Filtering by Subject: Artificial Intelligence and Robotics

A Comprehensive Evaluation of Multimodal Large Language Models in Hydrological Applications

Likith Kadiyala, Omer Mermer, Dinesh Jackson Samuel, et al.

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

Faye Hsu, Ziheng Sun, Gokul Prathin, et al.

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

Elżbieta Krystyna Lasota, Timo Houben, Julius Polz, et al.

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

Lorenzo Nava, Alessandro Cesare Mondini, Kushanav Bhuyan, et al.

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

Jana Lim, Giorgio Santinelli, Ashok Dahal, et al.

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

Brett Lawrence, Emerson de Lemmus

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

Alberto M. Esmorís, Hannah Weiser, Lukas Winiwarter, et al.

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

Scott A Martin, Georgy Manucharyan, Patrice Klein

Published: 2024-01-15
Subjects: Artificial Intelligence and Robotics, Fluid Dynamics, Oceanography and Atmospheric Sciences and Meteorology

Ocean eddies affect large-scale circulation and transfer energy between scales through non-linear eddy interactions. This eddy-induced kinetic cascade depends on the strain rate, which is strongly sensitive to the precise geometry and configuration of eddies. However, surface currents estimated globally from altimetry smooth and distort eddies, severely underestimating the strength of non-linear [...]

Open-source approach for reproducible substrate mapping using semantic segmentation on recreation-grade side scan sonar datasets

Cameron Scott Bodine, Daniel David Buscombe, Toby D. Hocking

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

Frederik Kratzert, Martin Gauch, Daniel Klotz, et al.

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

Solomiia Kurchaba, Artur Sokolovsky, Jasper van Vliet, et al.

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

Felix Lobert, Johannes Löw, Marcel Schwieder, et al.

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 [...]

An Artificial Neural Network to Estimate the Foliar and Ground Cover Input Variables of the Rangeland Hydrology and Erosion Model

Mahmoud Saeedimoghaddam, Grey Nearing, David C. Goodrich, et al.

Published: 2023-07-12
Subjects: Artificial Intelligence and Robotics, Environmental Monitoring, Hydrology, Natural Resources and Conservation, Natural Resources Management and Policy, Soil Science

Models like the Rangeland Hydrology and Erosion Model (RHEM) are useful for estimating soil erosion, however, they rely on input parameters that are sometimes difficult or expensive to measure. Specifically, RHEM requires information about foliar and ground cover fractions that generally must be measured in situ, which makes it difficult to use models like RHEM to produce erosion or soil risk [...]

Mitigation effectiveness on groundwater-dependent ecosystems revealed by counterfactual AI

Debaditya Chakraborty, Chetan Sharma, Hakan Basagaoglu, et al.

Published: 2023-07-09
Subjects: Artificial Intelligence and Robotics, Environmental Engineering, Hydrology, Sustainability, Water Resource Management

Overexploitation of groundwater threatens groundwater-bound aquatic and terrestrial biodiversity and ecosystem stability, underscoring the need to devise appropriate mitigation strategies. Yet, substantial scientific evidence that mitigation measures effectively protect groundwater ecosystems is presently nonexistent. We provide unique and compelling evidence, using counterfactual artificial [...]

Ten deep learning techniques to address small data problems with remote sensing

Anastasiia Safonova, Gohar Ghazaryan, Stefan Stiller, et al.

Published: 2023-06-09
Subjects: Artificial Intelligence and Robotics, Other Earth Sciences

Researchers and engineers have increasingly used Deep Learning (DL) for a variety of Remote Sensing (RS) tasks. However, data from local observations or via ground truth is often quite limited for training DL models, especially when these models represent key socio-environmental problems, such as the monitoring of extreme, destructive climate events, biodiversity, and sudden changes in ecosystem [...]


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