Preprints

Filtering by Subject: Computer and Systems Architecture

A paradigm shift towards decentralized cloud-integrated spatial data infrastructures: Lessons learned and solutions provided for public authorities

Florian Beyer, Patric Brandt, Michael Schmidt, et al.

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

Andrea Thomer, Lindsay Barbieri, Jane Wyngaard, et al.

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.

Daniel David Buscombe

Published: 2018-06-19
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 [...]

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