Preprints
Filtering by Subject: Artificial Intelligence and Robotics
Terrain-Dependent Generalization Failure in Multimodal Lunar Crater Classification: A Comparative Study Using Optical and Topographic Data
Published: 2026-09-08
Subjects: Artificial Intelligence and Robotics, Computer Sciences, Planetary Geology, Planetary Geomorphology, Planetary Sciences
Automated lunar crater detection is an important task in planetary mapping, geological analysis, and future lunar exploration. Recent studies have demonstrated the potential of combining optical imagery with topographic information; however, the influence of terrain variability on crater classification performance remains insufficiently explored. This study presents a comparative evaluation of [...]
Global Power Outage Detection at the Kilometer Scale from Satellite Nighttime Lights
Published: 2026-08-28
Subjects: Artificial Intelligence and Robotics, Geographic Information Sciences, Geography, Human Geography, Nature and Society Relations, Power and Energy, Public Health, Remote Sensing
Satellite nighttime light observations show great promise for remote sensing of power outages, yet have only been utilized in limited case studies to date. Here, we present generalized detection of power outages across the globe at ~1-kilometer resolution. Our method, the Outage frameWork for Luminosity Interruptions (OWL-I), reveals the power outages can be reliably detected from VIIRS/Black [...]
A Blueprint for Modern Floodplain Management: System Requirements and Architecture for AI-Ready Platforms
Published: 2026-07-29
Subjects: Artificial Intelligence and Robotics, Civil and Environmental Engineering, Computer Sciences, Engineering, Hydraulic Engineering, Physical Sciences and Mathematics, Water Resource Management
Floodplain managers play a critical role in mitigating flood risks and ensuring compliance with federal, state, and local regulations. Their daily work often depends on a patchwork of standalone tools, manual processes, and legacy systems that have evolved over time to meet regulatory and community needs. To establish a foundation for next-generation technological solutions, we synthesized [...]
Detecting illegal water abstractions using Earth Observation and artificial intelligence: challenges, research gaps, and future directions
Published: 2026-07-28
Subjects: Artificial Intelligence and Robotics, Environmental Monitoring, Hydrology, Natural Resources Management and Policy, Remote Sensing, Water Resource Management
Water scarcity and unsustainable withdrawals are major environmental challenges, particularly in semi-arid and Mediterranean regions where agriculture places strong pressure on limited hydrological resources. Illegal or unreported water abstractions intensify these pressures by undermining ecosystem stability, policy compliance, and long-term water security. Although Earth Observation (EO) and [...]
Cloud-Free Imaging Probability Forecasting for Optical Earth-Observation Tasking over Yerevan, Armenia
Published: 2026-07-17
Subjects: Artificial Intelligence and Robotics, Atmospheric Sciences, Environmental Monitoring, Numerical Analysis and Scientific Computing, Remote Sensing
Optical Earth-observation satellites such as Sentinel-2 cannot see through cloud, so a tasking attempt over a cloudy target wastes a limited imaging window along with onboard power and downlink bandwidth. We study whether next-day cloud conditions over Yerevan, Armenia can be forecast accurately enough to automate the binary Go/No-Go tasking decision. Daily cloud-free fractions are derived from [...]
Deep Neural Network-Based Inversion of Turbidites in Confined Basins
Published: 2026-07-11
Subjects: Artificial Intelligence and Robotics, Fluid Dynamics, Geology, Sedimentology
Turbidites generated by large earthquakes and other geological events are commonly preserved in small, topographically confined basins along active continental margins. Reconstructing flow conditions from these deposits is essential for assessing past hazards; however, existing inverse models have been validated only for unconfined settings, and their applicability to confined basins remains [...]
A machine learning approach for detecting biofouling in oceanographic data
Published: 2026-07-11
Subjects: Artificial Intelligence and Robotics, Fluid Dynamics, Numerical Analysis and Computation, Oceanography, Oceanography and Atmospheric Sciences and Meteorology, Physical Sciences and Mathematics
Autonomous ocean observing platforms collect long-term biogeochemical time series, but sensor degradation from biofouling introduces progressive biases that contaminate the climate record. This work focuses on the BGC-Argo fleet of profiling floats, where optical sensors measuring chlorophyll-a and backscatter are particularly susceptible to biofouling. Current detection relies on per-float [...]
Deep learning methods for the simulation and optimization of shallow geothermal energy systems
Published: 2026-07-01
Subjects: Applied Mathematics, Artificial Intelligence and Robotics, Computational Engineering, Geology, Natural Resources Management and Policy
Shallow geothermal energy (SGE) systems are crucial for decarbonizing the heating and cooling sector. Their planning, design and operation, however, rely on the simulation of heat transport in the subsurface, a task that is computationally demanding and particularly prohibitive for multi-query applications such as sensitivity analysis and optimization. Deep learning (DL) has recently emerged as a [...]
Spectrally structured CNN encoding for interpretable and edge-ready fractional vegetation cover mapping using UAS multispectral and imaging spectroscopy
Published: 2026-06-30
Subjects: Artificial Intelligence and Robotics, Biogeochemistry, Computer Sciences, Earth Sciences, Engineering, Environmental Monitoring, Environmental Sciences, Natural Resources and Conservation, Physical Sciences and Mathematics, Remote Sensing
Fractional vegetation cover (FVC) is a key indicator of semi-arid ecosystem condition, but non-photosynthetic vegetation (NPV) remains difficult to map because dry or senescent vegetation, litter, woody debris, and standing dead material can overlap spectrally with photosynthetic vegetation (PV) and bare ground (BE), especially where shadows, exposed bare earth surfaces, and mixed vegetation-soil [...]
Two decades of kilometer-scale daily PM2.5 from satellite observations and machine learning reveal geographically diverging exposure in Ghana
Published: 2026-06-05
Subjects: Artificial Intelligence and Robotics, Atmospheric Sciences, Civil and Environmental Engineering, Earth Sciences, Engineering, Environmental Engineering, Environmental Monitoring, Environmental Public Health, Environmental Sciences, Geographic Information Sciences, Geography, Oceanography and Atmospheric Sciences and Meteorology, Other Earth Sciences, Remote Sensing, Spatial Science
Exposure to fine particulate matter (PM2.5) is a major contributor to global burden of disease, yet air quality data remain sparse in many low- and middle-income countries, limiting nationwide monitoring and effective policy development. We address this gap by developing a high-resolution gridded (1 km × 1 km) dataset for daily surface PM2.5 concentrations in Ghana from 2005 to 2025 by training [...]
Engineering AI-Assisted Client-Side Scientific Workflows: WebGPU Inference Architecture and Framework for Privacy-Preserving Hydrological Analysis
Published: 2026-05-30
Subjects: Artificial Intelligence and Robotics, Environmental Sciences, Hydrology, Software Engineering
Deep learning has demonstrated strong potential for improving hydrological predictions, yet its practical adoption remains limited by software complexity, infrastructure requirements, data governance constraints, and fragmented analytical workflows. This study presents Hydro AI Lab, an AI-assisted client-side scientific workflow platform that enables end-to-end hydrological analysis, including [...]
A benchmark deep learning dataset for the classification of supraglacial lake drainage mechanism across the central-west Greenland Ice Sheet
Published: 2026-05-29
Subjects: Artificial Intelligence and Robotics, Earth Sciences, Glaciology, Numerical Analysis and Scientific Computing, Physical Sciences and Mathematics
Supraglacial lakes on the Greenland Ice Sheet drain through physically distinct pathways: hydrofracture, moulins, lateral stream routing, and crevasse-fields. Each drainage mechanism carries unique implications for ice sheet dynamics. Existing automated classifications reduce each lake's drainage behavior to a time-series of scalar values representing the observed water surface-area and classify [...]
CryoSentinel: A Multimodal Foundation-Model Segmenter for Glacial Lakes in High Mountain Asia from Sentinel-1 SAR, Sentinel-2 Optical, and Copernicus DEM Imagery
Published: 2026-05-16
Subjects: Artificial Intelligence and Robotics, Environmental Monitoring, Geomorphology, Glaciology, Hydrology
Glacial-lake outburst floods (GLOFs) are the dominant climate-driven hazard in High Mountain Asia, and reliable lake-extent segmentation is the prerequisite for every downstream early-warning workflow. We present CryoSentinel, a multimodal foundation-model semantic segmenter built on the IBM/ESA TerraMind 1.0 Large encoder (1.1 B parameters) with a UperNet decoder, fine-tuned on 5,614 [...]
A Blueprint for Integrated Climate Intelligence
Published: 2026-05-13
Subjects: Artificial Intelligence and Robotics, Environmental Indicators and Impact Assessment, Environmental Monitoring, Planetary Sciences, Sustainability
Climate information is advancing faster than the decision systems designed to use it. Emergency response operates on timescales of hours, whereas societal adaptation unfolds over decades. Yet climate science, impact assessment and policy remain poorly integrated, limiting coherent action across timescales. We argue that artificial intelligence should be developed not only as a domain-specific [...]
From 2D labels to 3D structure: Scalable label transfer and benchmarking of 3D vegetation models in rangeland ecosystems
Published: 2026-05-02
Subjects: Artificial Intelligence and Robotics, Biogeochemistry, Computer Sciences, Earth Sciences, Engineering, Environmental Monitoring, Environmental Sciences, Natural Resources and Conservation, Physical Sciences and Mathematics, Remote Sensing
Three-dimensional (3D) characterisation of vegetation structure at the level of individual growth forms is critical for understanding ecosystem function and resilience, yet remains challenging in rangelands because vegetation is sparse, low-stature, and structurally heterogeneous. Recent 3D deep-learning models perform strongly in forests, but their transfer beyond closed-canopy benchmarks is [...]