Search Results (1,983)

Satellite-derived ground deformation monitoring has become an essential tool for infrastructure management; however, the spatial heterogeneity of Interferometric Synthetic Aperture Radar (InSAR) observations limits reliable assessment in regions with sparse measurement coverage. While spatial transfer learning offers a practical means of extending deformation predictions beyond well-observed areas, existing approaches primarily emphasise predictive accuracy and provide little indication of whether transferred predictions can be trusted during operational deployment. This study presents an evidence-based reliability assessment framework for spatial transfer learning using European Ground Motion Service (EGMS) observations. The framework accompanies every prediction with complementary evidence reliability, transfer reliability, and validation-calibrated expected prediction error. The methodology was evaluated within a single 100 km × 100 km EGMS tile in Eastern England, characterised by gradual, subsidence-type ground motion, using 815 EGMS observations and deployed to assess deformation risk for 140 road corridors. The proposed local–transfer fusion achieved a mean RMSE of 1.720 mm yr⁻¹, outperforming multi-source transfer strategies. Feature–space divergence exhibited a significant positive relationship with transfer prediction error ($\rho =0.515$, $p<0.001$), demonstrating that environmental similarity is a stronger indicator of transfer success than structural similarity within the investigated study area. The proposed framework extends conventional transfer learning beyond prediction accuracy and provides a practical foundation for risk-informed infrastructure monitoring using satellite-derived ground deformation observations. Full article

Slow earthquakes represent an important mode of fault slip transitional between stable creep and dynamic rupture. Their occurrence is jointly controlled by mineral composition, pore-fluid pressure, effective normal stress, system stiffness, and microstructural evolution. Because the internal state of natural faults cannot be directly observed, studies of slow slip, tectonic tremor, and low-frequency earthquakes have long been challenged by weak signals, complex noise, and discrepancies in observational scales. Building on the physical foundations of rock friction and slip stability, this review summarizes recent applications of deep learning to laboratory friction and acoustic data, natural seismic waveforms, Global Navigation Satellite System (GNSS) observations, and strain measurements, with particular emphasis on event detection, fault-state estimation, rate-and-state friction parameter inversion, and forecasting of slip evolution. Existing studies have progressed from event identification to the reconstruction of shear stress, estimation of frictional parameters, and prediction of future fault states. Nevertheless, applications to natural faults remain dominated by event detection and catalog construction, whereas parameter inversion and forecasting still rely largely on laboratory experiments or synthetic data. Physics-informed neural networks, transfer learning, reduced-order modeling, and data assimilation provide promising pathways for integrating laboratory experiments, numerical simulations, and natural observations; however, their reliability remains limited by constitutive-model dependence, parameter non-uniqueness, domain shift, and insufficient independent validation. Future work should strengthen multi-observation integration, cross-region validation, and uncertainty quantification, while developing a bidirectional framework linking laboratory experiments, numerical simulations, and natural fault observations. At present, deep learning is better suited to fault-state characterization and probabilistic assessment of slip trends than to deterministic prediction of the exact timing of slow earthquakes. Full article

Remote sensing research increasingly relies on heterogeneous satellite, UAV, hyperspectral, multispectral, SAR, and environmental monitoring data to support land, urban, hydrological, and environmental applications. However, these data are often affected by sensor differences, spatial and temporal heterogeneity, missing observations, irregular sampling, noise, and non-stationary environmental processes. This systematic review was conducted within the context of the Romanian Hub for Artificial Intelligence (HRIA) project, which supports the development of strategic artificial intelligence technologies. The review synthesizes current research on adaptive neural networks for remote sensing and Earth observation, with particular attention to Liquid Neural Networks and related continuous-time neural models. A systematic search was conducted across IEEE Xplore, Scopus, Web of Science, ScienceDirect, SpringerLink, Wiley Online Library, Google Scholar, and reference lists. After duplicate removal, screening, and full-text assessment, 61 studies published between 2018 and 2026 were included in the qualitative synthesis. The findings show that adaptive neural networks have gained increasing attention after 2022 and are mainly applied to image-centered remote sensing tasks, including classification, mapping, object detection, segmentation, enhancement, and change detection. Most studies adapt established deep learning architectures through multi-scale processing, adaptive feature fusion, attention mechanisms, graph relationships, or task-specific refinement. Continuous-time models are used less frequently but are relevant for irregular observations and dynamic environmental processes. Liquid Neural Networks remain emerging, and current evidence suggests only preliminary, task-specific relevance for irregular, noisy, multimodal, and dynamic remote sensing applications. Full article

Traditional stochastic models that rely solely on elevation angle and signal-to-noise ratio (SNR) struggle to adapt to the precision differences in BDS-3 multi-frequency observations, making it difficult to support high-precision positioning in complex scenarios. To address the issues that existing models fail to adapt to the differentiated error characteristics of BDS-3 five-frequency observations, lack adaptive modeling capabilities, and cannot support high-precision five-frequency PPP-AR in complex environments, this study proposes a Transformer-based adaptive stochastic model for five-frequency precise point positioning ambiguity resolution (PPP-AR). Satellite elevation angle, the SNR, and position dilution of precision (PDOP) are used as inputs, while observation noise labels derived from pseudorange post-fit residuals support supervised training. The predicted noise standard deviations are introduced into the observation covariance matrix for adaptive weighting. To distinguish generalization from memorization, the model was evaluated using observations from different days. On day of year (DOY) 244, the Transformer model achieved a mean post-convergence three-dimensional root-mean-square (3D RMS) error of 0.029 m, outperforming the comparison models, which yielded errors of 0.0037–0.0039 m. It also reduced the mean convergence time to 20.33 min, compared with 21.22–22.17 min for the comparison models. At the HARB station, the Transformer and elevation angle models both converged in 7 min, only one 30 s epoch faster than the multilayer perceptron (MLP) and SNR models. At the GAMG station, the ambiguity fix rate reached 31.25%, exceeding those of the elevation angle and SNR models by 18.28 and 15.40 percentage points, respectively. The results for DOY 245 and DOY 246 further support short-term transferability, but not long-term temporal generalization. Overall, the proposed model improves aggregate positioning accuracy and convergence efficiency while maintaining competitive ambiguity fixing performance. Full article

Global Navigation Satellite System (GNSS)-based measurements are widely used for sports monitoring and outdoor activity analysis; however, consumer-grade smartphones often produce degraded trajectories, inaccurate elevation profiles, and unreliable pace estimates. This study proposes a multi-stage framework for reconstructing low-fidelity GNSS running trajectories using an averaged high-fidelity wearable GNSS reference proxy. The framework combines activity-window selection, trajectory filtering and route-consistent projection, reference-based elevation correction, and pace reconstruction using two complementary approaches: a Linear Acceleration Influence Model and a Physics-Based Model. The methodology was validated using three high-fidelity and three low-fidelity recordings collected on a shared 5.726 km route. Within the common activity window, raw low-fidelity observations had a pooled nearest-route RMSE of 30.72 m, whereas retained route-consistent assignments had a residual RMSE of 14.84 m. Aggregate pace agreement improved from 2.40 to 1.74 min/km RMSE and from 32.84% to 25.46% MAPE. Raw smartphone elevation had a pooled RMSE of 190.27 m relative to the adopted reference profile, supporting reference-based elevation substitution. A constant-velocity Kalman RTS baseline reduced positional RMSE from 30.72 to 29.05 m (5.4%), whereas the complete route-association procedure eliminated severe backtracking that remained after distance thresholding alone. The proposed framework provides a transparent and reproducible solution for reconstructing sparse consumer-grade GNSS activities while preserving explicit uncertainty. Full article

Multi-object tracking (MOT) in satellite video is fundamentally limited by low target observability: genuine targets often produce weak and unstable responses, while structured backgrounds can generate persistent target-like interference, leading to missed detections, fragmented trajectories, and identity switches. To address this ambiguity, we propose a weak-observation-aware and reliability-guided framework with a layered two-stage design: the front end enhances weak observations over short temporal windows, whereas the back end controls their use for long-term trajectory association and state updating according to their reliability. The front-end Temporal Prior Module (TPM) constructs a structure-aware temporal prior from motion-aligned historical evidence, strengthening weak-target responses while limiting the propagation of structured-background interference. The back-end Satellite Weak-Observation Reliability-Guided Tracker (SWRT) introduces active-track support into short-window reasoning and combines current, short-term, and long-term evidence into a candidate-reliability score that regulates association eligibility and state-update strength. On VISO, the proposed method achieves a multiple object tracking accuracy (MOTA) of 72.5% and an identity F1 score (IDF1) of 81.1%. On SATMTB-MOT, it achieves the best MOTA and IDF1 for airplanes and vehicles and remains competitive for ships among the compared methods. These results demonstrate that this layered design achieves a better balance between target recovery and identity preservation under low-observability conditions. Full article

Ionospheric scintillation and radio frequency interference (RFI) affect Global Navigation Satellite System (GNSS) signals, making their distinction important for ionospheric monitoring and interference detection. This study identifies GNSS signal anomalies associated with scintillation and RFI using COSMIC-2 1 Hz precise orbit determination (POD) observations. Using 60 s per-satellite windows, dual-frequency time-series inputs and 16-dimensional statistical features were extracted, and weak labels for Normal, Scintillation, and RFI were generated from the amplitude scintillation index S₄ and the RFI index. An InceptionTimeLite-FiLM-DeepSets joint multi-satellite model, where FiLM denotes feature-wise linear modulation, was trained and tested on 2024 data and directly applied to the full-year 2025 dataset. On the 2024 test set, recall for Normal, Scintillation, and RFI was 98.9%, 76.1%, and 58.5%, respectively, with a Macro-F1 of 0.8227 and a Matthews correlation coefficient of 0.7120. Predicted Scintillation occurrence rates were higher at low magnetic latitudes and during the postsunset premidnight period, whereas predicted RFI occurrence rates were concentrated over North Africa, the Middle East, South Asia, and Southeast Asia. These results show that, within the weak-label framework, COSMIC-2 1 Hz POD observations can support GNSS signal anomaly classification and spatiotemporal distribution analysis, providing a complementary approach for long-term anomaly analysis. Full article

Ground-based GNSS tomography reconstructs three-dimensional tropospheric water-vapor fields from slant observations. However, unconstrained solutions rely heavily on station and satellite geometry. This study introduces a GNSS-only approach that uses precise SP3 orbit products to create pseudo-slant observations in satellite directions not tracked by individual receivers. These directions combine with existing IWV and horizontal-gradient estimates. This completes the ray distribution without adding external atmospheric constraints or independent water-vapor information. The method is tested in Hong Kong and Iceland with GPS-only, GLONASS-only, observed multi-constellation, and SP3-completed setups. GPS-derived gradients were generally in line with the full multi-GNSS solution, but GLONASS-only gradients showed larger differences. Overall, the mapped directions improved or maintained the inversion’s effective rank and numerical stability. They did not systematically degrade the retrieved water-vapor profiles, although the advantages decreased when the additional rays were geometrically redundant. Full article

Under increasing cropland constraints and pressure to secure oilseed supplies, using winter–fallow cropland for winter rapeseed production can improve annual cropland-use efficiency. Taking the Wanjiang Plain, China, as the study area, this study integrated 10 m winter–fallow cropland maps (2019–2024), 30 m winter rapeseed maps (2000–2022), annual Satellite Embedding features, cropland data, and administrative boundaries. Multi-year occurrence frequency and Getis–Ord Gi* statistics characterized temporal persistence and spatial clustering. A MaxEnt model calibrated with 394 occurrence records from long-term high-frequency rapeseed areas and 26 screened embedding dimensions delineated cropland with present-day land-surface characteristics similar to historically persistent rapeseed locations. This layer was progressively intersected with the historical winter–fallow union and stable winter–fallow cropland. Stable winter–fallow cropland covered approximately 4437 km², whereas long-term high-frequency winter rapeseed covered only 568 km², revealing a marked spatial mismatch. Under five-fold spatial cross-validation, the selected linear-feature model with a regularization multiplier of 4 achieved a mean test AUC of 0.904 ± 0.015 and a 10% training-omission rate of 0.108 ± 0.022. Using the model-specific threshold of 0.3096, the final estimates were 6017 km² of potentially suitable cropland, 2943 km² of general expansion potential, and 1137 km² of candidate spatial priority areas for field verification. The last tier was concentrated mainly in Xuanzhou District, Lujiang County, urban Wuhu, Wuwei City, Nanling County, and He County. The framework provides a cautious spatial-screening basis for optimizing winter–fallow cropland use and guiding subsequent field and feasibility assessments. Full article

In densely vegetated and topographically complex mountainous areas, the applicability of SAR data for potential landslide hazard identification depends not only on whether slopes are visible to the radar, but also on whether stable interferometric coherence can be preserved under vegetation and terrain constraints. To clarify the applicability differences between L-band Lutan-1 and C-band Sentinel-1 in such environments, this study focused on Hanyuan County, Sichuan Province, China. Ascending and descending SAR images acquired by the two satellite systems from 2024 to 2025 were processed using stacking-based Interferometric Synthetic Aperture Radar (Stacking-InSAR) and Small Baseline Subset Interferometric Synthetic Aperture Radar (SBAS-InSAR) to extract regional deformation anomalies and time-series deformation characteristics of representative landslides. DEM, LiDAR, optical imagery, fractional vegetation cover (FVC) derived from Sentinel-2, and field investigation data were further integrated to establish a comparative framework linking geometric visibility, interferometric coherence, and landslide identification results. The results show that both Lutan-1 and Sentinel-1 provided favorable geometric observation conditions after combining ascending and descending tracks, with joint visibility proportions of 98.48% and 97.76%, respectively, indicating limited differences in geometric coverage within the study area. However, at a unified grid scale, the mean coherence and valid grid-cell proportion of Lutan-1 reached 0.564 and 72.49%, respectively, substantially higher than those of Sentinel-1, which were 0.320 and 24.26%. As FVC increased, coherence decreased for both datasets, but Lutan-1 maintained higher coherence in densely vegetated areas, suggesting stronger adaptability to vegetation-induced decorrelation. Based on integrated interpretation of multi-source remote sensing data, 77 potential landslide hazards were identified in the study area, including 74 detected by Lutan-1, 17 detected by Sentinel-1, and 14 jointly detected by both datasets. Comparisons of representative landslides further show that Lutan-1 provided a higher density of valid deformation points in densely vegetated and small-scale landslides, with deformation patterns corresponding well to slope geomorphic boundaries and local deformation zones. Sentinel-1, with its higher temporal sampling density, can provide complementary information for time-series verification and multi-source cross-validation of key landslides. These results indicate that Lutan-1 is more suitable for spatial identification of potential landslide hazards in densely vegetated, topographically complex mountainous areas, while the joint use of Lutan-1 and Sentinel-1 can better balance landslide identification detail and time-series monitoring continuity. Full article

Multi-decadal trajectories of large arid-zone reservoirs are seldom described by integrated satellite observation. This study characterises the optical and thermal variability of Lake Nasser, Egypt, over 2005–2025. Eight monthly MODIS indicators—NDVI, EVI, NDWI, NDTI, a near-infrared reflectance index, white-sky albedo, and day- and night-time land surface temperature (LST)—together with the derived diurnal temperature range (DTR), were compiled in Google Earth Engine over 186 cells of a 5 km grid and analysed by non-parametric trend, change-point and correlation procedures under false-discovery-rate control, correction for serial correlation, and effective-sample-size significance testing. Because a fixed polygon cannot separate environmental change from shoreline migration, every trend was recomputed on four domains of decreasing shoreline exposure. Three signals survive on all four: night-time LST rises (+0.51 to +0.76 °C decade⁻¹), DTR contracts (−0.83 to −1.95 °C decade⁻¹) and albedo declines (−0.0034 to −0.0193 decade⁻¹). The NDVI, EVI, near-infrared and day-time LST declines that the fixed polygon reports are not reproduced under the control, whereas NDWI declines (−0.048 to −0.077 decade⁻¹) only once it is applied. Change-point tests date a shift in level to 2016–2017, three years before the first filling of the Grand Ethiopian Renaissance Dam. Full article

Large-area remote-sensing data provide essential pre-mission information for unmanned ground vehicles, but their spatial support and temporal latency may obscure local terrain changes. A remaining challenge is to translate heterogeneous regional evidence and recent local observations into a consistent, updateable, and planner-ready map. This study presents a satellite–unmanned aerial vehicle (UAV) workflow for constructing and incrementally maintaining an off-road traversability map for mission-level global planning. A common H3 index organizes satellite imagery, terrain, soil, road evidence, and local UAV semantic observations while retaining their native spatial support and provenance. The map separates environmental-prior, semantic, and traversability-cost layers to support interpretable fusion and independent updating. A confidence-hierarchical conflict resolution mechanism resolves inconsistencies in the regional prior, while an observer-agnostic interface projects UAV semantic observations onto local map cells. RGB imagery is used by the primary UAV observer, and digital surface model (DSM) is evaluated as an optional semantic-observation modality. Evaluation included a manually reviewed regional benchmark, a unified buffered spatial holdout, cell-level update assessment, and 40 fixed replanning tasks. Conflict resolution reduced high-risk omissions. RGB-only SegFormer-B2 achieved the highest semantic accuracy with moderate computational complexity. UAV override achieved a cell-level F1 score of 96.96% and limited the false-positive accumulation associated with conservative union. Replanning further revealed a trade-off between hazardous-cell avoidance and search-graph connectivity. The proposed workflow provides a maintainable interface between multi-source remote sensing and global UGV planning rather than a replacement for onboard perception, local obstacle avoidance, or vehicle control. Full article

Floods degrade road networks at the same time as demand for emergency medical services (EMSs) rises, yet national EMS command systems rarely receive any information on flood-induced road barriers. This paper presents Ambulance STARS (SaTellite-assisted Ambulance Routing System), a service-oriented framework that links satellite observation with ambulance dispatch. A cloud-based flood detection service derives flood extent from Sentinel-1 SAR amplitude change detection executed in a cloud-based Earth observation data and compute backend and translates it into road passability layers. A routing engine then maintains an in-memory road graph whose travel times are calibrated with empirical ambulance speed models built from four years (2020–2023) of GPS records of an EMS fleet in southern Poland, with separate speeds for driving with and without emergency signals (61.8 and 37.2 km/h, respectively). An API gateway with single-file tile delivery, a replicated relational data tier, and an observability stack complete the architecture, and a web client offers dispatchers live routing and multi-unit incident simulation. The framework was tested on the September 2024 flood in the Municipality of Nysa, Poland. The SAR module delineated 665 ha of inundation and marked 8.5 km of the 656.7 km routing network as impassable (508 barrier points), and the same procedure applied to a reference optical mask of 18 September yielded 17.3 km and 1006 points. Because the SAR and optical acquisitions captured different phases of the flood wave, agreement on the rare impassable-road class was low, and the two products were, therefore, used to bracket operational uncertainty rather than to define a single ground truth. Applied without local retuning to Lewin Brzeski, the same flood detection workflow showed consistent performance against the CEMS reference product. The routing module produced statutory 8/15/20 min accessibility maps in 12–34 s under warm-cache benchmark conditions. With SAR-derived barriers, the share of the network reachable within 15 min fell from 88% to 80%, and 2 villages with 938 inhabitants lost road access to EMS entirely. With barriers derived from the optical mask, the 15 min share fell to 39.8% and seventeen settlements lost road access entirely, underlining how strongly the barrier source shapes the operational picture. Post-acquisition processing completes in under one minute under warm-cache conditions with road data preloaded, and satellite-derived road passability is fast enough to support near-real-time decision-making, subject to the constellation revisit time and to integration with EMS command systems. Full article

Marine remote sensing combines broad-coverage satellite observations of the ocean surface with sparse in-situ and underwater observations. However, reconstructing continuous three-dimensional environmental fields remains challenging because of irregular sampling, vertical non-stationarity, and bathymetric barriers. In this paper, a multi-granularity graph collaborative neural network (MG-GCNN) is proposed for high-fidelity field reconstruction and situational awareness in sparse monitoring scenarios. The network abstracts discrete observation points as heterogeneous nodes in the topological graph structure. By incorporating high-resolution bathymetry as a geometric prior, terrain-aware graph construction, vertical feature integration, multi-granularity aggregation, and self-supervised masked node reconstruction are jointly used to capture spatial and vertical dependencies under limited observation availability. Experimental results show that MG-GCNN significantly outperforms baseline interpolation and convolution models in terms of reconstruction accuracy, especially in regions with complex underwater terrain and extreme sampling sparsity. The reconstructed environmental fields can potentially provide three-dimensional environmental inputs for subsequent ocean-acoustic propagation modeling, underwater sensing, and related marine applications. Full article

With the increase in global fishing activities, the transparency of fishery data has drawn increasing attention. Data transparency enables stakeholders to play a greater role in ensuring that fisheries are legal, ethical, and sustainable. Improving the transparency of fishing vessel data can effectively support vessel monitoring and enhance fishery safety. This paper presents a systematic review of satellite remote sensing modalities and datasets currently available for fishing vessel identification and monitoring. Conducted in accordance with the PRISMA 2020 guidelines, this study employs a dual-track search strategy to retrieve, screen, and synthesize academic literature and public datasets from mainstream databases, including the Web of Science Core Collection, IEEE Xplore, and CNKI. First, the existing remote sensing modalities were classified into three major categories based on their imaging principles: synthetic aperture radar (SAR), optical remote sensing, and nighttime light (NTL) remote sensing. In addition, the mainstream satellite data sources and their corresponding parameters were summarized for each category. Second, an in-depth comparative analysis of these remote sensing modalities is conducted from core dimensions such as target detection sensitivity, robustness under complex environments and meteorological conditions, and spatiotemporal resolution. This reveals the performance limitations and significant complementarity of different sensor data in fishing vessel detection. Finally, mainstream remote sensing datasets for fishing vessels (such as xView3-SAR, xView, and VBD) are summarized and evaluated, pointing out the gaps in certain types of datasets. In conclusion, this paper suggests that building a “full spatiotemporal and multi-scale” observation framework based on multi-source heterogeneous data fusion is an important trend for the future development of fishing vessel detection using remote sensing, aiming to provide a reference for relevant researchers. Full article