Preprints
Filtering by Subject: Artificial Intelligence and Robotics
MicroCrystalNet: An Efficient Convolutional Neural Network for Microcrystal Classification using Scanning Electron Microscope Petrography
Published: 2024-08-22
Subjects: Artificial Intelligence and Robotics, Engineering, Other Engineering, Other Environmental Sciences
Morphological characterization of microcrystalline rock textures typically relies upon the visual interpretation and manual measurement of scanning electron microscopy (SEM) imagery: a practice fraught with subjectivity, inefficiency, sampling bias, and data loss. We introduce a state-of-the-art computer vision pipeline, built on deep learning architectures, for segmenting and classifying [...]
Implementation of the Peruvian Earthquake Early Warning System
Published: 2024-06-12
Subjects: Artificial Intelligence and Robotics, Geophysics and Seismology
We present the implementation and testing of the seismological components of the Peruvian Earthquake Early Warning System (Sistema de Alerta Sísmica Peruano, SASPe). SASPe is designed to send alert messages to areas located within a given distance from the epicenter of large (magnitude ≥ 6.0) subduction earthquakes, with a first alert based on data available 3 seconds after the arrival [...]
Integrating Conversational AI Agents for Enhanced Water Quality Analytics: Development of a Novel Data Expert System
Published: 2024-06-01
Subjects: Artificial Intelligence and Robotics, Civil Engineering, Databases and Information Systems, Environmental Engineering, Hydrology, Software Engineering
Despite advancements in environmental monitoring, the gap between data collection and user-friendly data interpretation remains a significant challenge, especially in the domain of water quality management. This paper introduces the Artificial Intelligence Data Expert (AI-DE), a novel data analytics system that is designed to facilitate on-demand analysis of time-series sensor data related to [...]
A Comprehensive Evaluation of Multimodal Large Language Models in Hydrological Applications
Published: 2024-05-25
Subjects: Artificial Intelligence and Robotics, Environmental Monitoring, Hydrology
Large Language Models (LLMs) combined with visual foundation models have demonstrated remarkable advancements, achieving a level of intelligence comparable to human capabilities. In this study, we conduct an analysis of the latest Multimodal LLMs (MLLMs), specifically Multimodal-GPT, GPT-4 Vision, Gemini and LLaVa, focusing on their application in the hydrology domain. The hydrology domain holds [...]
A Review of Machine Learning in Snow Water Equivalent Monitoring
Published: 2024-05-09
Subjects: Artificial Intelligence and Robotics, Computer Sciences, Databases and Information Systems, Earth Sciences, Hydrology, Numerical Analysis and Scientific Computing, Oceanography and Atmospheric Sciences and Meteorology
In recent years, the scientific community focused on snow dynamics has witnessed a surge in efforts aimed at enhancing Snow Water Equivalent (SWE) monitoring capabilities, largely propelled by the incorporation of Machine Learning (ML) techniques. This comprehensive review delves into the current state of research within this evolving domain, shedding light on the indispensable role of precise [...]
Interpretable Quality Control of Sparsely Distributed Environmental Sensor Networks Using Graph Neural Networks
Published: 2024-05-02
Subjects: Artificial Intelligence and Robotics, Earth Sciences
Environmental sensor networks play a crucial role in monitoring key parameters essential for understanding Earth’s systems. To ensure the reliability and accuracy of collected data, effective quality control (QC) measures are essential. Conventional QC methods struggle to handle the complexity of environmental data. Conversely, advanced techniques such as neural networks, are typically not [...]
Sentinel-1 SAR-based Globally Distributed Landslide Detection by Deep Neural Networks
Published: 2024-04-05
Subjects: Artificial Intelligence and Robotics, Geomorphology
Efficient response to large and widespread multiple landslide events (MLEs) demands rapid and effective landslide detection. Despite extensive efforts using optical remotely sensed imagery, limitations in global, day & night, and all-weather operational capabilities remain. To address these gaps, we introduce an approach that harnesses Deep Neural Networks (DNNs) and Synthetic Aperture Radar [...]
An ensemble neural network approach for space-time landslide predictive modelling
Published: 2024-02-27
Subjects: Artificial Intelligence and Robotics, Geomorphology, Multivariate Analysis
There is an urgent need for accurate and effective Landslide Early Warning Systems (LEWS). Most LEWS are currently based on a single temporally-aggregated measure of rainfall derived from either in-situ measurements or satellite-based rainfall estimates. Relying on a summary metric of precipitation may not capture the complexity of the rainfall signal and its dynamics in space and time in [...]
Using computer vision to detect and segment fire behavior classifications in UAS-captured images
Published: 2024-02-21
Subjects: Artificial Intelligence and Robotics, Forest Management, Natural Resources and Conservation
The widely adaptable capabilities of artificial intelligence, in particular deep learning and computer vision has led to significant research output regarding fire and smoke detection. Previous studies often focus on themes like early fire detection, increased operational awareness, and post-fire assessment. To further test the capabilities of deep learning detection in these scenarios, we [...]
Deep learning with simulated laser scanning data for 3D point cloud classification
Published: 2024-01-16
Subjects: Artificial Intelligence and Robotics, Computer Sciences, Earth Sciences, Numerical Analysis and Scientific Computing, Other Earth Sciences
Laser scanning is an active remote sensing technique applied in many disciplines to acquire state-of-the-art spatial measurements. Semantic labeling is often necessary to extract information from the raw point cloud. Deep learning methods constitute a data-hungry solution for the semantic segmentation of point clouds. In this work, we investigate the use of simulated laser scanning for training [...]
Deep Learning Improves Global Satellite Observations of Ocean Eddy Dynamics
Published: 2024-01-15
Subjects: Artificial Intelligence and Robotics, Fluid Dynamics, Oceanography and Atmospheric Sciences and Meteorology
Ocean eddies affect large-scale circulation and induce a kinetic energy cascade through their non-linear interactions. However, since global observations of eddy dynamics come from satellite altimetry maps that smooth eddies and distort their geometry, the strength of this cascade is underestimated. Here, we use deep learning to improve observational estimates of global surface geostrophic [...]
Open-source approach for reproducible substrate mapping using semantic segmentation on recreation-grade side scan sonar datasets
Published: 2023-12-21
Subjects: Analysis, Artificial Intelligence and Robotics, Databases and Information Systems, Environmental Monitoring, Hydrology, Natural Resources and Conservation, Numerical Analysis and Computation, Numerical Analysis and Scientific Computing, Programming Languages and Compilers, Software Engineering, Terrestrial and Aquatic Ecology, Water Resource Management
Knowledge of the variation and distribution of substrates at large spatial extents in aquatic systems, particularly rivers, is severely lacking, impeding species conservation and ecosystem restoration efforts. Air and space-borne remote sensing important for terrestrial and atmospheric measurements are limited in benthic environments due to river stage, turbidity, and canopy cover, requiring [...]
Never train an LSTM on a single basin
Published: 2023-12-05
Subjects: Artificial Intelligence and Robotics, Computer Sciences, Earth Sciences, Hydrology, Physical Sciences and Mathematics
Machine learning (ML) has an increasing role in the hydrological sciences, and in particular, certain types of time series modeling strategies are popular for rainfall-runoff modeling. A large majority of studies that use this type of model do not follow best practices, and there is one mistake in particular that is very common: training deep learning models on small, homogeneous data sets (i.e., [...]
TROPOMI/S5P sensitivity limits with respect to detection of NO2 plumes from seagoing ships
Published: 2023-09-20
Subjects: Artificial Intelligence and Robotics, Atmospheric Sciences, Environmental Monitoring
The marine shipping industry is among the strong emitters of nitrogen oxides (NOx) -- a substance harmful to ecology and human health. Monitoring of emissions from shipping is a significant societal task. Currently, the only technical possibility to observe NO2 emission from seagoing ships on a global scale is using TROPOMI data. A range of studies reported that NO2 plumes from some individual [...]
A deep learning approach for deriving winter wheat phenology from optical and SAR time series at field level
Published: 2023-07-13
Subjects: Agricultural Science, Agriculture, Artificial Intelligence and Robotics, Environmental Monitoring
Information on crop phenology is essential when aiming to better understand the impacts of climate and climate change, management practices, and environmental conditions on agricultural production. Today’s novel optical and radar satellite data with increasing spatial and temporal resolution provide great opportunities to provide such information. However, so far, we largely lack methods that [...]