TerraMosaic Daily Digest: August 3, 2026
Daily Summary
Mountain-hazard studies resolve how long-term preconditioning and short-lived triggers combine. A 62-year reconstruction of the Sedongpu Gully links glacier and snow retreat to repeated rock-ice avalanches and river blockage, while field-constrained InSAR in the Gulf of Corinth separates gravitational creep from tectonic deformation. At the fatal Taliye landslide, electrical resistivity tomography, stability analysis and runout modelling trace failure through weathered basalt, elevated pore-water pressure and steep relief.
Flood and coastal analyses show that hazard estimates depend on representation and nonstationarity as well as forcing magnitude. A 500-year Yangtze reconstruction attributes flood-regime shifts to coupled tropical and extratropical circulation and finds that embankments and lake reclamation amplify the climate-driven 100-year flood magnitude by about 18% and 8%, respectively. The 1950-2024 global storm-surge archive narrows return-level uncertainty, whereas Hurricane Ian experiments show that wind-field structure alone can bias peak surge by up to 70%; hourly mobility data further reveal 2.6-fold underestimation of urban flood exposure by static census counts.
Mechanism and transfer are also becoming explicit tests in volcanic, seismic and geospatial-AI research. Tests on 1,001 strong-motion records show that an 11-parameter stochastic ground-motion model can match more elaborate alternatives; a layered Timoshenko-beam neural graph retrieves stable shear-wave velocities across nine earthquakes in a 54-storey building, while Bayesian path and site kernels reduce artificial inflation of near-source portfolio-loss tails. Holocene stratigraphy and submerged observations recast Campi Flegrei as a long-lived resurgent dome, and very-long-period pulses at Piton de la Fournaise resolve intermittent magma transfer during lateral dike propagation. Validated Cascadia simulations, global tsunami ray tracing, and the GEOID-Flood and Obshazard benchmarks extend evaluation from isolated events to reproducible, multimodal stress tests; the foundation-model results remain early evidence from preprints rather than operational validation.
Key Trends
Five methodological shifts connect slope-process diagnosis, nonstationary risk and transferable geospatial models.
- Slope State Is Inferred from Complementary Evidence: InSAR time series, field geomorphology, electrical resistivity and process modelling are combined to distinguish gravitational motion, subsurface weakness and likely runout.
- Static Susceptibility Is Giving Way to Process Constraints: Road-cut geometry, deformation histories, pore pressure and hydro-fluctuation are represented directly, while land cover is treated cautiously when it acts as an exposure proxy rather than a causal slope or flood control.
- Nonstationarity Extends from Forcing to Exposure: Multicentury climate variability, engineering interventions, longer surge records and hourly population mobility all change the hazard or the population exposed to it.
- Physics and Operational Tests Constrain Machine Learning: Differentiable hydrology, physics-aware SAR, physics-consistent structural identification, parsimonious ground-motion models, spatial-correlation kernels and synthetic early-warning experiments are judged by transfer, physical consistency or downstream hazard performance rather than accuracy alone.
- Benchmarks Are Becoming Multimodal and Event-Complete: Flood and disaster-intelligence benchmarks combine pre- and post-event radar, optical, elevation, sounding and ground observations across regions, exposing the limits of nominally general models.
Selected Papers
Three contributions set the issue's scientific range: a six-decade reconstruction of Himalayan rock-ice avalanches, a five-century attribution of Yangtze flood variability, and a 75-year global storm-surge archive. Landslide and rockfall studies then combine InSAR, field geomorphology, electrical resistivity and engineering geometry to move from static susceptibility toward observable slope state and failure process. Bayesian spatial-correlation and stochastic ground-motion studies sharpen regional earthquake-risk modelling, while the remaining papers extend the same emphasis on mechanism and testability across volcanic unrest, tsunami hazards, flood exposure, differentiable models and multimodal Earth-observation benchmarks.
1. Climate warming preconditions Himalayan slopes for post-earthquake cascading hazards
Core Problem: The relative roles of climatic preconditioning and earthquake disturbance in repeated Himalayan rock-ice avalanches and river blockage remain difficult to separate from short event records.
Key Innovation: A multi-source reconstruction identifies at least 24 cascading events from 1961 to 2023, links progressive glacier and snow retreat to changing slope boundary conditions, and tests whether future avalanches could again dam the Yarlung Zangbo River.
2. Engineering and land use intensified climate-driven Yangtze River flood hazards since the Little Ice Age
Core Problem: Multicentury flood records rarely resolve how atmospheric circulation and river engineering jointly alter extreme-flood magnitude in a major basin.
Key Innovation: Lake sediments, historical documents and climate reanalysis reconstruct 500 years of Yangtze flooding; a nonstationary model attributes regime shifts to tropical-extratropical circulation and estimates amplification of the climate-driven 100-year flood by embankments and lake reclamation.
3. Global dataset of storm surges and extreme sea levels for 1950–2024 based on the ERA5 climate reanalysis
Core Problem: Global extreme-sea-level estimates have relied on records too short to constrain uncommon return periods consistently across coastlines.
Key Innovation: A global hydrodynamic reconstruction extends hourly tides and storm surges to 1950-2024 and provides revised extreme-level statistics with smaller return-period uncertainty than the preceding 40-year product.
4. Deep-seated landslides in tectonically active areas: insights from InSAR data and field evidence in the western Gulf of Corinth (Greece)
Core Problem: In tectonically active terrain, satellite-measured deformation can reflect fault motion or regional uplift rather than gravitational slope failure.
Key Innovation: Sentinel-1 time-series clustering is constrained by European Ground Motion Service data and field geomorphology to identify five deep-seated landslide sectors and resolve site-specific, delayed responses to rainfall and seismic forcing.
5. Analysis of landslide process in parts of basaltic terrain in Western Ghats, India using remote sensing, electrical resistivity tomography and flow modeling
Core Problem: Surface mapping alone cannot identify the subsurface weathering and pore-pressure conditions that controlled the fatal 2021 Taliye landslide in Deccan basalt.
Key Innovation: Field observations and remote sensing are integrated with electrical resistivity tomography, slope-stability analysis and runout modelling, identifying thick overburden, highly weathered basalt, elevated pore pressure and steep slopes as the principal controls.
6. When Land Use/Land Cover Misleads: Limitations in Data-Driven Flood and Landslide Susceptibility Assessment
Core Problem: Land-use and land-cover layers are routinely treated as causal conditioning factors in susceptibility models even when their dates or class definitions do not match the hazard inventory.
Key Innovation: Eighteen flood and landslide models across two urban land-cover products show that terrain dominates predictive skill and that land cover can introduce temporal and spatial bias; the study argues for interpreting it primarily as an exposure or vulnerability proxy in this setting.
7. A Road-Segment-Based Rockfall Susceptibility Mapping Approach Integrating Physically Informed Slope-Cutting Features and Comparative Machine Learning Models
Core Problem: County-scale rasters smooth the sharp topographic changes created by road cuts, while random validation can overstate rockfall-model transfer along mountain corridors.
Key Innovation: Object-based road evaluation units incorporate theoretical slope-cutting height and are tested with 13 models under leave-one-road-corridor-out validation, converting susceptibility thresholds into intervention mileage for maintenance planning.
8. Reappraisal of Holocene Caldera Resurgence at Campi Flegrei (Southern Italy): A Long‐Lived Magma‐Driven Resurgent Dome System
Core Problem: Incomplete reconstructions of permanent caldera deformation can cause long-lived resurgence to be mistaken for short-term precursory uplift.
Key Innovation: Marine stratigraphy and submerged observations reconstruct approximately 190 m of permanent Holocene uplift at Campi Flegrei and support incremental growth of a 9-km-wide dome above stacked magma sills at 3-4 km depth.
9. Very‐Long‐Period Seismic Signals During Lateral Dike Propagation at Piton de La Fournaise Volcano
Core Problem: Shallow magma transfer during lateral dike propagation is difficult to observe when supply appears continuous at broader temporal scales.
Key Innovation: A quasi-periodic sequence of very-long-period signals locates a repeatedly activated tensile crack at the transition from vertical to lateral intrusion, consistent with threshold-controlled, valve-like magma transfer before the 2022 eruption.
10. Hurricane wind field representation shapes storm surge and building-scale flood hazard estimates
Core Problem: Coastal-flood estimates often treat hurricane wind products as interchangeable, obscuring how wind-field intensity and geometry propagate into surge and building-scale hazard.
Key Innovation: Hurricane Ian experiments compare parametric, reanalysis and hybrid winds, showing that radius-of-maximum-wind errors can dominate equivalent intensity errors and bias peak surge by up to 70%.
11. Basin-scale geometric focusing: a probabilistic-geometric framework for global tsunami exposure assessment and the 2025 Kamchatka Peninsula tsunami
Core Problem: Global tsunami screening requires a computationally efficient way to identify bathymetry-driven far-field focusing without replacing high-fidelity inundation models.
Key Innovation: Probabilistic earthquake weights are coupled to 9,000 ray trajectories, and the resulting focusing patterns are checked against FUNWAVE-TVD simulations and DART observations for the 2025 Kamchatka and 2011 Tohoku events.
12. Geomorphic impacts of recurrent glacier-dammed lake outburst floods in Valle Huemules, Northern Patagonian Icefield
Core Problem: The cumulative geomorphic work of recurrent glacier-dammed lake outburst floods is poorly constrained where observations capture only individual events.
Key Innovation: Historical imagery, UAV topography and hydraulic reconstruction document repeated outbursts in Valle Huemules and quantify how successive floods reworked the proglacial valley rather than treating each event in isolation.
13. Directional impact mechanisms of dry granular flows and two-phase debris flows on a square pier
Core Problem: Design forces on bridge piers depend on flow composition and impact direction, yet dry granular and water-sediment surges are often represented by one idealized loading case.
Key Innovation: Coupled VOF-DEM simulations compare dry and two-phase impacts on a square pier and isolate how Froude number, solid fraction and approach direction control peak force and force-history shape.
14. A risk-based approach to the quick clay landslide at Flatanger, Norway – comparison of national regulatory guidelines
Core Problem: Conventional factor-of-safety requirements can demand disproportionate stabilization after a quick-clay failure when reopening critical roads under residual uncertainty.
Key Innovation: The 2023 Flatanger case is evaluated under Norwegian, Swedish and Finnish guidance, showing how trigger-focused mitigation and risk-based interpretation can maintain public safety while avoiding unnecessarily extensive works.
15. A Comparison Study on the Dynamic Response of Rectangular and Circular Pile–Slab Retaining Walls Against Rockfall Impact
Core Problem: The influence of retaining-wall pile geometry on force transfer and concrete damage under rockfall impact is not sufficiently quantified for protective design.
Key Innovation: Validated LS-DYNA simulations compare circular and rectangular pile-slab walls across impact velocities and find lower deformation and damage for the rectangular configuration in the tested cases.
16. Validation framework for semi-stochastic simulations in Cascadia earthquake early warning
Core Problem: Cascadia earthquake early-warning algorithms lack recorded large events for testing, but synthetic waveforms are useful only if their operationally relevant characteristics are validated.
Key Innovation: A suite of 112 M6.6-9.4 rupture scenarios at 191 sites is checked for detectability, magnitude estimation and ground-motion intensity, then used in example tests of ShakeAlert EPIC and Ocean Networks Canada algorithms.
17. Complex Versus Parsimonious Site‐Based Stochastic Ground Motion Models: Which One Is Better?
Core Problem: Site-based stochastic ground-motion models range from compact parameterizations to complex nonstationary formulations, but added complexity is useful only if it improves the statistics and structural response represented by synthetic motions.
Key Innovation: A controlled comparison against 1,001 strong-motion records finds that an 11-parameter modulated filtered white-noise model reproduces the tested motion characteristics and response spectra, with little gain from a more elaborate R-vine dependence structure over a Gaussian copula.
18. Cross‐Period Ground Motion Spatial Correlation With Path and Site Effects: Bayesian Inference and Risk Implications
Core Problem: Regional seismic-loss estimates require cross-period spatial correlation, yet isotropic models confound source-to-site path geometry with local site effects and can distort the aggregation of near-source extremes.
Key Innovation: Principal-component residuals, path and site kernels, and Bayesian posterior inference improve predictive density over isotropic baselines; synthetic portfolios show that the formulation reduces artificial inflation of near-source loss-exceedance tails.
19. Recovery of low-magnitude seismic events before the July 29, 2025, Kamchatka megathrust earthquake using waveform cross-correlation enhanced by the addition of stochastic noise
Core Problem: The July 29, 2025, Kamchatka megathrust earthquake is one of the largest events in the 21st century.
Key Innovation: Waveform cross-correlation with controlled stochastic-noise augmentation recovers low-magnitude events before the Kamchatka megathrust earthquake.
20. Multi-Source Dynamic Graph Learning for Compound-Flood Forecasting in Managed Coastal Systems
Core Problem: Compound flooding in managed coastal systems is influenced by hydrological conditions and water-management activity observed across multiple monitoring stations.
Key Innovation: Anchored dynamic graphs fuse hydrometeorological and operational stations without sacrificing local high-water forecasts.
21. GeoCore-9B: Towards Geo-Aware Generative Foundation Models in Earth Observation
Core Problem: Existing generative models for earth observation (EO) predominantly rely on fine-tuning natural image priors, which limits their scalability and introduces perspective biases that conflict with geospatial constraints.
Key Innovation: GeoCore-9B is trained from scratch on Earth-observation image-text data and evaluated across generative and transfer tasks.
22. Assessing the Benefits of Combining Advanced Deep Learning Techniques for Post-Disaster Building Damage Assessment from UAV Imagery
Core Problem: Rapid and accurate post-disaster building damage assessment is essential, yet remains a challenging task.
Key Innovation: UAV damage mapping combines precise detectors with vision-language reasoning to address sparse labels and cross-region assessment.
23. GEOID-Flood: A Large-Scale Multi-Modal Benchmark Dataset for Flood Segmentation
Core Problem: Geospatial foundation models aim to learn representations that transfer across regions and sensors, yet evaluating them on specific tasks requires large, high-quality, multi-modal benchmarks that measure how well such models extract value from data.
Key Innovation: GEOID-Flood supplies bi-temporal SAR, optical imagery and elevation for 219 floods across 65 countries.
24. Obshazard-bench: Benchmarking Multimodal Foundation Models for Real-Time Disaster Intelligence from Raw Earth Observation Streams
Core Problem: Multimodal Large Language Models (MLLMs) are increasingly used to interpret Earth observation data, yet their capability to support real-world disaster emergency response remains insufficiently evaluated.
Key Innovation: Obshazard-bench evaluates multimodal models on raw, time-evolving observations from more than 120 hazard events rather than post-processed products.
25. Extending the Joint Probability Method to Compound Flooding: Transition Zone Delineation, Flood Depth Attribution, and Design Event Selection
Core Problem: Quantifying the frequency of compound flood depths is a fundamental challenge in low-gradient coastal watersheds, where flood hazards arise from the nonlinear interaction of storm surge, rainfall, and riverine flooding.
Key Innovation: An extended joint-probability method derives compound flood-depth frequency, driver attribution and design-event selection.
26. A coupled SPH–OpenSees approach for tsunami-induced loads and dynamic response of multi-column bridge bents
Core Problem: Tsunami-induced loading on bridge substructures remains insufficiently characterized in terms of the relationship between transient hydrodynamic demands and nonlinear structural response, particularly for multi-column bents, in a fully three-dimensional setting.
Key Innovation: A coupled SPH-OpenSees model transfers tsunami hydrodynamics into the dynamic response of multi-column bridge bents.
27. Drought propagation and ecosystem resilience in a peri-urban catchment of Berlin-Brandenburg
Core Problem: Climate change intensifies pressure on water resources, making it essential to understand how drought severity affects both surface and groundwater systems and their impact on vegetation.
Key Innovation: Catchment observations resolve how meteorological drought propagates through soils, groundwater and peri-urban ecosystem response.
28. GIS-Based Flood Susceptibility Assessment Using the Analytical Hierarchy Process: A Case Study of the Sebeya Catchment, Rwanda
Core Problem: Flood susceptibility mapping is crucial for understanding flood-prone areas and mitigating the associated risks in vulnerable regions like the Sebeya Catchment.
Key Innovation: A GIS-AHP case study maps flood susceptibility in Rwanda's Sebeya catchment and evaluates the conditioning hierarchy.
29. Stability assessment of tunnel portal using DFN-DEM modeling and photogrammetry: A case study of Tehran-Shomal freeway in Iran
Core Problem: Assessment of tunnel portal stability is a crucial consideration due to the portal's proximity to the ground surface and the non-uniform stress distribution around it.
Key Innovation: SfM, laser scanning, digital joint mapping and stochastic DFN-DEM jointly test portal stability and support performance.
30. Mechanism of 2G-NPR anchor cables for reinforcing anti-dip rock slopes subjected to hydro-fluctuation belt
Core Problem: In order to address these challenges, a similarity model testing was adopted in this study to compare the performance of conventional Poisson's ratio (PR) with microscopic negative Poisson's ratio (2G-NPR) anchor cables in reinforcing anti-dip layered rock slopes.
Key Innovation: Physical modelling shows how negative-Poisson-ratio anchor cables delay cracking and redistribute energy in anti-dip reservoir slopes.
31. A globally scalable, light data framework for flood-hazard mapping using open geospatial services
Core Problem: Flood-risk screening is often limited by the lack of globally consistent hazard layers, because detailed hydraulic models require local calibration, boundary conditions, and substantial computation.
Key Innovation: Open geospatial services, HAND, valley-bottom flatness and monotonic gradient boosting produce transferable screening-grade flood-hazard classes.
32. Beyond static census: Hourly mobile phone data reveals 2.6-fold underestimation of urban flood exposure
Core Problem: Urban flood-exposure estimates commonly treat census population as static even though occupancy changes strongly by hour and neighbourhood.
Key Innovation: Hourly mobile-phone population data reveal that static census exposure can undercount urban flood exposure by a factor of 2.6.
33. Landslide susceptibility mapping in southwest Sweden using an automated deep neural support vector regression model
Core Problem: Landslides are a persistent geohazard in Sweden, especially in areas dominated by soft, clay-rich soils.
Key Innovation: Automated deep neural support-vector regression maps landslide susceptibility in southwest Sweden and identifies clay distribution as the dominant control.
34. Landslide Susceptibility Mapping Using Weight of Evidence Method in Tieng Village and Surrounding Areas, Kejajar District, Wonosobo Regency, Central Java Province, Indonesia
Core Problem: Tieng village and its surrounding areas in Kejajar District, Wonosobo Regency, are identified as regions with a relatively high risk of landslide hazards.
Key Innovation: Weight-of-evidence modelling maps shallow-landslide susceptibility in Central Java and reports both success- and prediction-rate validation.
35. Machine learning-based landslide susceptibility assessment for shallow landslide mitigation in the Gumitir Mountain Area, Indonesia
Core Problem: Recurrent rainfall-induced landslides in Gumitir, East Java, threaten the Jember–Banyuwangi route.
Key Innovation: Random Forest and logistic regression are compared for shallow-landslide mitigation in the Gumitir Mountains.
36. PCA–GPR-Assisted Sequential Bayesian Inversion of Slope Mechanical Parameters from Multi-Stage Deep Horizontal Displacement Monitoring
Core Problem: Reliable mechanical parameters are needed to predict deformation during staged slope excavation, yet deep inclinometer profiles are high-dimensional and repeated numerical inversion is costly.
Key Innovation: Sequential Bayesian inversion combines PCA, Gaussian-process regression and staged displacement monitoring to update slope mechanical parameters.
37. Rising Lake Levels as a Distinct Flood Hazard: An Integrated Risk Assessment Framework for Semi-Arid Inland Lake Basins
Core Problem: Unlike riverine floods, lake inundation develops gradually and persists over extended periods, yet integrated flood risk assessments specifically addressing this phenomenon remain scarce.
Key Innovation: Scenario inundation, spatial exposure and household vulnerability are integrated for persistent lake-level flooding at Lake Baringo.
38. Causal-Discovery-Based Analysis of Time-Lagged Environmental Signals Associated with Land Subsidence
Core Problem: Land subsidence is a complex geophysical phenomenon shaped by delayed, nonlinear interactions among climatic, soil, and hydrological variables.
Key Innovation: A lag-aware causal-discovery audit separates robust environmental associations from unsupported short-term drivers of land subsidence.
39. A Hierarchical Framework for Quantifying Seasonal and Daily Wildland Fire Risk in Great Plains Grasslands
Core Problem: Accurate quantification of wildfire risk is essential for balancing wildfire mitigation and prescribed fire management in grassland ecosystems, yet existing fire danger indices do not explicitly distinguish seasonal fuel dynamics from daily weather variability.
Key Innovation: A hierarchical model separates seasonal fuel readiness from daily fire weather and improves discrimination over conventional fire-danger indices.
40. Combined local knowledge with remote sensing and machine learning for seismic vulnerability assessment in rural areas: A case study of Weinan City, China
Core Problem: Field surveys in rural Weinan assign EMS-98 vulnerability classes to self-built housing, then train a machine-learning proxy that is applied across the study area with remote-sensing building information.
Key Innovation: Field-derived EMS-98 labels, remote sensing and machine learning scale building vulnerability and displacement estimates across rural Weinan.
41. Liquefaction of alpine lake sediments: Predictive simulations and post-event validation at Spitallamm Reservoir
Core Problem: Fine-grained lacustrine sediments in alpine reservoirs may exhibit pronounced undrained instability when subjected to rapid hydraulic unloading.
Key Innovation: Pre-event numerical prediction is tested against the observed Spitallamm alpine-reservoir sediment failure.
42. Distribution Patterns of Moraines and Susceptibility Assessment of Geological Hazards in the Shangri-La Region, Northwest Yunnan
Core Problem: A Monte Carlo simulation was also conducted to test the sensitivity of the factor weights to potential uncertainties in expert judgments.
Key Innovation: Mapped moraine deposits enter an uncertainty-tested GIS-AHP model for landslide and debris-flow susceptibility in Shangri-La.
43. Behavior of reinforced concrete buildings subjected to wind loads plus vertical and horizontal ground motions
Core Problem: Structural design commonly treats wind and earthquake actions independently, leaving the axial-force and failure consequences of simultaneous wind and vertical-horizontal shaking unresolved.
Key Innovation: Analyses of low-, medium- and high-rise reinforced-concrete buildings compare separate and combined actions and show that wind and vertical motion can materially alter column demand and failure sequence.
44. Community perception of institutional failure in flood early warning systems in South Africa.
Core Problem: Flood warnings can fail even when forecasts exist if messages arrive late, exclude residents without reliable mobile access or come from institutions that communities no longer trust.
Key Innovation: Interviews with 36 residents identify seven connected failure modes and place trust repair, multi-modal dissemination and access equity ahead of a technical-only warning upgrade.
45. Crosscurrents in Flood Planning and Insurance Discounts: What Drives Action?
Core Problem: Voluntary flood-risk programmes may reproduce local resource inequalities when participation requires administrative capacity that exposed counties do not possess.
Key Innovation: A three-decade county panel finds only 17% uptake of the US Community Rating System and substantially higher participation among larger and wealthier counties, while party affiliation is not a significant predictor.
46. Data-driven method for seismic response prediction of rocking rigid bodies in buildings using deep neural networks
Core Problem: City-scale screening of earthquake-induced overturning is prohibitively expensive when every rigid non-structural object must be simulated dynamically.
Key Innovation: A deep neural network trained on nonlinear response simulations predicts rocking-body overturning with reported accuracy of 94.37%, converting high-fidelity analyses into rapid regional screening labels.
47. Effect of a semi-cylindrical weathered canyon on the seismic response of a bridge under SH waves
Core Problem: Bridge demand near a canyon cannot be inferred from homogeneous-site motion when a weathered surface layer changes SH-wave scattering and local amplification.
Key Innovation: An analytical canyon-bridge solution varies layer stiffness, thickness, incidence and geometry to isolate the weathered canyon's contribution to seismic amplification and structural response.
48. 2024 Flood in Rio Grande do Sul: Climatic Conditions and Changes in Vegetation Cover in the Jacuí River Sub-Basin
Core Problem: The 2024 Rio Grande do Sul flood requires spatial evidence that separates exceptional moisture and inundation from background vegetation variability in the Jacuí sub-basin.
Key Innovation: Google Earth Engine combines CHIRPS rainfall with Sentinel-2 NDWI and NDVI for 2023-2024, mapping the increase in wet surfaces and the persistence of post-flood water signatures.
49. Forests as Living Public Infrastructure: Evidence Requirements for Mediterranean Wildfire Risk Governance, Restoration and Carbon Accounting
Core Problem: Wildfire prevention, ecological restoration and carbon claims are often audited through incompatible evidence chains, allowing activity counts to substitute for demonstrated risk reduction or persistence.
Key Innovation: A Mediterranean synthesis defines shared evidence gates from diagnosis and intervention through field measurement, carbon integrity and public audit, with Greece used as an implementation case.
50. Full‐Scale Shaking‐Table Evaluation of a Controlled Multiple‐Rocking‐Column System for Low‐Damage Seismic Performance
Core Problem: Low-damage seismic systems require full-scale evidence that self-centring mechanisms preserve immediate occupancy under repeated and very rare shaking.
Key Innovation: A three-storey controlled multiple-rocking-column frame is tested from 0.055 g to 0.62 g and retains negligible residual drift with no visible damage through the sequence.
51. Improved forecasting of spring flood volume and low-flow discharge in the Buktyrma river (Kazakhstan) using hydrometeorological and soil–water indicators
Core Problem: Spring-flood and low-flow forecasts in snow-dominated Central Asian basins omit antecedent soil-water depletion when they rely only on precipitation and melt-season temperature.
Key Innovation: A soil-water indicator derived from end-of-flood and winter-minimum discharge augments conventional predictors and improves both flood-volume and low-flow regression forecasts for the Buktyrma River.
52. Linear motion guide (LMG) and tension springs for base isolation of building models - Seismic performance assessment
Core Problem: Affordable isolation systems must reduce building acceleration while maintaining controlled displacement and reliable restoring force under earthquake excitation.
Key Innovation: Shaking-table tests compare fixed-base models with a mechanically simple assembly of linear motion guides and tension springs across alternative spring configurations.
53. Linking land–atmosphere processes and hydrological response under prolonged drought in a semi-arid basin
Core Problem: Precipitation deficits alone do not explain how prolonged semi-arid drought propagates through atmospheric demand, land-surface drying, basin storage and runoff.
Key Innovation: Joint meteorological, land-surface and hydrological indicators resolve process lags and feedbacks across a persistent drought, linking atmospheric forcing to basin response.
54. Research on remote sensing landslide image recognition method based on dual-model ensemble swin transformer
Core Problem: Remote-sensing landslide recognition must preserve irregular boundaries and multi-scale terrain context while separating failures from spectrally similar surroundings.
Key Innovation: A dual-model ensemble combines a Swin Transformer backbone with SCPD-Deeplabv3+ and LSMFormer branches and reports 91.88% mean intersection over union on the study dataset; transfer beyond that dataset remains to be established.
55. Seismic behavior of RC skewed bridges equipped with BRB elements under rotational ground motions
Core Problem: Rotational ground motion is usually omitted from skew-bridge analysis even though plan irregularity can convert it into large deck rotation and shear-key demand.
Key Innovation: Nonlinear analyses report increases of up to 150% in deck rotation and 68% in shear-key deformation, then quantify how buckling-restrained braces reduce those demands.
56. Spatial evaluation of urban seismic resilience using GIS and remote sensing
Core Problem: Urban seismic resilience spans built, social and service-system conditions that cannot be compared reliably from a single exposure or vulnerability indicator.
Key Innovation: A four-level system integrates 18 GIS and remote-sensing indicators with entropy weighting and a deep-belief network, producing spatially explicit resilience scores for six cities.
57. The Public Perception Of Earthquake Disaster Mitigation In Teluk Betung Timur District In 2025
Core Problem: Household awareness does not guarantee preparedness when neighbourhoods differ in access to earthquake warnings, public education and mitigation infrastructure.
Key Innovation: A 100-household survey across a fault-exposed district identifies village-level warning and outreach gaps despite generally positive risk perception.
58. Adaptive Sampling for Automated Post-Disaster Rapid Damage Assessment via Level-Set Cost-Aware Bayesian Optimization
Core Problem: Post-disaster surveys must locate damage quickly without exhausting limited UAV flight time or repeatedly sampling areas whose condition is already known.
Key Innovation: Cost-aware Bayesian optimization with level-set estimation updates the damage boundary and directs sampling toward uncertainty; controlled and R2D-generated scenarios test boundary recovery and information gain under operational cost.
59. Rainfall Frequency Analysis to Estimate Probable Maximum Precipitation Using Stochastic Storm Transposition With Radar-Rain Gauge Data in Japan
Core Problem: Operational probable-maximum-precipitation estimates in Japan lack an explicit exceedance probability and the spatially evolving storm fields required for hydrological simulation.
Key Innovation: Stochastic storm transposition applied to 35 years of radar-gauge data produces probability-linked design storms for two watersheds and shows where operational depth-area-duration estimates are deliberately conservative.
60. Assessing Land Cover Change Impact on Peak Discharge Using Principal Component Analysis and Random Forest With Remote Sensing Data Assimilation
Core Problem: Flood-peak attribution is difficult when land-cover classes are correlated, hydrometric records are short and nonlinear runoff responses differ among subbasins.
Key Innovation: PCA reduces correlated Landsat-derived predictors, random forests model peak discharge, and reconstructed feature contributions plus SHAP diagnostics identify where urban or barren cover accelerates runoff and vegetation moderates it.
61. An Interpretable Physics-Consistent Neural Framework for Wave-Based System Identification of Buildings
Core Problem: Post-earthquake structural identification needs sensitivity to local stiffness change, stable estimates across events and parameters that engineers can interpret physically.
Key Innovation: A layered Timoshenko-beam propagator becomes the neural computational graph itself; nine earthquake records from a 54-storey building yield layer-wise shear-wave velocities with coefficients of variation no greater than 5%.
62. EchoChange: A Diffusion Language Model With Dual Pass Remasking for Factual Remote Sensing Disaster Change Captioning
Core Problem: Autoregressive disaster change captioning cannot revisit an early object or relation error, allowing visual ambiguity to cascade through the final description.
Key Innovation: EchoChange uses discrete-diffusion denoising, draft-aware dual-pass training and confidence-guided remasking to revise the whole caption repeatedly, improving lexical and semantic performance on RSCC while awaiting broader factual validation.
63. Diagnosing Cryospheric Runoff Dynamics: A Distributed Differentiable Hydrological Model With Global Transfer Learning
Core Problem: Accurate hydrological prediction in alpine regions remains challenging due to complex cryospheric processes and limited observational records.
Key Innovation: A distributed differentiable hydrological model diagnoses cryospheric runoff while testing transfer across basins.
64. Hybrid Quantum CNN for Cross-Sensor Spaceborne Volcanic Thermal Activity Recognition Worldwide
Core Problem: As Earth Observation (EO) enters the Big Data era, the exponential volume of daily satellite imagery poses significant computational and storage challenges for classical Deep Learning (DL) models.
Key Innovation: A cross-sensor global experiment tests hybrid quantum convolution for volcanic thermal-activity recognition.
65. GeoArbiter: Verifiability-Guided Grounding for Remote-Sensing Multimodal LLMs
Core Problem: However, retrieved records can also contradict visible evidence, and we find that models frequently follow the records even when the image is decisive.
Key Innovation: GeoArbiter checks multimodal remote-sensing answers against verifiable spatial evidence.
66. SPECTRA: Band-Routed Embedding and Stage-Wise LoRA for Cross-Sensor Fine-Tuning of Geospatial Foundation Models
Core Problem: However, there are two challenges of adapting EO-pretrained GeoFMs to practical downstream datasets.
Key Innovation: SPECTRA routes spectral bands and applies stage-wise LoRA for cross-sensor geospatial foundation-model tuning.
67. RSVideo: Are Your Vision-Language Models Ready for Remote Sensing Videos?
Core Problem: However, a unified evaluation setting for assessing vision-language models on continuous remote-sensing video understanding remains lacking.
Key Innovation: RSVideo measures whether vision-language models reason over temporal change in Earth-observation video.
68. Probabilistic Deep Learning for Drought Forecasting: Role of Internal Climate Variability
Core Problem: Yet drought forecasts remain uncertain because variability can substantially alter regional precipitation and evaporative demand.
Key Innovation: A probabilistic deep network represents structured internal climate variability in European drought bounds.
69. Semantic-Guided Cross-Sensor Super Resolution of Remote Sensing Images: A Gated Dual Conditioning Flow Matching Model
Core Problem: High spatial resolution satellite imagery is critical for monitoring fine-scale Earth surface processes, but is often limited by cost and revisit time.
Key Innovation: Semantic guidance and gated dual conditioning align fine spatial detail across sensors in a flow-matching super-resolution model.
70. OSMDA: OpenStreetMap-based Domain Adaptation for Remote Sensing VLMs
Core Problem: Vision-Language Models (VLMs) adapted to remote sensing rely heavily on domain-specific image-text supervision, yet high-quality annotations for satellite and aerial imagery remain scarce and expensive to produce.
Key Innovation: OSM-derived spatial supervision adapts remote-sensing vision-language models across regions without dense manual labels.
71. FusionRS: A Large-Scale RGB-Infrared-Style Remote Sensing Dataset for Cross-Modal Vision-Language Learning
Core Problem: Remote sensing vision-language models have advanced Earth observation, but available large-scale vision-language resources remain RGB-centered, leaving complementary infrared information underexplored.
Key Innovation: FusionRS provides large-scale paired RGB and infrared-style remote-sensing data for cross-modal vision-language learning.
72. OVEarth-Bench: Evaluating Category Breadth and Query Diversity for Open-Vocabulary Earth Observation
Core Problem: Existing benchmarks, however, usually cover narrow category vocabularies or limited query forms.
Key Innovation: OVEarth-Bench tests category breadth and query diversity rather than a narrow fixed-label Earth-observation vocabulary.
73. MERIT-FullBasin: A global 90-meter basin dataset with a per-pixel upstream index and morphometric attributes
Core Problem: Fine-resolution hydrographic data are essential for flood prediction and risk mapping, large-sample hydrology, and machine-learning streamflow models.
Key Innovation: MERIT-FullBasin adds per-pixel upstream topology and morphometry to a seamless global 90 m basin grid.
74. A Study on Radar–Gauge Rainfall Data Merging and Its Impact on Flood Simulation
Core Problem: Accurate rainfall input is critical for reliable flood simulation, particularly in semi-arid watersheds with pronounced spatiotemporal precipitation heterogeneity.
Key Innovation: Radar-gauge merging methods are evaluated through their downstream effect on flood simulation rather than rainfall error alone.
75. Spatial Correspondence Between Seismic b -Value Stress Localization and Pre-Earthquake GNSS-TEC Anomalies in Southern California and Northern Baja California
Core Problem: Identifying reliable earthquake precursors remains a major challenge in geophysics.
Key Innovation: Spatial correspondence between b-value stress localization and GNSS-TEC anomalies is tested for southern California and Baja California.
76. Spatiotemporal Characteristics and Hydrogeological–Urban Controls of Surface Deformation in Haikou, China, Revealed by PS/DS-InSAR and Spatial Attribution Analysis
Core Problem: Surface deformation in rapidly urbanizing coastal cities is often shaped by the interplay between hydrogeological setting and development disturbance, yet these controls remain insufficiently constrained in tropical coastal environments.
Key Innovation: PS/DS-InSAR and spatial attribution separate hydrogeological and urban controls on deformation in Haikou.
77. Physics-Aware Deep Learning for SAR and InSAR Remote Sensing: Models, Methods, and Open Challenges
Core Problem: SAR and InSAR are fundamental sensing modalities for all-weather, day-and-night Earth observation because they operate independently of solar illumination and retain sensitivity to scene structure under conditions that often limit optical imaging.
Key Innovation: A review organizes physics-aware deep learning across SAR and InSAR forward models, constraints and unresolved validation problems.
78. Pseudo 3-D GPR and 2-D ERT Study to Reveal Subtle Tectonic Deformations of a Strike-Slip Raša Fault (Dinaric Fault System, W Slovenia) in Fluvial and Karstic Environments
Core Problem: Since the surface exposure of fault-related markers is discontinuous, and the near-surface expression of deformation is poorly constrained, there is a need to improve the detection of fault-related features in complex sedimentary environments.
Key Innovation: Pseudo-3D GPR and 2D ERT resolve subtle strike-slip fault deformation in fluvial and karst terrain.
79. A Multi-Parameter, Multi-Temporal, Large-Scale Retrospective Analysis Based on the Successful Prediction of the 2020 Dingri M5.9 Earthquake
Core Problem: The study focuses on the 2020 M5.9 Dingri earthquake in Tibet, which occurred within a monthly M5.5±0.2 hazard zone predicted by the China Earthquake Administration using NOAA OLR data.
Key Innovation: A retrospective multi-parameter analysis compares OLR, tides, gravity and GPS around the 2020 Dingri earthquake; its predictive claims require prospective testing.
80. Event-based high-temporal resolution modeling of streamflow and sediment transport in a flood-prone catchment
Core Problem: Streamflow performance ranged from limited to very good, whereas sediment simulation ranged from limited to good.
Key Innovation: Hourly SWAT calibration exposes event-scale limits in simulating peak flow, timing and sediment export in a steep flood-prone catchment.
81. Beyond prediction accuracy: using differentiable hybrid models as a tool for hydrological knowledge discovery
Core Problem: Differentiable hybrid modelling has dramatically improved streamflow prediction, yet its potential as a tool for genuine scientific discovery remains largely untapped.
Key Innovation: A differentiable diagnosis-extraction-improvement loop uses neural substitutions to expose and repair process-model structural error across 476 catchments.
82. Forensic hydrological assessment of flood response under land use/land cover (LULC) change using ANN and HEC–HMS
Core Problem: The study develops an integrative methodological framework that combines forensic hydrology, artificial neural networks (ANNs), and physically-based hydrological modeling to assess the cascading impacts of projected land use/land cover (LULC) changes on future flood dynamics in the upstream watershed of Golestan Dam, Iran.
Key Innovation: ANN projections and HEC-HMS simulations trace nonlinear flood amplification under future land-cover scenarios.
83. Shaking table tests on longitudinal seismic responses of shield tunnel in non-uniform liquefiable sites
Core Problem: Using the Shantou Bay Subsea Tunnel as a case study, this research conducted free-field and longitudinal non-uniform shaking table tests to investigate the longitudinal seismic responses of the surrounding soil and the shield tunnel in non-uniform liquefiable strata.
Key Innovation: Shaking-table tests quantify longitudinal tunnel response across non-uniform liquefiable strata and abrupt soil interfaces.
84. Vector-valued fragility analysis of 3D block quay walls under mainshock-aftershock sequences using the vine copula method
Core Problem: Gravity block quay walls are critical marine infrastructure systems whose seismic vulnerability may increase significantly under sequential earthquake events.
Key Innovation: Three-dimensional nonlinear models and vine copulas resolve cumulative mainshock-aftershock damage in block quay walls.
85. Machine Learning based Flood Susceptibility assessment in the West Rapti River Basin using Multi Sourced Geospatial Data
Core Problem: Identification of flood-prone areas is a substantial need for effective planning and risk management.
Key Innovation: Multi-source geospatial predictors and machine learning map flood susceptibility in Nepal's West Rapti basin.
86. Transboundary flood dynamics in the Sutlej basin (2025): Sentinel-1 SAR insights on inundation and reservoir release impacts
Core Problem: Multi-temporal Sentinel-1 SAR maps approximately 2,693 square kilometres of inundation during the 2025 Sutlej Basin flood with reported accuracy of 0.94.
Key Innovation: Sentinel-1 inundation mapping links the 2025 Sutlej flood footprint to extreme rainfall and emergency reservoir releases.
87. Predicting post-earthquake settlement of river embankments using parametric numerical
Core Problem: However, these analyses are time-consuming and costly, which limits their applicability in preliminary evaluations.
Key Innovation: A parametric numerical chart screens post-earthquake river-embankment settlement and is checked against damaged cross-sections.
88. Unraveling the Impacts of Land Use/Land Cover and Climate Change on Water Erosion in the Northern Andes: A Predictive GIS Modeling Approach
Core Problem: Climate variability and land use/land cover changes (LULCC) can intensify soil erosion.
Key Innovation: Scenario modelling separates land-cover and climate contributions to future water erosion in a northern Andean watershed.
89. Density estimation of weak periodic signals in pre-earthquake seismic waves
Core Problem: Weak deterministic components in noisy pre-earthquake waveforms are difficult to distinguish from ordinary background variability, and retrospective anomalies can be mistaken for forecasts.
Key Innovation: A driven Duffing-oscillator scale response and kernel-density analysis compare three pre-event records with two quiet controls; the resulting frequency shifts are a retrospective monitoring signal, not prospective validation.
90. Dynamic Leveling of the Kanto Basin, Japan: A Unified Model Linking the Kanto Fragment and Mw 8 Deep Earthquakes
Core Problem: Long-wavelength levelling changes in the Kanto Basin have no accepted single mechanism linking deep earthquakes, crustal structure and near-surface consolidation.
Key Innovation: A Kanto-fragment model connects stress shielding and Mw 8 deep earthquakes to liquefaction, lateral spreading and differential consolidation, offering testable hypotheses rather than a validated forecast model.
91. Floods with Debris and Anthropogenic Waste: Accounted for or Not?
Core Problem: Flood maps and regulations commonly represent water extent and depth but omit mobile debris and waste that can block channels and intensify local damage outside designated risk zones.
Key Innovation: A process review and Bulgarian flood case identify this omission and specify where debris loading should enter flood assessment, mapping and regulation.
92. A Two-Stage Machine Learning Framework for High-Resolution Multi-Source Precipitation Fusion in Complex Terrain: A Case Study of Shaoxing, China
Core Problem: Radar, satellite and gauge precipitation each miss important aspects of intense rainfall over complex terrain, limiting the forcing available to small-basin flash-flood models.
Key Innovation: A two-stage occurrence-and-intensity model fuses 61 features at 1 km and 1 h resolution; station-blocked validation and 15 held-out gauges show a 49.22% RMSE reduction over the satellite estimate and better detection above 10 mm per hour.
93. Advancing the Classification of Supraglacial Lake Winter Behaviours on the Greenland Ice Sheet Using Sentinel-1 Time Series Analysis
Core Problem: Polar darkness prevents optical observation of Greenland supraglacial lakes whose winter storage, freeze-through and drainage affect runoff routing and ice dynamics.
Key Innovation: Unsupervised clustering of Sentinel-1 time series detects winter lakes with 87.6% accuracy and classifies three behavioural states with 75.0% accuracy without labelled training data, while revealing within-winter state changes.
94. Volcanic Clouds Detection through QCNN and Geostationary Satellite Multispectral Imagery
Core Problem: These capabilities have motivated the exploration of whether quantum-enhanced models can address long-standing challenges in satellite remote sensing, where complex spectral and spatial signals often require sophisticated feature extraction.
Key Innovation: A quantum convolutional classifier is tested on geostationary multispectral imagery for volcanic-cloud recognition.
95. BRIC-Net: Boundary-Reliable Illumination-Color Interaction for Remote Sensing Image Deshadowing
Core Problem: Shadows in remote sensing images obscure surface appearance and disrupt radiometric continuity, reducing the reliability of visual interpretation and downstream analysis.
Key Innovation: Boundary-reliable illumination-colour modelling targets shadows that corrupt downstream remote-sensing interpretation.
96. Proteus: A Truncation-Robust Entropy Model for Progressive LiDAR Compression
Core Problem: However, wireless channels inherently impair and corrupt transmitted signals.
Key Innovation: A truncation-robust entropy model supports progressive LiDAR transmission under variable bandwidth.
97. Breaking Diversity Collapse in Spiking Pseudo-Ensembles for Efficient OOD Detection in Remote Sensing
Core Problem: Spiking Neural Networks (SNNs) are attractive for resource-constrained remote-sensing systems, but reliable out-of-distribution (OOD) detection remains challenging.
Key Innovation: A spiking pseudo-ensemble is regularized against diversity collapse for efficient remote-sensing OOD detection.
98. QWRF-Net: A Quantum-Wavelet Framework with Rectified Flow for Short-Term Precipitation Nowcasting
Core Problem: A key challenge in warning-oriented nowcasting is that radar precipitation fields contain strongly coupled multi-scale structures, while forecast quality often degrades at later lead times, making it difficult to preserve intense precipitation cores and their spatial organization over the full warning-relevant horizon.
Key Innovation: Wavelet decomposition and rectified flow are combined for short-term precipitation nowcasting.
99. PNEC-Mamba: Prototype-Guided Positive-Negative Evidence Calibration for Hyperspectral Image Classification
Core Problem: In real-world hyperspectral scenes, pixel representations are often ambiguous due to factors such as spectral similarity, mixed pixels, and local context interference, which may simultaneously encode discriminative evidence and interfering information.
Key Innovation: Positive-negative evidence calibration improves uncertainty handling in a Mamba hyperspectral classifier.
100. Global-Scale Self-Supervised Spatiotemporal Learning for NDVI Time-Series Reconstruction
Core Problem: Accurate and efficient reconstruction of cloud-contaminated and noise-corrupted NDVI time series remains a challenge in remote sensing.
Key Innovation: Global self-supervision reconstructs missing NDVI observations while testing transfer across climates and sensors.
101. USP-Mamba: Unmixing-Derived Spectral and Structural Prompting for Hyperspectral Image Super-Resolution
Core Problem: Hyperspectral image super-resolution aims to reconstruct high-resolution imagery while preserving dense spectral information.
Key Innovation: Spectral unmixing supplies structural prompts to a state-space hyperspectral super-resolution model.
102. UAV-Based Environmental Monitoring of Rip-Current Indicators Using Wavelet-Derived Texture Features
Core Problem: Rip currents are recurrent coastal natural hazards that threaten beachgoers and create operational challenges for lifeguards and coastal managers.
Key Innovation: Wavelet-derived image texture is evaluated as a UAV indicator of rip-current activity.
103. ISRS-DETR: Detection-Guided Click Propagation for Remote Sensing Interactive Segmentation
Core Problem: However, applying this paradigm directly to remote sensing imagery is non-trivial: ultra-high resolutions, small object sizes, and sparse spatial distributions all degrade segmentation quality.
Key Innovation: Detection-guided click propagation reduces interaction burden for remote-sensing segmentation.
104. Onboard Satellite Image Classification for Earth Observation: A Comparative Study of ViT Models
Core Problem: Remote sensing (RS) image classification is central to Earth observation, but onboard deployment requires models that are accurate, efficient, and robust to sensor and transmission degradation.
Key Innovation: Vision Transformers are compared under onboard compute and accuracy constraints for satellite image classification.
105. Retrieval of Coastal Biogeochemical Parameters From Near-Surface Hyperspectral Remote Sensing Reflectance Using Physics-Aware Meta-Learning
Core Problem: However, generalising such retrieval algorithms across water bodies remains challenging, as the relationship between remote sensing reflectance (Rrs) and BGC parameters can vary considerably from one region to another due to regional distinctions in environmental conditions and biogeochemistry that lead to different BGC ranges and bio-optical properties.
Key Innovation: Physics-aware meta-learning targets coastal biogeochemical retrieval under limited local calibration.
106. Backward Erosion Piping: Influence ofFull Grain Size Distribution
Core Problem: This resistance is well understood for uniform materials; however, further research is required for more widely graded soils to account for the influence of the entire grain size distribution in characterizing the erosion process.
Key Innovation: Experiments test how the full grain-size distribution controls backward-erosion piping resistance in dam foundations.
107. CFD–DEM simulation of seabed scour around submarine pipelines with particle-scale analysis of sediment transport
Core Problem: Submarine pipelines are susceptible to local scour, which may induce free spans and threaten structural safety.
Key Innovation: Validated CFD-DEM simulation resolves pipeline scour from bedform to particle transport scales.
108. Wave-attenuating and wave-induced loading performances of an artificial reef with realistic coral canopies: Implications for optimal deployment
Core Problem: In this study, we investigate experimentally and numerically the wave-induced loading and wave attenuation performances of a multifunctional artificial reef with a coral canopy intended for both restoration and shore protection, with special focus on an optimization to achieve good wave attenuation under minimal wave-induced loading by considering different restoration sites across the reef flat.
Key Innovation: A realistic coral-canopy artificial reef is optimized jointly for wave attenuation and structural loading.
109. Precipitation nowcasting based on convolutional LSTM with spatio-temporal information transformation using multi-meteorological factors
Core Problem: Precipitation nowcasting is vital for protecting lives and economic activities, yet accurate forecasts based solely on past precipitation remain elusive.
Key Innovation: A convolutional LSTM transforms multiple short-term meteorological fields for computationally efficient precipitation nowcasting.
110. Field-scale soil moisture retrieval from drone-based L-band radiometry with optical and thermal infrared priors
Core Problem: Drone-based low-frequency (L-band) radiometry offers a promising intermediate scale between in situ measurements and satellite observations, but retrieval remains ill-posed because brightness temperature depends jointly on soil dielectric properties, vegetation attenuation, surface temperature, and sub-footprint heterogeneity.
Key Innovation: Bayesian retrieval fuses UAV L-band radiometry with optical and thermal priors and propagates field-scale uncertainty.
111. Satellite Detection of Diffuse Tectonic CO2 Degassing in the East African Rift
Core Problem: However, the nature and amount of diffuse CO₂ fluxes from faults in continental rifts remain largely unconstrained.
Key Innovation: Satellite observations are used to detect diffuse tectonic carbon-dioxide degassing along the East African Rift.
112. Consistent Photometric Enhancement Network for Remote Sensing Change Detection
Core Problem: Change detection performance in remote sensing is highly sensitive to illumination variations between bi-temporal images.
Key Innovation: Photometric enhancement and change inference are trained jointly to reduce illumination-driven false changes.
113. Few-Shot SAR Object Detection with Prior Class Perceptron and Cross-Entropy
Core Problem: Few-Shot Synthetic Aperture Radar (SAR) object detection aims to identify and localize unseen categories using only a small number of annotated support samples.
Key Innovation: A prior-class perceptron and cross-entropy formulation address sparse labels in SAR object detection.
114. Diagnosing and Conditionally Correcting X-Band Radar Underestimation in Cyprus: A Cross-Validated Evaluation of Spatial Merging and Machine Learning Approaches
Core Problem: Radar-based Quantitative Precipitation Estimation (QPE) in semi-arid Mediterranean climates is critically challenged by systematic underestimation of shallow precipitation, yet gauge–radar merging frameworks tailored to such environments remain poorly evaluated.
Key Innovation: Cross-validation determines when spatial merging and machine learning can correct X-band radar underestimation.
115. CDGP-Net: Channel-Decoupling and Geographic-Prior Fusion for Spatial Super-Resolution of HIRAS Radiances with Co-Platform MERSI-II
Core Problem: Hyperspectral infrared sounders provide valuable observations for numerical weather prediction (NWP), but their native nadir spatial resolution of approximately 12–16 km is coarser than the approximately 4 km grid spacing commonly used in convection-permitting regional forecasting systems.
Key Innovation: Channel decoupling and geographic priors sharpen HIRAS radiances using co-platform MERSI-II observations.
116. Change Detection in Remote Sensing Imagery: A Systematic Review of Statistical, Machine Learning, and Deep Learning Methods
Core Problem: Change detection (CD) is a fundamental remote sensing task that identifies surface modifications from multi-temporal imagery of the same area, with applications in urban monitoring, agriculture, forest disturbance mapping, disaster assessment, and land cover analysis.
Key Innovation: A systematic review compares statistical, machine-learning and deep-learning change-detection families and their validation practices.
117. Impact of Clouds on Infrared and Microwave Sounding Retrieval and an Objective Correction Method for Numerical Weather Prediction
Core Problem: Atmospheric temperature and moisture profiles retrieved from spaceborne sounders provide essential environmental information in regions lacking in situ observations.
Key Innovation: Cloud impacts on infrared and microwave sounding are diagnosed before objective correction for numerical weather prediction.
118. An Adaptive Polyline-Path Mask Attention for Hyperspectral and Multispectral Image Fusion
Core Problem: However, existing Transformer-based HSI–MSI fusion methods still face difficulty in jointly preserving geometric structures and spectral continuity.
Key Innovation: Polyline-path mask attention preserves elongated spatial structures during hyperspectral-multispectral fusion.
119. Towards people-centred approaches in disaster risk reduction – the first mile: Dam-failure risk management in a Swedish river basin
Core Problem: These governance dynamics constrain the perceived feasibility of integrating community knowledge and engagement into preparedness processes, particularly in risk communication, early warning, evacuation planning, and exercises.
Key Innovation: A Swedish basin case study identifies institutional barriers to citizen participation in dam-failure contingency planning.
120. Compounding heat and hydrological stress under climate change amplifies land degradation in Northwest Queensland rangelands
Core Problem: The study assessed land degradation risk in the rangelands of the Southern Gulf region (Northwest Queensland, Australia) using climate, soil and vegetation data.
Key Innovation: A composite index maps where acute heat stress and chronic aridity compound land-degradation risk in northwest Queensland.
121. Visual detection method for tunnel cracks based on target edge annotation error constraints
Core Problem: The considerable uncertainty inherent in manual annotation of target edges results in significantly larger annotation errors for crack samples than for block-like defects, which in turn introduces substantial uncertainty during the training of crack detection models and leads to detection performance markedly inferior to that for block-like defects.
Key Innovation: Edge-annotation uncertainty is modelled explicitly to improve visual detection of thin tunnel cracks.
122. Coastal Flood Risk Governance in Newfoundland, Canada: A Multidimensional Analysis of Assessment, Management, and Communication
Core Problem: Findings reveal that although Newfoundland employs various tools for hazard identification (e.g., LiDAR, hydrological models) and vulnerability assessment, challenges persist in data resolution, equitable resource distribution, and consistent public engagement.
Key Innovation: A multidimensional review traces how coastal flood risk is assessed, managed and communicated in Newfoundland.
123. Regression and Neural Network Modeling of Earthquake Hypocenter Depth in the Broader Croatia–Adriatic–Dinarides Region
Core Problem: Hypocentre-depth estimates in the Croatia-Adriatic-Dinarides catalogue remain uncertain where location, magnitude and calendar attributes have nonlinear but weak predictive relationships with depth.
Key Innovation: Four regressors are compared by five-fold validation on 6,197 events; the multilayer perceptron lowers RMSE by 25.7% relative to a dummy baseline but retains an 18.10 km RMSE.
124. Statistical Analysis, Feature Selection, and Optimization Techniques for Accurate Seismic Event Forecasting and Risk Assessment in Advanced Earthquake Prediction
Core Problem: High in-sample seismic-event classification scores can be mistaken for earthquake forecasting skill when feature leakage, temporal transfer and independent validation are not resolved.
Key Innovation: Feature selection and two metaheuristic optimizers tune quadratic discriminant analysis to a reported 0.933 test accuracy; the result is retained as a transferable method study with explicit need for prospective validation.
125. From field-scale to large-scale spectral libraries: Tabular foundation models in soil spectroscopy
Core Problem: Yet, translating high-dimensional, highly collinear spectra into accurate soil property predictions remains challenging, particularly when employing machine learning.
Key Innovation: Tabular foundation models are tested for transfer from field-scale to large soil-spectral libraries.
126. OSSDD - a New Open Dataset for Sentinel-1 Ship Detection
Core Problem: Modern ship detection methods using neural networks usually require large training datasets, which are considerably scarcer in the SAR domain than in the electro-optical domain.
Key Innovation: OSSDD releases an open Sentinel-1 ship-detection dataset and baselines relevant to SAR representation learning.
127. Fermat Active Laplace Learning for Semi-Supervised Hyperspectral Image Classification
Core Problem: Two active learning algorithms for hyperspectral image (HSI) classification are proposed that combine density-aware Fermat distances with Poisson-reweighted harmonic label propagation.
Key Innovation: Graph-based active learning selects informative hyperspectral labels under sparse supervision.
128. Edge-Aware Spatial–Spectral Mamba for Contrastive Clustering of Hyperspectral Images
Core Problem: However, ViT-based deep clustering architectures suffer from quadratic computational complexity, imposing a significant memory burden for high-resolution HSIs.
Key Innovation: An edge-aware spatial-spectral Mamba preserves neighbourhood topology in linear-time hyperspectral clustering.
129. Beyond Pixels: Identifying Built-Up Features at Subpixel Level for Enhanced Satellite-Based Land Cover Mapping
Core Problem: Identifying buildings and urban/built-up features has become increasingly important as cities grow more complex and traditional pixel-based classification methods struggle with mixed-land-cover signatures.
Key Innovation: Subpixel mixture features recover built-up structures below Sentinel-2's nominal spatial resolution.
130. SDRCNet: A Lightweight Structure-Guided Dual-Relation Consensus Network for Optical Remote Sensing Images
Core Problem: Multi-label classification of very-high-resolution remote sensing scenes is difficult not only because multiple land-cover categories coexist in one image, but also because their discriminative evidence is spatially uneven: boundaries, elongated structures, and fragmented regions are often weakened by appearance-dominated features; small categories can be suppressed by global scene responses; and large-area categories require broader spatial context.
Key Innovation: A lightweight structure-guided dual-relation network balances spatial detail and context in optical imagery.
131. Frequency-Domain Modeling and Removal of Platform Jitter Stripes in GF-7 DSMs for Flat Terrains
Core Problem: Platform jitter in stereo mapping satellites introduces periodic stripe artifacts into digital surface models (DSMs), degrading geometric quality, while existing detection methods usually depend on disparity maps or high-frequency attitude data.
Key Innovation: Frequency-domain modelling removes platform-jitter stripes from GF-7 digital surface models.
132. GeoAdapt: Fine-Grained Keypoint Localization via Deformable Feature Refinement for Ground-Based Optical Remote Sensing
Core Problem: Ground-based optical remote sensing of aerial targets at kilometer-scale standoff distances requires accurate keypoint localization for six-degree-of-freedom (6-DoF) pose recovery under variable illumination, motion blur, and atmospheric degradation.
Key Innovation: Deformable feature refinement improves fine-grained keypoint localization in ground-based optical observations.
133. An Improved JSEG-Based Algorithm for Segmentation of Categorical and Remote Sensing Classification Maps
Core Problem: However, these products are usually analysed at the pixel level, which limits the identification of larger spatial structures and coherent landscape units.
Key Innovation: An improved JSEG workflow segments categorical and remote-sensing classification maps.
134. Occlusion Removal in Remote Sensing Images Based on Deep Matrix Completion
Core Problem: Matrix completion provides an effective solution for recovering incomplete data; however, existing deep learning-based approaches often rely on random initialization, resulting in slow optimization and limited reconstruction quality under severe missing conditions.
Key Innovation: Deep matrix completion reconstructs remote-sensing imagery occluded by foreground objects or missing observations.
135. Open-Source Reproducible Pipeline for Multitemporal Vegetation Monitoring Using Sentinel-2 L2A in Cloud-Prone Tropical Regions
Core Problem: Monitoring vegetation-index dynamics in tropical regions remains challenging due to persistent cloud contamination, landscape heterogeneity, and the lack of standardised and reproducible analytical workflows.
Key Innovation: An open pipeline standardizes multitemporal Sentinel-2 vegetation monitoring in persistently cloudy tropical regions.
136. Hyperspectral Image Classification Based on a Spatial–Spectral Dual-Branch Mamba Architecture
Core Problem: Existing Transformer-based methods have achieved excellent performance but are limited by the quadratic computational complexity of the self-attention mechanism, while the high-dimensional redundancy of hyperspectral data and the difficulty in deeply integrating spatial–spectral features also restrict further performance improvement.
Key Innovation: A dual-branch Mamba separates spatial and spectral dependencies for hyperspectral classification.
137. SGW-DETR: A Spectral-Guided Graph-Structured Wavelet Transformer for UAV Infrared Object Detection Under Degradation
Core Problem: Infrared object detection from unmanned aerial vehicles (UAVs) is critically challenged by multi-type composite degradation—including noise, blur, and low contrast—which severely undermines feature discriminability and multi-scale target perception.
Key Innovation: Spectral guidance, graph structure and wavelets target UAV infrared detection under degraded imaging.
138. DyPerceiver-Det: Instance-Wise Dynamic Perception for Fine-Grained Oriented Object Detection in Remote Sensing Images
Core Problem: Fine-grained oriented object detection in remote sensing is challenged by extreme scale variation, arbitrary rotations, and dense layouts with structured clutter, where fixed feature selection and RoI extraction often lead to unstable localization and intra-class confusion.
Key Innovation: Instance-wise dynamic perception adapts features to fine-grained oriented targets in remote-sensing images.
139. Enhanced data-based analysis introducing a previously unidentified empirical hydrological benchmark: baseflow dominance threshold
Core Problem: Enhanced understanding of baseflow dynamics is critical for effective water resources management, especially in regions experiencing significant climatic variability.
Key Innovation: Ninety-four gauges reveal an empirical baseflow-dominance threshold and its climatic variation.
140. Evaluating Deterministic Spatial Interpolation Methods for Mapping Soil Geochemical Heterogeneity in Flood-Affected Alluvial Plains of Maglaj Area, Bosnia and Herzegovina
Core Problem: Deterministic interpolation choices can create different apparent pollution patterns in flood-reworked alluvial soils where sparse samples cross hydrological discontinuities.
Key Innovation: Spline, barrier-constrained spline, inverse-distance weighting and natural-neighbour surfaces are compared for 34 composite samples to expose method sensitivity in post-flood geochemical mapping.