation RMSEs of 11.02% and 12.39%, respectively. The results showed that the mean forest AGB in the plateau karst region was 110.66 t·ha⁻¹ in 2013 and 111.46 t·ha⁻¹ in 2024; in the peak-cluster depression karst region, the mean forest AGB was 123.98 t·ha⁻¹ in 2014 and 125.57 t·ha⁻¹ in 2024. Compared with the baseline years (2013/2014), total forest AGB in both regions increased significantly by 2024, up to 11.43% in the plateau karst region and 6.43% in the peak-cluster depression karst region. Spatially, the most marked increase in AGB occurred in the mid-to-high slope zones, while the differences in forest AGB among slope grades gradually narrowed, indicating progressively enhanced forest structural integrity and functional stability under effective land management. This study methodologically validates the scientific soundness and feasibility of using high-precision AGB retrieved from high-resolution satellite imagery as a scale-conversion bridge. The established AGB inversion system provides a methodological framework and data foundation for carbon sink accounting, ecological restoration, and land management in the karst region of Southwest China, and offers a transferable approach for forest AGB inversion in other highly heterogeneous landscapes. Full article

Existing state cadastral land valuation methodologies often focus exclusively on economic factors while ignoring geo-environmental hazards. This paper proposes a mechanism for incorporating seismic risk indicators and environmental degradation into the cadastral model to improve pricing accuracy. Using four cadastral blocks in the Bostandyk District of Almaty as a case study, the authors applied MAVT and AHP methods, integrating seismic microzonation data and satellite monitoring (Sentinel-2, Landsat 8) to calculate a comprehensive risk coefficient (Krisk). According to spatial analysis data, the presence of natural hazards serves as a valid criterion for reducing the cadastral valuation of vulnerable land plots by 20–45%. The proposed methodology ensures a balance between market valuation and environmental safety, representing a scalable tool for urban land management in seismically active zones. Full article