TerraMosaic Daily Digest: July 31, 2026
Daily Summary
Today's landslide studies distinguish where slopes fail from when they move. In the eastern Tibetan Plateau, slope-unit averaged channel steepness tracks landslide occurrence probability but correlates weakly with InSAR deformation rate, indicating that fluvial incision sets a long-term spatial boundary condition rather than present velocity. A 104-year Calabrian inventory tests whether rainfall thresholds transfer across gauges, while LiDAR-derived tree lean and electrical resistivity connect surface kinematics to saturated clay-rich zones in an active Indonesian landslide.
Flood research treats missing terrain and observations as physical constraints on the answer. A Duero River dataset joins surveyed bathymetry to sub-metre urban geometry; paired 500-year simulations show that omitting either changes the mapped hazard. In Quito, conserved natural cover reduces simulated peak flow by 73-85% across scenarios, although the apparent saturation of extreme rainfall remains model-dependent. A review of 264 data-scarce assessments then formalizes how hazard, exposure and vulnerability methods should be combined, and a post-fire UAV-SWMM-HEC-RAS workflow resolves where burned surfaces and drainage networks amplify urban runoff.
High-resolution evidence is also narrowing causal claims: sub-annual eastern African lake proxies place the Toba super-eruption in Northern Hemisphere winter and indicate less than two years of modest cooling and acute drought, rather than a prolonged regional catastrophe. Reliability is moving from an accuracy statistic to a system property. A geohazard digital-twin framework makes accuracy, transparency, reproducibility and accountability joint requirements; tests of Prithvi inside GIS and Python show that accessibility can be preserved without hiding environment-dependent variation. In seismology, a machine-learning-assisted workflow relocates 11,374 Tehuantepec aftershocks, adaptive station selection extends mean warning time from 10.5 to 12.2 seconds without raising error, and moment-rate comparisons identify where geodetic strain can credibly inform earthquake budgets.
Key Trends
Five methodological shifts connect today's work across landslides, floods, earthquakes and compound climate hazards.
- Process Controls Are Being Separated by Timescale: Channel incision explains long-term landslide location more clearly than current InSAR velocity, preventing susceptibility and active motion from being conflated.
- Transfer Is Becoming an Empirical Question: Rainfall thresholds, multimodal landslide predictors and foundation-model workflows are tested across gauges, sites, sensors or software environments rather than assuming that local skill will travel.
- Observation Gaps Are Entering the Model Explicitly: Sub-annual lake proxies, urban bathymetry, sparse flood records and heterogeneous seismic networks are treated as measurable constraints on causal attribution, hazard estimates and warning time.
- Sensor Fusion Is Becoming Mechanistic: LiDAR and resistivity, SAR and HAND, and temporary and permanent seismic networks are combined to test physical interpretations, not merely to add input channels.
- Governance Is Entering Technical Validation: Digital twins and GeoAI deployment are evaluated for provenance, reproducibility, transparency and accountable use alongside predictive performance.
Selected Papers
The strongest contributions connect process evidence to decision-ready models: they separate long-term landslide controls from present motion, turn missing urban terrain into reusable flood inputs, and define explicit reliability tests for geohazard digital twins.
1. Linking Fluvial Incision and Landslide Activity in a Tectonically Active Landscape
Core Problem: The long-term coupling between river incision and landslide activity is difficult to isolate from lithologic, structural, topographic and meteorological controls.
Key Innovation: A new landslide inventory, slope units, channel-steepness analysis and InSAR show that slope-unit averaged steepness predicts landslide occurrence but not current deformation rate, separating long-term location control from present motion.
2. Ethical considerations for geohazard digital twins
Core Problem: Machine-learning components are entering geohazard digital twins faster than standards for judging their accuracy, transparency, reproducibility and accountability.
Key Innovation: The review provides a life-cycle assessment framework that connects data provenance, uncertainty, model limitations and institutional responsibility to trustworthy digital-twin deployment.
3. Data integration of urban surface and river bathymetry to support flood risk management
Core Problem: Flood models often omit street-scale urban geometry, river bathymetry and under-bridge flow paths, creating terrain errors that directly alter simulated inundation.
Key Innovation: Airborne LiDAR, cadastral geometry and RTK-GNSS echosounder surveys are integrated into a reusable 1 m terrain-bathymetry dataset with validation and uncertainty layers; paired 500-year simulations quantify the effect of the corrections.
4. Trends in geo-hydrological risk to the population of Italy
Core Problem: Long, homogeneous records are rarely available to determine whether geo-hydrological mortality has changed structurally or which climatic and social factors explain the trend.
Key Innovation: A 1950-2024 Italian catalogue shows a sharp decline in fatalities and fatal days until about 1970 followed by stabilization, with landslides driving most of the decline and precipitation controlling much of the remaining year-to-year variability.
5. Sub-annual resolution evidence for limited impact of the 74-ka Toba eruption on eastern African climate
Core Problem: The climatic and ecological consequences of the 74-ka Toba super-eruption remain disputed because few geological archives resolve the event at sub-annual scale.
Key Innovation: Sub-annually resolved eastern African lake proxies constrain the eruption to Northern Hemisphere winter and indicate modest cooling plus acute drought lasting less than two years, separating the volcanic perturbation from a longer background drying trend.
6. Transferability of Rainfall Triggering Conditions for Shallow Landslides: Insights from Calabria, Southern Italy
Core Problem: Rainfall thresholds derived in data-rich municipalities may not transfer to nearby areas because gauges, topography, climate and exposure-biased historical reporting differ.
Key Innovation: A 104-year inventory containing 467 records and 41 shallow-landslide events tests 1-, 3- and 5-day rainfall conditions across stations, identifying where threshold consistency supports transfer and where local factors weaken it.
7. Detecting Reactivation Mechanisms of an Active Landslide Using LiDAR-Derived Stem-Lean Metrics and Electrical Resistivity Tomography
Core Problem: Surface deformation indicators alone cannot reveal whether saturated subsurface materials are driving reactivation in thick clay-rich volcanic soils.
Key Innovation: UAV-LiDAR stem-lean metrics and electrical resistivity tomography jointly locate coherent surface motion and 3-30 ohm-m saturated zones, linking cracks and flow accumulation to recharge of the weak layer.
8. A Deep Learning Integrated Stacked Ensemble Framework for Futuristic Landslide Prediction using Multimodal Sensor Data
Core Problem: Short-horizon landslide movement prediction must combine heterogeneous sensors while detecting rare movement classes under severe imbalance.
Key Innovation: DISEL stacks machine-learning base models, deep meta-learners and a LightGBM blender with temporal features and focal loss, using environmental and geotechnical measurements from five Himachal Pradesh sites for prediction up to 10 minutes ahead.
9. A sensitivity analysis of a Landslide to assess the influence of shear strength parameters and groundwater conditions on slope stability of Kaande Landslide
Core Problem: At Kaande, uncertainty in shear strength and groundwater conditions obscures which parameters most strongly shift the failure threshold across representative slope sections.
Key Innovation: Phase2 strength-reduction and one-at-a-time sensitivity tests across three profiles show that friction angle dominates stability, groundwater level ranks second, and low-magnitude cohesion contributes less in the sampled SM-SC soil.
10. Attention-driven Deep Fusion for Enhanced Landslide Susceptibility Mapping Using Multi-source Remote Sensing Data
Core Problem: Multi-source landslide imagery is heterogeneous, and it is unclear whether attention should fuse sensor information before feature learning or after separate decisions.
Key Innovation: Attention-driven early and late fusion are compared on Landslide4Sense and HR-GLDD; early fusion performs best in the reported combined-data tests, reaching 98% accuracy, 96.50% Dice and 93.24% intersection over union.
11. Estimación de umbrales y dinámica de inundaciones integrando Random Forest, el índice SDWI de Sentinel-1, el modelo HAND y datos SAR en la cuenca del río Tumbes Estimation of thresholds and flood dynamics integrating Random Forest, the Sentinel-1 SDWI index, the HAND model, and SAR data in the Tumbes River Basin
Core Problem: Persistent cloud and sparse instrumentation in tropical basins make it difficult to move from one-off flood maps to spatial inundation estimates tied to river discharge.
Key Innovation: Random Forest and the Sentinel-1 SDWI index are combined with HAND across 13 events in the Tumbes basin, reaching 91.0% overall accuracy, 0.80 F1 and 69.0% IoU; a discharge-area relation estimates overflow extent with 0.87 km² RMSE.
12. Green Infrastructure as a Climate Shield: Nonlinear Flood Response and Extremes Saturation in a Tropical Andean Urban Watershed
Core Problem: Flood-risk modelling in tropical mountain cities must combine sparse observations, climate projections and land-cover change without hiding scenario and model uncertainty.
Key Innovation: A CHIRPS-CMIP6-GEV-HEC-HMS workflow finds that conserved natural cover reduces peak flows by 73-85% across scenarios in Quito, while identifying an extreme-rainfall saturation signal that still requires convection-permitting validation.
13. Navigating Data Scarcity in Flood Risk Assessment
Core Problem: Data-scarce flood studies use inconsistent definitions of risk and scarcity, leaving no context-specific basis for choosing hazard, exposure and vulnerability methods together.
Key Innovation: A systematic review of 264 studies defines data scarcity, inventories available indicators and sources, and derives a three-step procedure for selecting compatible methods across the three components of flood risk.
14. Equatorial wave embedded within the Madden-Julian oscillation leading to the 2021 Peninsular Malaysia flood
Core Problem: The respective roles of synoptic forcing, the Madden-Julian Oscillation, Kelvin waves and equatorial Rossby waves in the 2021 Peninsular Malaysia flood are difficult to separate because the modes covary.
Key Innovation: Regression against a 2006-2020 GPM baseline attributes the largest independent rainfall contribution to Kelvin waves while retaining joint MJO and Rossby-wave effects; both simple and multiple regressions reproduce the anomaly with correlation 0.79.
15. Post-Fire Urban Runoff Assessment in a Mediterranean Basin Using Integrated UAV–SWMM–HEC-RAS Modelling
Core Problem: Forest fire changes infiltration, roughness and drainage response, but post-fire urban runoff is difficult to represent where conventional terrain and gauge data are sparse.
Key Innovation: A 0.2 m UAV surface model, 5 cm orthomosaic and field observations drive a calibrated SWMM-HEC-RAS-2D chain that maps how vegetation loss, hydrophobic soil and drainage geometry alter runoff and flood-prone areas.
16. Performance evaluation and limitations assessment of GeoAI democratization for natural hazard induced disasters
Core Problem: GIS interfaces make Earth-observation foundation models accessible, but users need evidence that outputs remain reproducible and methodologically transparent outside the interface.
Key Innovation: Prithvi burn-scar and flood models are compared in commercial GIS and standalone Python; burn mapping agrees more strongly than flood mapping, exposing the trade-off among accessibility, reproducibility and implementation control.
17. Constructing a High-Resolution Aftershock Catalog for the 2017 Mw 8.2 Tehuantepec Earthquake Sequence Using a Machine Learning–Based Workflow
Core Problem: The offshore Tehuantepec aftershock sequence was under-resolved despite temporary stations, limiting depth control and separation of slab and crustal seismicity.
Key Innovation: PhaseNet and GaMMA detections are combined with conventional velocity modelling and relocation to produce 11,374 relocated earthquakes from seven months of data, including the first full use of the temporary network.
18. Improving the PLUM Method with DATES: Delaunay-Based Adaptive Technique for Earthquake Early Warning
Core Problem: PLUM earthquake early warning depends on a fixed station-selection radius, which degrades coverage and lead time in sparse or uneven networks.
Key Innovation: DATES uses Delaunay triangulation to adapt reference stations to local density and azimuthal coverage, achieving full coverage and increasing mean lead time from 10.5 to 12.2 seconds without increasing error in the 2023 Turkiye-Syria test.
19. How to Determine an Earthquake Rate Budget? A Comparison of Geodetic, Geologic, and Seismologic Moment Rates
Core Problem: Using geodetic strain in seismic hazard analysis requires knowing where moment accumulation agrees with the earthquake record preserved by geology and seismology.
Key Innovation: A floating-footprint comparison across the western United States defines moment-rate deviation and finds better budget agreement in well-characterized, high-strain plate-boundary regions than in lower-strain intraplate areas.
20. Evolution of crustal deformation before the Longxi M S 5.6 earthquake based on GNSS observations
Core Problem: The deformation sequence preceding the 2025 Longxi MS 5.6 earthquake was not resolved well enough to distinguish steady regional loading from localized fault locking.
Key Innovation: GNSS velocity, strain, cross-fault baselines and regional deformation fields reveal northeast-southwest shortening and a multi-parameter deceleration about three years before rupture, consistent with a transition toward stronger locking on the Zhangxian segment.
21. Operational strategies for producing reliable seismic intensity information for earthquake response in the Korean Peninsula
Core Problem: A denser Korean seismic network does not by itself guarantee reliable response-grade intensity maps because installation effects and Japan-sourced subduction motions can violate local ground-motion assumptions.
Key Innovation: The study evaluates borehole and MEMS deployment, site and structural effects, and far-field records, then specifies standardized metadata, quality control and subduction-appropriate ground-motion models for operational intensity production.
22. Discovery of basaltic and magnesian andesite magmas within the Hunga volcanic system, Tonga arc
Core Problem: The Hunga volcanic plumbing system was inferred largely from subaerial andesites, leaving the deeper magma diversity involved in the 2022 eruption unresolved.
Key Innovation: Rapid-response dredging recovers rocks from basalt to rhyolite, including basaltic and magnesian andesite groups not represented by the earlier subaerial record, supporting coexisting primary basaltic and andesitic magmas beneath Hunga.
23. Tracing the pathways from drought to wildfire: Compound and cascading hot-dry hazard processes in a Mediterranean climate
Core Problem: Same-day compound indices can miss the multi-week climatic preconditioning that links drought and heatwaves to wildfire occurrence.
Key Innovation: ERA5 data for 1970-2025 show that matched fires rise from 5.05% with a 7-day cascading window to 22.28% with a 30-day window, with heatwave-to-wildfire the dominant sequence and moderate discriminatory skill.
24. Future wildfire risk in Southern Europe under changing land use, population, and climate: a data-driven approach
Core Problem: Future wildfire risk cannot be inferred from climate alone because land cover and population change alter ignition and exposure patterns in scenario-dependent ways.
Key Innovation: An XGBoost model driven by hydro-meteorology, land cover, human activity and terrain projects limited end-century change under SSP1-2.6 but increased Southern European risk under SSP3-7.0, with population and land-cover trajectories modulating the result.
25. Rogue wave early warning using a triple-stream bilinear-gated fusion model with wave buoy data
Core Problem: Rogue-wave warnings must detect rare, rapidly developing extremes from noisy buoy records without creating an operationally costly false-alarm rate.
Key Innovation: Three representations of sea-surface elevation are combined through bilinear interaction and gated fusion, allowing the warning model to preserve complementary temporal signals while suppressing false alarms.
26. State-of-the-art review on seismic design considerations of floating offshore wind turbines
Core Problem: Floating wind systems are expanding into seismically active deep water, but design guidance remains fragmented across shaking, liquefaction, fault rupture, submarine landslides, seaquakes and tsunamis.
Key Innovation: The review consolidates hazard-specific mechanisms, code provisions and unresolved modelling needs into a common seismic-design agenda for floating foundations and their coupled mooring-turbine systems.
27. Ecosystem-based disaster solutions for reducing climate risk in urbanizing tropical watersheds: effectiveness of policy-relevant alternatives for the new capital of Indonesia
Core Problem: Indonesia's new capital is converting the forested Sanggai watershed to urban land, but the flood- and drought-reduction benefit of proposed ecosystem-based measures lacked quantitative testing under climate change.
Key Innovation: Hydrological scenarios show Eco-DRR reducing daily and monthly streamflow by 29% and 21%, flood events by more than 75%, and maximum drought duration by as much as 78% relative to urbanization without those measures.
28. Seismic wave field reconstruction using recorded data from near-surface and in-structure seismic sensors
Core Problem: Soil-structure interaction analysis needs a site-specific seismic wavefield, yet sparse surface or downhole measurements cannot directly describe motion throughout the soil and structure.
Key Innovation: A PDE-constrained inversion implemented in Real-ESSI reconstructs displacement fields from surface, downhole and in-structure sensors; tests show that structural sensors materially improve reconstruction of building response.
29. An Improved LSTM Time-Series Approach for Forecasting Debris Flow Events
Core Problem: Existing debris-flow warning methods struggle to deliver timely forecasts from numerous correlated environmental and geological variables.
Key Innovation: Field observations are standardized and reduced to eight dominant factors with principal-component analysis before LSTM forecasting; reported errors remain below 0.06 for training, 0.12 for testing and 0.19 for validation against conventional methods.
30. Landslide Risk Assessment of Geological Hazards in Bengbu City Based on Hyperband-CatBoost and SHAP Interpretability Framework
Core Problem: Located on the Jianghuai Plain, Bengbu features complex geology.
Key Innovation: To address the limitations of limited labeled samples, complex factor coupling, inefficient hyperparameter tuning and low interpretability of traditional machine learning methods, this study proposes an integrated landslide risk assessment framework. Comparative tests with LightGBM and Random Forest show that Hyperband-CatBoost achieves the highest test accuracy (0.9630).
31. Predictive Modelling and Optimization of Slope Stability Using Numerical Simulations and Machine Learning Techniques
Core Problem: Repeated numerical slope-stability simulations are costly when reinforcement design must span many combinations of cohesion, friction angle, unit weight and slope geometry.
Key Innovation: PLAXIS LE simulations train Random Forest, linear-regression and k-nearest-neighbour surrogates; Random Forest performs best with MAE 0.053, MSE 0.006 and R² 0.957 for factor-of-safety prediction.
32. Investigation of complex phenomena related to expansive soils : the most severe geohazard in South Africa and many parts of the world
Core Problem: This University of Pretoria research record investigates the complex behaviour of expansive soils, a major ground-deformation hazard in South Africa and many other regions.
Key Innovation: It addresses the geotechnical processes that make moisture-sensitive soils swell, shrink and damage foundations and infrastructure.
33. Deep Learning Models for Flood Detection in Nepal: Challenges and Insights
Core Problem: Climate-driven increases in flood frequency and intensity have made rapid and reliable flood monitoring essential for effective emergency response.
Key Innovation: In this study, a 5-channel U-Net was developed for flood mapping in Nepal, using multi-temporal Sentinel-1 SAR data and hydrologically relevant topographic information. The input configuration combined pre-flood VV and VH backscatter, post-flood VV and VH backscatter, and Height Above the Nearest Drainage (HAND) data to improve the discrimination of flooded and non-flooded areas.
34. Investigating the Effects of SPH Numerical Parameters for Dam-Break Flood Prediction
Core Problem: SPH dam-break forecasts depend on numerical choices whose influence on water-surface elevation can be mistaken for physical uncertainty.
Key Innovation: Three-dimensional Cleveland Dam simulations with LiDAR terrain and Sobol indices rank time integration as the dominant tested control, identify a particle-resolution plateau near 298,188 particles, and show little sensitivity to artificial viscosity between 0.2 and 0.3.
35. State of knowledge and future needs for mitigating flood risk of masonry structural systems
Core Problem: Masonry standards offer little flood-specific design or retrofit guidance despite substantial coastal exposure to storm surge and wave loading.
Key Innovation: Evidence on one- and two-way fluid-structure interaction is combined with a survey of 45 masonry experts to prioritize masonry-specific guidance, experimental validation and data-driven design tools.
36. Spatiotemporal mapping of surface water variability for flood risk and water infrastructure management in the Gongola river basin (2000–2025)
Core Problem: Flood-risk planning in the Gongola basin requires a single view of how land-cover change, rainfall variability, terrain and surface-water dynamics have evolved since 2000.
Key Innovation: A 2000-2025 geospatial synthesis links forest loss and urban-farmland expansion to lower infiltration and higher runoff, and estimates a gradual rise in mapped flood-prone area from 33.22% to 34.0%.
37. Remote Sensing of Flood-Driven Water Quality Degradation Using Sentinel-2 MSI in Southern Brazil
Core Problem: The May 2024 floods in southern Brazil required rapid evidence of both inundation extent and flood-driven water-quality deterioration without simultaneous field sampling.
Key Innovation: Before-during-after Sentinel-2 imagery tracks seven-day water expansion and spatially coherent increases in CDOM and DOC proxies at flood peak, while explicitly limiting the result to qualitative patterns because in situ validation was unavailable.
38. Climate Transition Zones as Emerging Hotspots for Natural Hazards: Insights from Land Use- Climate Feedbacks Amplify Disaster Risk in Taiwan
Core Problem: Static land-cover or climate maps cannot show whether shifting climate-class boundaries concentrate landslide and flood occurrence.
Key Innovation: Satellite land cover, CHIRPS-MODIS climate classes and a 2001-2020 disaster inventory show transition zones occupying 15% of Taiwan but recording 2.8 times the disaster frequency of stable zones, with warming transitions containing more than half of typhoon-induced landslides.
39. Windstorm Hazard Index Development for Malaysia
Core Problem: Peninsular Malaysia lacks a location-specific index that translates windstorm drivers into hazard classes suitable for mitigation planning.
Key Innovation: AHP and PCA combine observations, WRF-ARW simulations and Envi-MET urban morphology into a six-level index whose 2020-2024 validation places the highest hazard in northern and coastal Kedah, Perlis and Penang.
40. The impact of urban wetland landscape patterns on runoff: A comparative study of three Chinese cities with frequent extreme rainfall
Core Problem: Urban flood planning typically measures wetland area but has limited evidence on whether patch density, edge, shape and connectivity independently alter runoff under extreme rainfall.
Key Innovation: Unified InfoWorks ICM scenarios across three Chinese cities show that wetland cover, patch density, edge density and connectivity are negatively associated with runoff, whereas greater shape complexity is positively associated with it.
41. Enhanced seasonal soil moisture forecasts by integrating APCC multi-model ensemble predictions into a land surface model framework
Core Problem: Skillful forecasts at these scales, however, remain a significant challenge.
Key Innovation: To bridge this gap, we developed a seasonal soil moisture prediction system using the Joint UK Land Environment Simulator driven by NCEP CFSv2 meteorological forecasts, and integrated monthly temperature and precipitation forecasts from the APCC MME—which exhibits superior seasonal prediction skill to single dynamical models—into the system’s meteorological forcing.
42. The impact of AMOC SST fingerprints on tropical storm risk along the U.S. East and Gulf coasts and Latin America
Core Problem: Uncertain sea-surface-temperature fingerprints of Atlantic overturning change propagate into uncertainty about where future tropical cyclones may form.
Key Innovation: CESM2 perturbations of the North Atlantic Warming Hole and Gulf Stream warming produce seasonally dependent changes of roughly plus or minus 10% in genesis potential across parts of the western North Atlantic, Gulf of Mexico and Caribbean, mainly through humidity and wind shear.
43. Damage and rainfall databases associated with Cyclone Harry (18–21 January 2026) in Calabria, Southern Italy
Core Problem: Event loss and rainfall observations are often separated, preventing direct comparison of meteorological forcing with the spatial pattern of compound cyclone damage.
Key Innovation: A Zenodo release pairs 195 georeferenced damage records from five hazard classes with event and long-term observations from 87 Calabrian rain gauges for Cyclone Harry.
44. Household Flood Impacts and Future Risk Perceptions in Ndanu, Kinshasa: Survey Dataset Following the April–May 2025 Floods
Core Problem: Household-level evidence is needed to connect experienced urban flood impacts with residents' perceptions of future risk after the 2025 Kinshasa floods.
Key Innovation: The survey dataset preserves primary household responses from Ndanu following the April-May 2025 floods for analysis of damage, perceived future risk and community vulnerability.
45. Data and Scripts Supporting the 2022 Yangtze Flash Drought Study
Core Problem: Attribution of the 2022 Yangtze flash drought requires vertically resolved circulation, moisture-source and soil-moisture evidence rather than a single drought index.
Key Innovation: The repository combines water-column, vertical-motion, moisture-flux and WAM-2layers attribution fields with root-zone soil-moisture percentiles and PCMCI+ outputs against a 1991-2020 baseline.
46. Seismic Microzonation of the Peshawar Metropolitan Area, Pakistan, Based on One-Dimensional Nonlinear Site Response Analysis
Core Problem: Foreign code amplification factors may misrepresent Peshawar's local soil response because they were derived for different impedance and seismic conditions.
Key Innovation: Forty-two penetration-test profiles, 50 SPT-Vs correlations and 20 ground motions support nonlinear one-dimensional microzonation; the Pakistani code overestimates short-period amplification for 12 profiles and long-period amplification for all 16 analysed profiles.
47. Seismic lateral earth pressure distribution on a hunched-back retaining wall under pseudo-static conditions
Core Problem: Prior research on the determination of seismic lateral earth pressure distribution along hunchedback walls is limited.
Key Innovation: The proposed method accounted for backfill soil friction angle, unit weight, wall roughness, failure plane inclination, wall inclination, and horizontal and vertical seismic ground accelerations. Results showed a non-linear distribution of lateral earth pressure decreasing to nearly zero at the wall base and were in agreement with the experimental results of earlier studies.
48. What’s New from the Recent MW = 4.8 Earthquake in the Lesina Village Area (Apulia, Southern Italy)? Focal Mechanisms and Regression Analysis with Implications for the Seismogenic Source
Core Problem: The fault responsible for the 2025 Lesina offshore earthquake sequence remains uncertain in a coastal area with debated historical tsunami sources.
Key Innovation: Focal mechanisms and three-dimensional regression of 91 hypocentres identify a best-fit surface aligned with one representative nodal plane, constraining the geometry considered in regional seismotectonic and hazard studies.
49. Impact of Earthquake Migration on the Structure and Dynamics of Diffuse Seismicity in Some Sections of the Tan-Lu Fault System
Core Problem: The influence of migrating earthquake activity on diffuse minor seismicity along the Tan-Lu fault system is difficult to resolve from individual event locations.
Key Innovation: Earthquakes of magnitude 2.5 and above from 1960-2024 are analysed by interval to estimate migration directions and velocities, identify active periods and classify interactions between migration chains and scattered seismicity in Priamurye.
50. Analysis of Seismo-Ionospheric Anomaly Disturbance Associated with the Mw7.6 Mexico Earthquake on 19 September 2022
Core Problem: Candidate pre-earthquake ionospheric anomalies must be distinguished from disturbances driven by solar and geomagnetic activity before they can inform seismic-ionospheric hypotheses.
Key Innovation: A sliding-quartile detector and wavelet analysis compare total electron content with solar and geomagnetic indices; the authors identify a day-10 disturbance without concurrent space-weather anomalies, while presenting the precursor interpretation cautiously.
51. StEER: June 24 Venezuela Earthquake Sequence Preliminary Virtual Reconnaissance Report (PVRR), in 2026 Venezuela Earthquake Sequence
Core Problem: Rapid earthquake reconnaissance must organize heterogeneous remote evidence without presenting provisional observations as field-verified fact.
Key Innovation: The StEER report assembles source-traceable evidence on the June 2026 Venezuela sequence across structures, infrastructure, geotechnical systems, coasts and lifelines, while explicitly reserving conclusions for later field corroboration.
52. Disaster Vulnerability in an Ageing Rural Society: The 2024 Noto Peninsula Earthquake
Core Problem: Physical damage alone cannot explain why the 2024 Noto earthquake produced severe impacts and uneven recovery in an ageing, depopulating rural society.
Key Innovation: Documentary evidence is integrated across demographic ageing, infrastructure, care dependence, health systems, gender and place-based culture, showing how these factors jointly shaped vulnerability and recovery capacity.
53. From “informal mass assault” to digital coordination: revisiting disaster roles in the age of social media – the case of the 2017 Mexico earthquake
Core Problem: Classical disaster-role theory predates social media and does not explain how citizens self-organize or connect to formal authorities during digitally mediated response.
Key Innovation: Social-network analysis of more than 5,000 Twitter nodes from the first 72 hours after the 2017 Mexico earthquake finds strong relational but weak role knowledge and identifies citizen hub Verificado19s as more central than some formal channels.
54. New data on Metaima monogenetic volcanic field in the Central Cordillera, Colombia
Core Problem: The Metaima Monogenetic Volcanic Field (MMVF), located on the eastern flank of the Central Cordillera of Colombia, represents one of the least studied monogenetic volcanic fields in the northern Andes, despite its proximity to densely populated areas.
Key Innovation: The study presents new geological, morphometric, stratigraphic, geophysical and seismological data that improve understanding of its eruptive history, age and volcanic hazard potential.
55. Sand and dust storm disaster risk reduction framework for Kuwait
Core Problem: Frequent sand and dust storms in Kuwait create recurring environmental and public-safety impacts, but risk reduction requires a coordinated framework rather than isolated response measures.
Key Innovation: The study organizes the sand-and-dust-storm hazard into a disaster-risk-reduction framework for Kuwait, linking hazard understanding with preparedness, mitigation and response planning in an arid national context.
56. Grey Swans and Ambiguity: Causes of Failure in Natech Risk Collaborative Response Network
Core Problem: As a compound risk, Natech exhibits chain, cluster, and concurrent characteristics, accompanied by a certain degree of complexity, uncertainty, and ambiguity, and is typically classified as a gray swan.
Key Innovation: The study introduces the four stages of resilience governance into social network analysis and constructs a dynamic evolutionary framework for the Natech collaborative response network.
57. Uncertainty‐Aware Machine Learning for Onset of Deep Convection: Under what Conditions Are Trigger Predictions More Reliable?
Core Problem: Machine Learning (ML) models have emerged as a powerful tool for predicting deep convection triggering, yet the atmospheric conditions that systematically challenge these models in detecting deep convection remain poorly understood.
Key Innovation: To diagnose such ambiguous regimes, we trained a Controlled Abstention Neural Network (CAN) that separates high‐ and low‐confidence predictions, enabling the physical characterization of uncertain environments over the Southern Great Plains during the warm season.
58. PromptScaleDINO: Prompt-Stabilized and Scale-Aware Adaptation of Grounding DINO for Infrared Small Target Detection
Core Problem: Direct transfer of vision-language detectors to few-pixel infrared targets fails when prompts underdescribe target variation, generic queries ignore scale and IoU losses become unstable for tiny boxes.
Key Innovation: PromptScaleDINO adds an anchor prompt bank, scale-aware query refinement and NWD-GIoU localization; on IRSTD-1k it reaches 88.32% F1 and 87.40% mAP@0.5, improving the baseline by 2.56 and 4.00 points.
59. A Multidimensional Hotspot Assessment of Long-Term Terrestrial Water Storage Anomaly Change Across China
Core Problem: Linear trend maps cannot jointly represent shifts in mean terrestrial water storage, interannual variability and the frequency of wet and dry extremes.
Key Innovation: An SED hotspot metric integrates all three dimensions across three 1961-2020 reconstructions, consistently identifying northwestern China, the North China Plain and the southwest basin while exposing sensitivity to normalization.
60. Seismic response of prefabricated CFST stiff skeleton column-bent cap joint: experimental evaluation and lateral strength model
Core Problem: When prefabricated bridge piers are applied in high seismic risk regions, seismic safety becomes one of the primary limiting factors.
Key Innovation: Accordingly, this paper developed a prefabricated CFST stiff skeleton column-bent cap joint, in which the stiff skeleton was embedded in the column. The results indicated that the joint’s failure mode involved buckling and fracture of the column limb steel tubes at the connection surface, exhibiting excellent energy dissipation capacity, lateral strength, and displacement ductility.
61. Numerical investigation on the seismic response of precast concrete beam–column joints with fiber-reinforced post-cast segments
Core Problem: Precast concrete joints with different lap-splice details need numerical models that capture anchorage slip, fiber-reinforced post-cast concrete and joint-panel shear within one cyclic analysis.
Key Innovation: An OpenSees component model combines modified reinforcement laws and an MCFT-inspired shear panel, reproducing six experimental specimens well for interior joints and identifying asymmetric boundaries and interface damage as the main exterior-joint limitations.
62. Experimental study on the seismic performance and design optimization of precast piers with various connection types
Core Problem: Connection choice and axial load can shift plastic hinges and alter ductility in precast bridge piers, but comparable quantitative evidence across connection systems is limited.
Key Innovation: Quasi-static tests on five 1:3 piers show plastic zones 1.3-1.5 times taller than the cast-in-place reference with peak-load reductions below 4.05%; a 10% axial ratio accelerates stiffness degradation by 62%.
63. Natural hazard perception and preparedness in a rural, complex jurisdiction: A case study in southwestern British Columbia, Canada
Core Problem: Although substantial research has examined behavioural drivers of preparedness and the operational constraints of emergency management systems, these dimensions are often studied separately, limiting understanding of how individual and institutional factors operate together.
Key Innovation: The study examines both dimensions in the Squamish-Lillooet Regional District, a rural, multi-hazard region in southwestern British Columbia, Canada.
64. Household disaster preparedness in Germany: The roles of preparedness engagement and living conditions
Core Problem: Household disaster preparedness is increasingly framed as a whole-of-society responsibility, yet it remains unclear how preparedness gaps are shaped by both deliberate engagement and structural living conditions.
Key Innovation: The study examines household preparedness in Germany across three dimensions: essential supplies, emergency equipment, and emergency skills. Engagement with disaster preparedness is positively associated with all three forms of preparedness, but engagement-related differences capture only part of the observed inequalities.
65. Disaster risk reduction education in Indonesia: Territorial conversion factors and public policy processes
Core Problem: Discussions of so-called successes and failures in public policy processes for disaster risk reduction (DRR) sometimes neglect to account for the effects of local contexts and socio-historical events when explaining what influences contemporary policy engagement.
Key Innovation: Applying the concept of territorial conversion factors as a theoretical lens, we analyse interviews with a range of officials and associated documentation (policy, regulations) to trace the regional development plans and effects of socio-historical forms on DRRE policy processes.
66. Bridging authority and citizens: An integrated framework of social-mediated disaster communication
Core Problem: Existing research on disaster communication largely focuses on governments, mainstream media, and citizens, while overlooking entertainment-driven actors such as social media influencers and community fan pages.
Key Innovation: The study examines how disasters are reframed when narrated by storytellers rooted in entertainment culture. The results show that government agencies and the press play the role of “information legitimizers” while community fan pages and influencers function as new “intermediary actors” that reshape disaster discourse through intimate, humorous, or emotional storytelling.
67. Global comparison of different effective scattering albedo and soil roughness parameterization schemes for SMAP soil moisture and L-VOD retrieval
Core Problem: SMAP soil-moisture and L-band vegetation-optical-depth retrievals depend strongly on scattering-albedo and roughness choices that differ among operational algorithms.
Key Innovation: A global comparison of five albedo and five roughness parameter sets uses triple collocation and 557 in situ sites to derive new pixelwise choices, improving median soil-moisture correlation to 0.75 and mean ubRMSD to 0.057 m³/m³.
68. DINO-Pheno-Cluster: Integrating few-shot foundation models and UAV time-series for spatiotemporal growth and functional characterization of alfalfa
Core Problem: However, traditional plot level and coarse scale observations suffer from low signal to noise ratios particularly before canopy closure when phenotypic data are strongly affected by weeds and soil background.
Key Innovation: To address this, the DINO-Pheno-Cluster framework is introduced as a foundation model driven and mechanism decomposed phenotyping approach. Validation utilized high frequency Unmanned Aerial Vehicle (UAV) imagery from 12 time points across three growing seasons covering 127 alfalfa accessions.
69. Macro-element model-based limited-ductility seismic design methodology for scoured bridge pile-group foundations in cohesionless soils
Core Problem: Bridge pile-group foundations are typically designed as capacity-protected members intended to remain elastic during seismic events.
Key Innovation: To address these issues, this study proposes a systematic, applied limited-ductility seismic design methodology for scoured bridge pile-group foundations using the macro-element concept. These springs are characterized by multilinear backbone curves that capture the actual nonlinear load-deformation behavior of the foundation.
70. Performance of the “center of elasticity based three modal component” as a novel method in seismic response estimation of one-way eccentric multistory buildings
Core Problem: Simplified response methods for one-way eccentric multistorey buildings must retain torsional coupling without requiring a full nonlinear history analysis for every design iteration.
Key Innovation: The centre-of-elasticity three-modal-component method estimates response using modal properties referenced to floor elasticity centres and is checked against a modified uncoupled modal response-history analysis under unidirectional excitation.
71. Graph Representations for Slope Instability and Landslide Kinematics: From Fracture Networks to Physics-Informed Graph Learning
Core Problem: Slope instability develops through connected processes: discontinuities intersect, blocks transfer load, water follows preferential pathways, and deformation propagates between parts of a slope.
Key Innovation: The article first introduces nodes, edges, adjacency matrices, graph Laplacians, message passing, and graph convolution in accessible language. Priority needs include benchmark datasets, uncertaintyaware graph construction, physically meaningful edge design, spatial and temporal holdout testing, cross-site validation, and transparent comparison with non-graph baselines.
72. SLOPE DEFORMATION DATA CALIBRATION BASED ON LINEAR REGRESSION AND SEGMENTED IDENTIFICATION ALGORITHMS
Core Problem: Fiber-optic slope-displacement records can contain systematic sensor bias, and warning systems also need objective boundaries between slow, accelerating and rapid deformation.
Key Innovation: Linear calibration against vibrating-wire references reaches R² 0.999, after which smoothing, outlier control, Welch tests and segmented fits locate two transitions with stage velocities of 0.1753, 0.6115 and 4.1211 mm/h.
73. Analysis of Slope Stability Against River Openings Due to the Influence of Sedimentation at the Tinggar Buntut Water Gate
Core Problem: However, studies concerning the effect of sediment accumulation on sheet pile-supported slopes at irrigation structures remain limited.
Key Innovation: The study evaluates the influence of sedimentation on slope stability at the Tinggar Buntut Water Gate, Kali Sadar River, using the Rankine method and the finite element method. Soil parameters were obtained from BBWS Brantas, while the sediment thickness of 1.5 m was estimated and validated by comparing riverbed elevations derived from Google Earth Pro historical imagery between 2019 and 2025.
74. KEY PERFORMANCE INDICATOR-BASED EVALUATION OF NATURE-BASED SOLUTIONS FOR REDUCING LOCAL FLOOD RISK IN RESIDENTIAL DRAINAGE SYSTEMS USING EPA SWMM: A CASE STUDY OF GRAND PANORAMA RESIDENCE
Core Problem: A steep 2.13 ha residential catchment concentrates runoff on lower roads, requiring evidence on how low-impact drainage and storage perform separately and together.
Key Innovation: EPA SWMM scenarios reduce runoff volume by 42.58% with combined low-impact measures and attenuate peak discharge by 72.73% when storage is added; the best subcatchment combination reduces runoff by 75.33%.
75. The Role of Facebook in Crisis Information Dissemination: A Case Study of Dara-e-Noor District during the 2025 Eastern Afghanistan Earthquake
Core Problem: Social media platforms are often used in natural disasters, but there is a significant academic gap regarding their systematic impact on local crisis management in the region.
Key Innovation: A quantitative survey design was employed, utilizing a non-probability sampling strategy (purposive and snowball techniques) to select 231 eyewitnesses and active Facebook users, with the sample size determined by Cochran’s formula for unknown populations, using a structured online questionnaire. The results indicate that the Facebook information dissemination model can explain 65.9% of the variance of overall crisis communication effectiveness.
76. Comparative Analysis of Earthquake-Induced Hydrodynamic Pressures in Dam–Reservoir Systems Using CFD and Analytical Methods
Core Problem: Hydrodynamic pressures induced by earthquake excitation constitute a critical load component in the seismic safety assessment of dam–reservoir systems.
Key Innovation: A three-dimensional reservoir model with dimensions of 3 m × 3 m × 4 m was established in ANSYS Fluent, and an initial water depth of 2.5 m was defined. The numerical results indicated that the location and magnitude of peak hydrodynamic pressures varied with time, whereas the classical analytical methods generally predicted the maximum pressure at the dam base.
77. Identifying Leading Hazards in Riau Islands: A Monthly Markov Chain Analysis of Disaster Dominance Patterns
Core Problem: Monthly disaster records in the Riau Islands do not reveal which hazard states persist or which transitions may signal delayed cascades.
Key Innovation: A five-state Markov chain fitted to 2019-2024 records assigns wildfire a 40.6% steady-state probability and 63% monthly persistence, while estimating a 44.5% transition from flood months to wildfire months.
78. How Important Are the Critical Points in Selecting the Optimal Samples for Accurate Estimation of Subsurface Soil Moisture?
Core Problem: Subsurface soil-moisture models normally train on large fixed samples even though only a small, climate-dependent subset may carry most of the predictive information.
Key Innovation: Six physics-aware sampling strategies train a lagged Fourier neural operator at eight stations; uncertainty sampling matches full-data skill with 10% of training data in subarctic sites, while distribution sampling is strongest in arid sites.
79. Effect of scour and seabed slope on the cyclic performance of finned monopile foundations
Core Problem: Offshore wind turbine monopile foundations are increasingly challenged by complex seabed environments involving scour, slope inclination, and sustained one-way cyclic lateral loading.
Key Innovation: These findings establish structural fin modification as an effective strategy for enhancing cyclic resilience of offshore wind turbine foundations in scour-prone and sloping seabed environments. All experiments and numerical analyses were conducted using dry sand under effective stress conditions; direct generalisation to saturated offshore seabed conditions requires further validation.
80. STF-GatedCropNet: Spatiotemporal-Frequency Fusion Network With Spatially Adaptive Gated Fusion for Multimodal Remote Sensing Time Series Crop Classification
Core Problem: Multimodal remote sensing time series hold significant potential for agricultural monitoring; however, real-world observations are frequently constrained by cloud contamination, irregular sampling, spectral/structural similarities between crops, and intraparcel spatial heterogeneity.
Key Innovation: To address these challenges, we propose the spatiotemporal-frequency gated fusion network. The model incorporates three core innovations: first, a frequency domain processing module combined with a frequency attention mechanism explicitly extracts multiscale frequency-domain priors from optical time series, modulating temporal bottleneck features to achieve dual-domain synergy.
81. Iterative Least-Squares Collocation Bathymetry Inversion With Optimized Gravity Covariance
Core Problem: Bathymetry inversion using least-squares collocation (LSC) from satellite altimetry gravity data often relies on empirical covariance parameters or prior topography information, which is physically inconsistent with actual inversion conditions.
Key Innovation: This article presents an iterative LSC method for bathymetry estimation that strictly uses only observed gravity anomalies to construct the covariance model. The topography covariance is derived via covariance propagation, and its conversion coefficients from gravity covariance are iteratively optimized for improved consistency and numerical stability.
82. Sea-Surface Current Vector Retrieval Using Squinted Multiaperture ATI-SAR (Sq-MA-ATI)
Core Problem: Ocean surface-current vector retrieval using conventional along-track interferometric synthetic aperture radar remains challenging because, under broadside viewing, the line-of-sight velocity has little along-track projection and a 2-D current vector cannot be determined from a single radial-velocity measurement.
Key Innovation: This article presents a squinted multiaperture along-track interferometry framework (Sq-MA-ATI) that introduces a physical azimuth squint and partitions two-channel echoes into multiple azimuth subapertures to generate several interferometric observations with distinct line-of-sight orientations over the same area. The beam-center HLOS velocity predicted from the retrieved vector agrees with the conventional along-track interferometry estimate about 1%.
83. A continental benchmark dataset for evaluating ecosystem gradient-flux approaches across 47 NEON flux towers
Core Problem: Benchmark datasets for evaluating profile-based ecosystem flux methods across environmental gradients are currently lacking.
Key Innovation: Here, we present a benchmark dataset derived from 47 terrestrial towers in the National Ecological Observatory Network (NEON), integrating co-located EC, concentration profiles, tower geometry, and canopy structural metrics. We evaluate how canopy structure, sensor height configuration, and data filtering influence agreement with EC across ecosystems, with canopy heights ranging from 0.15 to 53 m.
84. Mapping complex cropping patterns in China (2018–2021) at 10 m resolution: a data-driven framework based on multi-product integration and Google satellite embedding
Core Problem: Mapping complex cropping patterns and temporal dynamics is of great significance for addressing the challenges faced by agricultural systems.
Key Innovation: In this study, we developed a data-driven crop mapping framework by integrating multiple existing crop products with the Google Satellite Embeddings derived from the AlphaEarth foundation model, and produced 10 m resolution mapping of complex cropping patterns across China from 2018 to 2021.
85. Satellite observations reveal underestimation of CO 2 emissions in Africa and the Middle East
Core Problem: Africa and the Middle East contribute significantly to global fossil fuel production, yet the associated carbon emissions from these extraction and processing activities remain insufficiently reported.
Key Innovation: Here, we introduce an alternative approach to indirectly infer and map anthropogenic carbon dioxide (CO2 ) emissions using the coemitted nitrogen oxides (NOx ) emissions derived from TROPOMI satellite observations. The analysis reveals that these underestimated emissions could lead to a delay of ∼5 years in achieving the 2030 Nationally Determined Contribution targets in Africa and the Middle East.
86. GCIE-Net: A Global and Channel Information-Enhanced Network for Ship Instance Segmentation in SAR Images
Core Problem: Synthetic aperture radar (SAR) ship instance segmentation is a sophisticated pixel-level analytical task that presents unique and persistent challenges in remote sensing image interpretation.
Key Innovation: To this end, we propose a global and channel information-enhanced network (GCIE-Net), which introduces a segmentation branch into the high-performance object detection model DEIM, achieving the generation of high-quality SAR ship instance-level masks.
87. Mapping Submarine Sand Wave Bathymetry from Sentinel-2 Texture Using a Spatial-Sequential Deep Learning Model
Core Problem: Their complex morphology and potential mobility create challenges for engineering surveys, navigation safety, and seabed stability assessment.
Key Innovation: Here, we propose a spatial-sequential 2DCNN–LSTM model for retrieving submarine sand wave bathymetry from Sentinel-2 surface reflectance imagery. Evaluation on the large extrapolated area against in situ bathymetric data achieved a root mean square error (RMSE) of 3.78 m, a mean absolute error (MAE) of 2.99 m, and a mean relative error (MRE) of 9.1%.
88. Calibration Optimization for Long-Term Consistency of the FY-3B Infrared Atmospheric Sounder
Core Problem: The Feng Yun-3B (FY-3B) Infrared Atmospheric Sounder (IRAS) provides key infrared observations for numerical weather prediction (NWP) and climate applications, but long-term consistency is affected by three factors: spectral response function (SRF) central wavenumber shifts, changes in the nonlinear coefficient of the instrument in orbit, and fixed brightness-temperature (BT) uniformity screening.
Key Innovation: The authors propose a three-step refinement chain consisting of SRF central wavenumber shift correction (SSC), in-orbit nonlinearity-coefficient optimization (NCO), and channel-dependent adaptive quality control (AQC).
89. A Sentinel-1 Dual-Polarimetric Scattering-Regime Framework with AMSR2 Consistency Assessment for Interannual Sea Ice Characterization in the Southern Sea of Okhotsk
Core Problem: Dual-polarimetric H–α scattering-regime analysis offers an interpretable approach for sea ice characterization when independent ice-type reference data are limited.
Key Innovation: The study presents a Sentinel-1 IW-mode dual-polarimetric framework for interannual sea ice characterization in the Southern Sea of Okhotsk. The large RMSE values indicate weak absolute agreement, particularly at the footprint scale.
90. Deep Learning Applications in Remote Sensing for Forest Inventory Methods
Core Problem: Deep-learning forest inventories report high local accuracy, but inconsistent reference data and limited external validation obscure transfer across forest structures, sensors and regions.
Key Innovation: A review of 122 studies separates counting, species identification and measurement, finding that only 42 used independent validation and that LiDAR, multimodal fusion and standardized reference datasets are central to more transferable inventories.
91. Influence of Lens Distortion Correction and Image Resampling on Vehicle Detection in UAV Imagery
Core Problem: Unmanned Aerial Vehicles (UAVs) provide high-resolution imagery for detailed object detection, but wide-angle lenses can introduce radial and tangential distortions that affect object geometry and detection performance.
Key Innovation: The study evaluated the effects of lens distortion correction and image preprocessing on UAV-based vehicle detection. At the final checkpoint, Cascade R-CNN achieved AP and AR values of 0.644 and 0.724, respectively, compared with 0.619 and 0.706 for Faster R-CNN.
92. Flexible High-Resolution Water Quality Monitoring and Mapping Using an Autonomous Surface Vehicle and Drone-Based Multispectral Imaging System
Core Problem: Traditional in situ sampling provides accurate point measurements but lacks spatial coverage, while satellite remote sensing is often limited by coarse spatial resolution and cloud cover.
Key Innovation: To address these limitations, this study developed and validated an integrated monitoring platform combining an Autonomous Surface Vehicle (ASV) and a drone-based multispectral imaging system for flexible, high-resolution water quality monitoring.
93. A Ground-Based Multi-Doppler Wind Retrieval Algorithm for Turbulent Convection: An LES-Based Radar Wind Retrieval Framework
Core Problem: Accurate retrieval of high-resolution three-dimensional (3D) wind fields from Doppler radar observations is essential for understanding the dynamical structure of convective storms and the turbulent processes that govern their evolution.
Key Innovation: The study develops and evaluates a ground-based dual and multi-Doppler radar wind retrieval framework designed to reconstruct turbulent wind structures within severe convective environments, including a squall-line event and a hurricane, using an advanced phased-array radar configuration at our facility. Vertical velocity remains more difficult to retrieve, particularly at low levels, although the idealized hurricane experiment shows improved skill aloft, with correlations reaching ~0.8.
94. Testing a Novel Transfer Learning Approach to Estimate War-Related Crop Yield Losses in Ukraine
Core Problem: Accurate yield estimation helps policymakers to plan compensation, yet modelling yields in conflict regions remains challenging due to significant non-meteorological disruptions.
Key Innovation: The study proposes a novel framework to quantify war-related crop yield losses by comparing estimations derived from meteorological data, representing weather-driven yield variability, with those based on Earth observation (EO) data, reflecting actual crop conditions influenced by both weather and conflict.
95. Maize Yield Prediction via Data Fusion of UAV Multi/Hyperspectral Imagery and In-Field Measurements
Core Problem: Maize-yield models must determine when UAV spectra add value beyond field measurements and whether apparent accuracy survives treatment-held-out validation.
Key Innovation: Multispectral, hyperspectral, biomass, LAI and SPAD data are fused across two growth stages; early-stage fusion reaches R² 0.82, late red-edge plus LAI reaches 0.86, and treatment-held-out tests expose optimism in random validation.
96. A Decade of Remote Sensing for Vegetation Monitoring with Sentinel-2
Core Problem: A decade of Sentinel-2 vegetation studies lacks a consolidated account of where methods transfer, where validation remains geographically uneven and how uncertainty is handled.
Key Innovation: A systematic review retains 1,097 studies from 1,700 records and traces a shift from vegetation indices toward machine learning, hybrid radiative-transfer models and sensor fusion, while finding no universally superior method across ecosystems.
97. CMGFDet: Cross-Modal Gated Fusion Network with Multi-Receptive Field Aggregation for RGB–Infrared Aerial Object Detection
Core Problem: However, existing methods still struggle with effective cross-modal feature fusion, spatial misalignment between modalities, and scale variation of objects in aerial views.
Key Innovation: In this paper, we propose CMGFDet, a Cross-Modal Gated Fusion Network with Multi-Receptive Field Aggregation designed for RGB–infrared aerial object detection. Extensive experiments on four public benchmarks (DroneVehicle, RGBTDronePerson, VEDAI, and VTUAV) show that CMGFDet improves the previous best mAP@0.5 by 1.6%, 2.2%, 1.9%, and 2.2%, respectively.
98. Fugitive Methane Monitoring: A Systems Review of Physics, Technology, Economics, and Regulation
Core Problem: Fugitive methane emissions from fossil fuel operations exceed 120 million tonnes per year globally, yet measurement campaigns consistently find actual emissions to be roughly twice the levels reported through inventory-based self-reporting, and up to three times higher in the most intensively studied producing regions of the United States.
Key Innovation: The authors establish that three compounding problems—the predominantly intermittent character of fugitive emissions (detected in fewer than 25% of repeat airborne passes at major Permian Basin well sites), the systematic underreporting gap, and the structural inadequacy of periodic inspection programs—together define a monitoring requirement that no single current technology satisfies.
99. Promoting inclusive institutional culture through intergenerational collaboration in disaster risk reduction and disaster risk management
Core Problem: Disasters are becoming increasingly complex; disaster prevention, mitigation, response, and recovery must continue to transform to meet that complexity.
Key Innovation: The authors used reflexive thematic analysis to analyze the interviews and found that institutional and age-based discrimination are central barriers to effective youth participation.
100. Mapping stories through multiple disasters: Secondary qualitative analysis of a cartographic storytelling resource
Core Problem: However, there has been limited research into how experiences from one disaster may shape an individual's future behaviors and expectations, or the ways in which knowledge sharing may be occurring between communities and government agencies from one disaster to the next.
Key Innovation: The study utilises a new cartographic storytelling resource, the HowWeSurvive (HWS) Map, as a source of large-scale secondary qualitative dataset to assess knowledge sharing practices in Australia among individuals who report multiple disaster experiences across their lifetime. These findings demonstrate that community disaster knowledge is relational and cumulative, evolving through repeated events.
101. The sky is falling! Examining disaster preparedness in supported independent living: Support worker's perspectives
Core Problem: Support workers in independent-living settings must prepare with and for clients with diverse disabilities, yet their operational and psychological burden is rarely represented in preparedness guidance.
Key Innovation: A mixed-methods study of 137 Australian support workers identifies three linked constraints: clients' mental-health and resilience needs, loss of control during disasters, and the cognitive load of maintaining supplies, medication and support.
102. Exploring strategies for enhancing frontline civil servants’ psychological preparedness for disasters and their work in Taiwan
Core Problem: Although studies have emphasized the psychological well-being of frontline emergency responders, research on strategies to strengthen their psychological preparedness for disasters and work-related duties remains limited.
Key Innovation: The study first conducted focus groups and in-depth interviews with 18 Taiwanese academic and practical experts, extracted 19 strategies across three categories: operation, training, and support. The findings revealed that gaining support from the local government leaders was considered the most crucial strategy, while frontline civil servants’ familiarity with disaster management regulations and legal frameworks was deemed the most feasible.
103. Deep unrolling for unified pansharpening with satellite-specific and image-adaptive priors
Core Problem: Satellite-specific pansharpening models do not scale across sensors, while naive mixed-sensor training entangles incompatible spectral and spatial priors.
Key Innovation: UP-PAN unrolls a physical observation model and separates a discrete satellite-specific codebook from an image-adaptive prompted prior, matching or exceeding dedicated models across four satellite datasets with one jointly trained network.
104. A partially supervised joint segmentation method for farmland and rural roads from single-class annotated remote sensing datasets
Core Problem: Although multi-class datasets are increasingly available, many high-quality annotations remain task-specific, where only a single target class is labeled and other co-occurring objects are treated as background.
Key Innovation: To address these challenges, we propose RSFRNet, a partially supervised joint segmentation framework for farmland and rural road extraction from mutually exclusive single-class annotations. Experiments on FarmSeg-VL and WHU-RuR + show that RSFRNet achieves the best evaluated performance, reaching 74.04 % mIoU with balanced farmland and road IoU.
105. Spatially varying NDVI–stand structure relationships in Scots pine forests across Poland using HLS, LiDAR metrics and GGP-GAM
Core Problem: This challenge is particularly important across broad ecological gradients, where relationships between optical metrics and stand attributes may be spatially non-stationary.
Key Innovation: Here, we analysed NDVI-stand structure relationships in Scots pine-dominated forests across the whole of Poland using 30 m HLSS30 imagery, LiDAR-derived stand metrics, and a Geographic Gaussian Process Generalised Additive Model (GGP-GAM).
106. Enabling maize mapping in double-cropping regions of Argentina and Brazil without ground reference data by leveraging multiple satellite platforms
Core Problem: Large-scale maize mapping is challenging due to the lack of field-level datasets, which were essential for training machine learning models.
Key Innovation: The authors first pre-trained a maize classifier on US cornfields using Global Ecosystem Dynamics Investigation (GEDI) Relative Height (RH) metrics and Sentinel-2 imagery, then transferred this model to Argentina and Brazil to produce candidate maize labels at peak growth. Finally, we validated the maps with Google Street View (GSV) imagery and benchmark maps.
107. Coastal Flood Prediction Using Machine Learning
Core Problem: Coastal flooding is one of the most severe natural hazards, causing significant damage to human life, infrastructure, and ecosystems in coastal regions.
Key Innovation: This research presents a Coastal Flood Prediction System based on Machine Learning techniques to improve the accuracy and efficiency of flood forecasting.
108. THE EFFECTIVENESS OF SHORT FILMS IN IMPROVING ADOLESCENTS’ KNOWLEDGE OF EARTHQUAKE DISASTER PREPAREDNESS
Core Problem: Adolescents are a vulnerable group at risk of being affected by disasters due to their lack of preparedness.
Key Innovation: This quasi-experimental research design involved 82 junior high school students (treatment group = 41; control group = 41) from SMP Muhammadiyah 14 Paciran, Lamongan Regency, who were selected using a simple random sampling. The results showed that 85.4% of students in the treatment group had good knowledge after short film education, compared to 63.4% in the control group after the lecture method.
109. A Dual-LiDAR ship-shore collaborative pose estimation method for USV berthing and unberthing
Core Problem: USV berthing creates occlusions and shoreline blind zones that allow shipborne localization drift to accumulate near fixed infrastructure.
Key Innovation: Shipborne and shore-based LiDAR are joined through place recognition, relative-pose estimation and factor-graph optimization, achieving 0.42 m absolute trajectory error at 14.2 frames per second in simulated berthing tests.
110. A lightweight single-head OBB network for SAR ship detection via semantic-guided multi-scale feature fusion
Core Problem: Nevertheless, the performance of existing approaches is often limited by inadequate feature extraction capability, insufficient exploitation of multi-scale information, and the computational burden introduced by conventional multi-head detection schemes.
Key Innovation: To overcome these limitations, a lightweight oriented bounding box (OBB) detection framework, termed SGMFF-Net, is developed by combining semantic-guided multi-scale feature fusion with a single high-resolution detection head. Furthermore, a single high-resolution OBB detection head is utilized to decrease computational overhead and parameter redundancy while preserving accurate localization capability for arbitrarily oriented ship targets.
111. CPDF-Net: A Cascaded Pyramid Decomposition-Fusion Network for SAR Ship Detection
Core Problem: Identifying ships in synthetic aperture radar images faces several challenges, including coherent speckle noise, intricate sea-land background interference, and substantial fluctuations in the size of ship targets.
Key Innovation: To mitigate these issues, this article proposes an enhanced ship edge information and multiscale perception network named the cascaded pyramid decomposition-fusion network. It employs grouped convolution and progressive feature reorganization to achieve fine-grained fusion of multiscale contextual information, thus improving the detail representation of small and blurred targets.
112. Beyond Nighttime Lights: Multisensor Satellite Fusion for Province-Constrained District-Level GDP Disaggregation in Data-Scarce Regions
Core Problem: Yet official statistics are often unavailable at fine administrative scales, leaving local dynamics poorly captured.
Key Innovation: The study develops a transparent, scalable framework for province-constrained small-area GDP disaggregation in data-scarce settings, using Türkiye as a case study.
113. A Hierarchical Geometry-Driven Framework for Instance Segmentation Within Junction Regions in Steel Grid Structure Point Clouds
Core Problem: Steel-grid junction point clouds are difficult to split into member instances when geometry overlaps, sampling is incomplete and no design drawing or BIM prior is available.
Key Innovation: HSC-DCR maps three-dimensional directions into spherical coordinates, corrects topology and refines dual-sphere centres; 521 stadium instances yield F1 0.948 and 85.5% mIoU without design-model priors.
114. Healthcare waste management for preparedness: shifting stakeholder salience in the disaster cycle
Core Problem: Healthcare-waste standards assume fixed stakeholder roles, although disasters can disable formal systems and shift operational responsibility to local actors.
Key Innovation: A stakeholder-salience analysis across the disaster cycle shows how adaptability and local knowledge compensate when standardized waste systems fail, making healthcare waste an explicit preparedness function.
115. BQEL-Net: A bimodal quantitative ensemble learning network with uncertainty-aware for vectorized extraction of martian impact craters from bimodal catalogic-and-quantitative dataset (BCQDS)
Core Problem: Current research on bimodal extraction of Martian impact craters, which integrates satellite imagery and digital elevation models (DEMs), remains limited to the object level, with no existing reports about addressing both pixel-level and irregularly vectorized extraction.
Key Innovation: To address these gaps, this study is the first to create and release a bimodal catalogic and quantitative dataset (BCQDS), combining DEM and satellite imagery with topographic landscape pattern indices. Experiments conducted using MOLA data within the BCQDS demonstrated that BQEL-Net achieves a maximum MIoU of 0.858.