{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,15]],"date-time":"2026-02-15T03:25:39Z","timestamp":1771125939368,"version":"3.50.1"},"reference-count":41,"publisher":"MDPI AG","issue":"15","license":[{"start":{"date-parts":[[2022,8,8]],"date-time":"2022-08-08T00:00:00Z","timestamp":1659916800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100000691","name":"Academy of Medical Sciences","doi-asserted-by":"publisher","award":["GCRFNGR4_1165"],"award-info":[{"award-number":["GCRFNGR4_1165"]}],"id":[{"id":"10.13039\/501100000691","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100000691","name":"Academy of Medical Sciences","doi-asserted-by":"publisher","award":["UUFRIP-10021"],"award-info":[{"award-number":["UUFRIP-10021"]}],"id":[{"id":"10.13039\/501100000691","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Royal Academic of Engineering","award":["GCRFNGR4_1165"],"award-info":[{"award-number":["GCRFNGR4_1165"]}]},{"name":"Royal Academic of Engineering","award":["UUFRIP-10021"],"award-info":[{"award-number":["UUFRIP-10021"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>This paper presents the development and applications of a new, open-source toolbox that aims to provide automatic identification and classification of hydroclimatic patterns by their spatial features, i.e., location, size, orientation, and shape, as well as the physical features, i.e., the areal average, total volume, and spatial distribution. The highlights of this toolbox are: (1) incorporating an efficient algorithm for automatically identifying and classifying the spatial features that are linked to hydroclimatic extremes; (2) use as a frontend for supporting AI-based training in tracking and forecasting extremes; and (3) direct support for short-term nowcasting of extreme rainfall via tracking rainstorm centres and movement. The key design and implementation of the toolbox are discussed alongside three case studies demonstrating the application of the toolbox and its potential in helping build machine learning applications in hydroclimatic sciences. Finally, the availability of the toolbox and its source code is included.<\/jats:p>","DOI":"10.3390\/rs14153823","type":"journal-article","created":{"date-parts":[[2022,8,9]],"date-time":"2022-08-09T04:16:55Z","timestamp":1660018615000},"page":"3823","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["A Spatial Pattern Extraction and Recognition Toolbox Supporting Machine Learning Applications on Large Hydroclimatic Datasets"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3018-4707","authenticated-orcid":false,"given":"Han","family":"Wang","sequence":"first","affiliation":[{"name":"China Institute of Water Resources and Hydropower Research, Beijing 100038, China"},{"name":"Research Center on Flood & Drought Disaster Reduction of the Ministry of Water Resources, Beijing 100038, China"},{"name":"Department of Civil Engineering, Swansea University Bay Campus, Fabian Way, Swansea SA1 8EN, UK"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2736-8625","authenticated-orcid":false,"given":"Yunqing","family":"Xuan","sequence":"additional","affiliation":[{"name":"Department of Civil Engineering, Swansea University Bay Campus, Fabian Way, Swansea SA1 8EN, UK"}]}],"member":"1968","published-online":{"date-parts":[[2022,8,8]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"48","DOI":"10.1016\/j.jhydrol.2005.02.006","article-title":"Identification of flood producing atmospheric circulation patterns","volume":"313","author":"Filiz","year":"2005","journal-title":"J. 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