Scivision and EDS book: making computer vision and data science more accessible for Environmental scientists

• Main Authors: Alejandro Coca-Castro, Alden Conner
• Format: Lecture
• Language: unknown
• Published: Zenodo 2023
• Subjects: scientific image analysis; computer vision; environmental data science; jupyter notebooks; digital communities; Antarc*; Antarctic; British Antarctic Survey
• Online Access: https://doi.org/10.5281/zenodo.8139015

This presentation was given to NERC’s Digital Gathering 23, https://digitalenvironment.org/events/dg23-digital-gathering-2023 , on Monday 10th July 2023. Further details of the complete list of authors and abstract below. Scivision authors: Coca Castro, A. (1), Conner, A. (1), Corcoran, E. (1), Costa Gomes, B., Fenton, I. (1), Famili, M. (1), Mehonic, A. (1), Strickson, O. (1), Van Zeeland, L. (1), Anhert, S. (1, 2), Lowe, A. (1, 3), Hosking, J. S (1, 4) EDS book authors: Coca Castro, A. (1), Hosking, J. S (1, 4), EDS book community (5) Affiliations (1 – The Alan Turing Institute, 2 – University College London, 3 – University of Cambridge, 4 – British Antarctic Survey, 5 – Multiple) Supported by interdisciplinary collaborations between teams from environmental, statistics and computer sciences, the past decade has seen accelerated development of environmental data, models and pipelines. This talk will highlight how two community-driven initiatives, created and maintained by the Alan Turing Institute, make research products in environmental science more accessible and discoverable. Scivision ( https://sci.vision ) is an open-source software tool, an open catalogue of datasets and models, and a community of computer vision experts and users. Scivision aims to accelerate scientific computer vision by sharing and matching models and datasets through the Scivision catalogue. The models in the catalogue have a common interface and are designed to be installable and runnable by someone without a computer science background; the datasets indicate their domain of application and any tasks that they may be suitable for, so that they are discoverable by computer vision model developers. Scivision has been applied to environmental use cases to analyse image datasets across different scales and formats including tree crown detection from drone imagery, coastal vegetation edge detection from satellite imagery, automated extraction of plant phenotype data from multiple 2D views of whole plants, among others. Scivision has also ...