{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T02:15:52Z","timestamp":1760148952339,"version":"build-2065373602"},"reference-count":61,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2023,6,13]],"date-time":"2023-06-13T00:00:00Z","timestamp":1686614400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100000038","name":"\u201cLassonde School of Engineering Strategic Research Priority Plan\u201d and \u201cLassonde School of Engineering Innovation Fund\u201d, York University, Canada, and \u201cNatural Sciences and Engineering Research Council of Canada\u2014NSERC","doi-asserted-by":"publisher","award":["RGPIN 2015-04292","RGPIN 2020-07144"],"award-info":[{"award-number":["RGPIN 2015-04292","RGPIN 2020-07144"]}],"id":[{"id":"10.13039\/501100000038","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Determining the height of plume clouds is crucial for various applications, including global climate models. Smokestack plume rise refers to the altitude at which the plume cloud travels downwind until its momentum dissipates and the temperatures of the plume cloud and its surroundings become equal. While most air-quality models employ different parameterizations to forecast plume rise, they have not been effective in accurately estimating it. This paper introduces a novel framework that utilizes Deep Convolutional Neural Networks (DCNNs) to monitor smokestack plume clouds and make real-time, long-term measurements of plume rise. The framework comprises three stages. In the first stage, the plume cloud is identified using an enhanced Mask R-CNN, known as the Deep Plume Rise Network (DPRNet). Next, image processing analysis and least squares theory are applied to determine the plume cloud\u2019s boundaries and fit an asymptotic model to its centerlines. The z-coordinate of the critical point of this model represents the plume rise. Finally, a geometric transformation phase converts image measurements into real-world values. This study\u2019s findings indicate that the DPRNet outperforms conventional smoke border detection and recognition networks. In quantitative terms, the proposed approach yielded a 22% enhancement in the F1 score, compared to its closest competitor, DeepLabv3.<\/jats:p>","DOI":"10.3390\/rs15123083","type":"journal-article","created":{"date-parts":[[2023,6,13]],"date-time":"2023-06-13T02:56:34Z","timestamp":1686624994000},"page":"3083","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Deep Convolutional Neural Network for Plume Rise Measurements in Industrial Environments"],"prefix":"10.3390","volume":"15","author":[{"given":"Mohammad","family":"Koushafar","sequence":"first","affiliation":[{"name":"Department of Earth and Space Science and Engineering, York University, 4700 Keele Street, Toronto, ON M3J 1P3, Canada"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5127-8358","authenticated-orcid":false,"given":"Gunho","family":"Sohn","sequence":"additional","affiliation":[{"name":"Department of Earth and Space Science and Engineering, York University, 4700 Keele Street, Toronto, ON M3J 1P3, Canada"}]},{"given":"Mark","family":"Gordon","sequence":"additional","affiliation":[{"name":"Department of Earth and Space Science and Engineering, York University, 4700 Keele Street, Toronto, ON M3J 1P3, Canada"}]}],"member":"1968","published-online":{"date-parts":[[2023,6,13]]},"reference":[{"key":"ref_1","unstructured":"Briggs, G.A. 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