Preprints
Filtering by Subject: Computer Sciences
Application of machine learning methods to forecast petrophysical properties in basalts of the Serra Geral Group: Implications for carbon storage
Published: 2024-10-22
Subjects: Artificial Intelligence and Robotics, Computer Sciences, Earth Sciences, Environmental Sciences, Geophysics and Seismology, Oil, Gas, and Energy, Physical Sciences and Mathematics
This study applies machine learning techniques for forecasting petrophysical properties (density, porosity, and permeability) in the basalts of the Serra Geral Group, located in the Paraná Basin, Brazil. These properties are crucial for the successful implementation of carbon capture and storage (CCS), an important technology to combat climate change. Employing machine learning models—XGBoost, [...]
DARTS: Multi-year database of AI-detected retrogressive thaw slumps in the circum-arctic permafrost region
Published: 2024-10-20
Subjects: Artificial Intelligence and Robotics, Computer Sciences, Earth Sciences, Geomorphology, Physical Sciences and Mathematics
Retrogressive Thaw Slumps (RTS) and Active Layer Detachment Slides (ALD) are widespread thermal mass-wasting hillslope failures triggered by thawing permafrost. Despite increasing rates of these failures, knowledge about their pan-arctic spatial and temporal distribution remains limited. We present the Database of AI-detected Arctic RTS and ALD footprints (DARTS), the largest hillslope [...]
Virtual laser scanning of dynamic scenes (VLS-4D): Current approaches and future perspectives in remote sensing
Published: 2024-10-07
Subjects: Computer Sciences, Earth Sciences, Environmental Sciences, Geographic Information Sciences, Geography, Numerical Analysis and Scientific Computing, Physical and Environmental Geography, Remote Sensing
Virtual laser scanning (VLS) has proven to be a useful tool for survey planning, method development and training data generation in a variety of areas of Earth and environmental sciences. Until recently, most applications have used static representations of the real or a fictive environment, neglecting the inherent dynamics of our world, that also affect Light Detection and Ranging (LiDAR) [...]
Climate Suitability Modelling of Miracle Tree Moringa oleifera Distribution in Pakistan using MaxEnt
Published: 2024-09-25
Subjects: Computer Sciences
Climate change has badly affected many countries in the world and Pakistan is being listed among the top ten of those countries. Pakistan is facing many adverse consequences due to climate change, which includes food security issues, water scarcity, temperature rise and high air pollution index. Moringa oleifera, known to be the miracle tree, has multiple advantages and can be used to combat [...]
Comparative Analysis of SVM and CNN for Hyperspectral Image Classification
Published: 2024-08-13
Subjects: Computer Engineering, Computer Sciences, Other Computer Sciences, Physical Sciences and Mathematics
This paper presents a comparative analysis of tra- ditional machine learning methods and Convolutional Neural Networks (CNNs) for hyperspectral image classification. Utilizing the Indian Pines dataset, we explore the efficacy of Principal Component Analysis (PCA) combined with a Support Vector Machine (SVM) classifier against a deep learning approach involving CNNs. Our methodology includes [...]
TROPICAL STORM SURGE: FORMATION, IMPACT, AND RECENT ADVANCES IN ITS PREDICTION TOWARDS DEVELOPING MITIGATION STRATEGIES
Published: 2024-07-19
Subjects: Computer Sciences, Earth Sciences, Environmental Sciences, Oceanography and Atmospheric Sciences and Meteorology, Other Physical Sciences and Mathematics, Physics, Planetary Sciences
Tropical storm surge poses significant risks to coastal areas, necessitating precise prediction for effective emergency preparedness and mitigation. Recent advances in numerical models such as SLOSH, ADCIRC, and FVCOM have revolutionized storm surge forecasting by accurately simulating complex hydrodynamic processes, bolstered by ADCIRC's use of high-resolution grids and parallel computing for [...]
A Review of Machine Learning in Snow Water Equivalent Monitoring
Published: 2024-05-10
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 [...]
GeoAI and the Future of Spatial Analytics
Published: 2024-05-02
Subjects: Computer Sciences, Earth Sciences, Environmental Sciences, Environmental Studies, Geography, Library and Information Science, Physical Sciences and Mathematics, Social and Behavioral Sciences
This chapter discusses the challenges of traditional spatial analytical methods in their limited capacity to handle big and messy data, as well as mining unknown or latent patterns. It then introduces a new form of spatial analytics – geospatial artificial intelligence (GeoAI) - and describes the advantages of this new strategy in big data analytics and data-driven discovery. Finally, a [...]
Towards an Open and Integrated Cyberinfrastructure for River Morphology Research in the Big Data Era
Published: 2024-02-16
Subjects: Computer and Systems Architecture, Computer Engineering, Computer Sciences, Databases and Information Systems, Engineering, Environmental Education, Environmental Engineering, Geomorphology, Hydrology, Systems and Communications, Water Resource Management
The objective of this paper is to present the initial illustration of a cyberinfrastructure named the River MORPhology Information System (RIMORPHIS) that addresses the current limitations related to river morphology data and tools. A new specification for data and semantics on river morphology datasets has been developed to support the web-based platform for discovering and visualization of [...]
Unsupervised Structural Damage Assessment from Space using the Segment Anything Model (USDA-SAM): A Case Study of the 2023 Türkiye Earthquake
Published: 2024-01-24
Subjects: Computer Sciences, Earth Sciences, Engineering, Environmental Sciences
This paper explores advanced deep learning methods, specifically utilising the Segment Anything Model (SAM) along with image processing techniques, to evaluate the structural damages caused by the devastating earthquake that occurred in Turkey on February 6, 2023. Leveraging exceptionally high-resolution pre- and post-disaster imagery provided by Maxar Technologies, this paper showcases the [...]
Deep learning with simulated laser scanning data for 3D point cloud classification
Published: 2024-01-17
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 [...]
Wealth over Woe: global biases in hydro-hazard research
Published: 2024-01-13
Subjects: Computer Sciences, Hydrology, Nature and Society Relations, Sustainability
Floods, droughts, and rainfall-induced landslides are hydro-hazards that affect millions of people every year. Anticipation, mitigation, and adaptation to these hazards is increasingly outpaced by their changing magnitude and frequency due to climate change. A key question for society is whether the research we pursue has the potential to address knowledge gaps and to reduce potential future [...]
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., [...]
Pygoda: a graphical interface to efficiently visualise and explore large sets of geolocated time series
Published: 2023-10-24
Subjects: Categorical Data Analysis, Climate, Computer Sciences, Databases and Information Systems, Earth Sciences, Environmental Monitoring, Environmental Sciences, Environmental Studies, Fresh Water Studies, Geographic Information Sciences, Geophysics and Seismology, Glaciology, Graphics and Human Computer Interfaces, Hydrology, Longitudinal Data Analysis and Time Series, Meteorology, Other Earth Sciences, Other Statistics and Probability, Spatial Science
Modern-day data sets in geosciences may comprise hundreds or thousands of geolocated time series. Despite all the automated tools and new algorithms now available to process and prepare those data before using them in research projects, it can be useful or even necessary to visualise and investigate them manually. Whether it be for data quality assessment, for the preparation of a training data [...]
A Deep Learning framework to map riverbed sand mining budgets in large tropical deltas
Published: 2023-10-20
Subjects: Computer Sciences, Earth Sciences, Environmental Education, Environmental Health and Protection, Environmental Monitoring, Environmental Sciences, Natural Resource Economics, Natural Resources and Conservation, Natural Resources Management and Policy, Other Environmental Sciences, Planetary Geomorphology, Planetary Sedimentology, Sustainability, Water Resource Management
Rapid urbanization has dramatically increased the demand for river sand, leading to soaring sand extraction rates that often exceed natural replenishment in many rivers globally. However, our understanding of the geomorphic and social-ecological impacts arising from Sand Mining (SM) remains limited, primarily due to insufficient data on sand extraction rates. Conventionally, bathymetry surveys [...]