Preprints
Filtering by Subject: Computer and Systems Architecture
ml4xcube: Machine Learning Toolkits for Earth System Data Cubes
Published: 2024-10-09
Subjects: Computer and Systems Architecture, Computer Engineering, Engineering, Geography, Remote Sensing, Social and Behavioral Sciences
Rapidly changing climate conditions and the increase in extreme events are posing severe challenges to human life and infrastructure, requiring sophisticated analytical capabilities for hazard prediction and disaster risk management. Earth System Data Cubes (ESDCs) have become an essential tool in Earth System Sciences (ESS) by organizing large-scale, multivariate environmental datasets into 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 [...]
UMIS: An Integrated Cyberinfrastructure System for Water Quality Resources in the Upper Mississippi River Basin
Published: 2023-11-28
Subjects: Computer and Systems Architecture, Databases and Information Systems, Engineering Education, Environmental Education, Environmental Engineering, Hydrology, Natural Resources Management and Policy, Systems and Communications, Water Resource Management
The Upper Mississippi Information System (UMIS) is a cyberinfrastructure framework designed to support large-scale real-time water quality data integration, analysis, and visualization for the Upper Mississippi River Basin (UMRB). UMIS is intended to directly address three of the Grand Challenges for Engineering including: 1) understanding access to clean drinking water, 2) management of the [...]
A paradigm shift towards decentralized cloud-integrated spatial data infrastructures: Lessons learned and solutions provided for public authorities
Published: 2023-06-08
Subjects: Agriculture, Computer and Systems Architecture, Numerical Analysis and Scientific Computing, Sustainability
Digital transformation is a key to turn public authorities into organisations that make decisions based on data-driven insights. The use of big geodata can enable public authorities to tackle complex sustainability issues. However, the efficient management of large amounts of geodata through implementing viable data infrastructures represents a major challenge for public authorities. In this [...]
Making Drone Data FAIR Through a Community-Developed Information Framework
Published: 2021-08-02
Subjects: Computer and Systems Architecture, Library and Information Science
Small Uncrewed Aircraft Systems (sUAS) are an increasingly common tool for data collection in many scientific fields. However, there are few standards or best practices guiding the collection, sharing, or publication of data collected with these tools. This makes collaboration, data quality control, and reproducibility challenging. To that end, we have used iterative rounds of data modeling and [...]
Landscape classification with deep neural networks.
Published: 2018-06-18
Subjects: Computer and Systems Architecture, Computer Engineering, Earth Sciences, Engineering, Environmental Monitoring, Environmental Sciences, Geology, Geomorphology, Other Statistics and Probability, Physical Sciences and Mathematics, Statistics and Probability
The application of deep learning, specifically deep convolutional neural networks (DCNNs), to the classification of remotely sensed imagery of natural landscapes has the potential to greatly assist in the analysis and interpretation of geomorphic processes. However, the general usefulness of deep learning applied to conventional photographic imagery at a landscape scale is, at yet, largely [...]