UDASH-AI: Unified Database for Arctic and Subarctic Hydrography Optimized for Artificial Intelligence Applications ...

• Main Authors: Chouai, Mohamed, Mieruch-Schnülle, Sebastian, Behrendt, Axel, Vredenborg, Myriel, Rabe, Benjamin
• Format: Dataset
• Language: unknown
• Published: PANGAEA 2024
• Subjects: AI Applications; Arctic; Artificial Intelligence; hydrography; Quality Assessment; subarctic; UDASH; Subarctic
• Online Access: https://dx.doi.org/10.1594/pangaea.973235; https://doi.pangaea.de/10.1594/PANGAEA.973235

UDASH-AI represents an updated version of the UDASH dataset, that has been created to develop an artificial intelligence algorithm, that we name SalaciaML-Arctic to support the visual/human quality control of the data. UDASH-AI can be directly used with our algorithm, provided under the DOI: https://doi.org/10.5281/zenodo.11535790 and the respective GitHub repository, to reproduce our results, extend the methods and more. Additionally, we have implemented SalaciaML-Arctic as an user-friendly app at https://mvre.autoqc.cloud.awi.de. Following steps have been applied on the original UDASH dataset to create UDASH-AI:• Concatenation of the single, annual txt files into one single csv file.• The original encoding of missing time and day information in the date/time string as 'T99:99' and '-00T' have been changed to ISO8601 conformity: 'T00:00' and '-01T'. To not loose this information we have added a quality flag ('QF_time') in the column next to the date/time with following encoding:◦ 0: No missing data (good ...