{"title":"利用岸基斜向摄影进行河冰探测和分类","authors":"","doi":"10.1016/j.coldregions.2024.104303","DOIUrl":null,"url":null,"abstract":"<div><div>River ice processes significantly impact various aspects of river systems, such as hydraulics, sediment transport, water quality, and morphology. Therefore, understanding these processes is essential for cold-region river studies, ship navigation, and forecasting ice-induced hazards. Remote sensing and close-range photogrammetry have gained attention in recent years, thanks to the growing accessibility of affordable photogrammetry devices and advances in computer vision. Despite progress, acquiring fast, accurate, and long-term data remains challenging. This study presents a novel application of IceMaskNet, a river ice detection, segmentation, and quantification algorithm, specifically designed for oblique shore-based imagery. Built on an enhanced version of the instance segmentation algorithm, Mask R-CNN, IceMaskNet for oblique shore-based imagery was trained using 1795 manually annotated images of the Dauphin River. The algorithm demonstrates high accuracy in detecting and segmenting various river ice categories, achieving 90 % detection accuracy and 86 % segmentation masking accuracy. The developed algorithm was applied over a set of four years of oblique shore-based imagery along the Dauphin River. The algorithm was used in a case study to efficiently generate quantitative estimate of different ice classes in a section of the Dauphin river from long-term shore-based monitoring, significantly contributing to our understanding of river ice processes. The study shows the complex nature of river ice processes in the Dauphin River, and highlights the influence of factors such as air temperature, river flow, flow velocity, and river hydrodynamic characteristics.</div></div>","PeriodicalId":10522,"journal":{"name":"Cold Regions Science and Technology","volume":null,"pages":null},"PeriodicalIF":3.8000,"publicationDate":"2024-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"River Ice Detection and Classification using Oblique Shore-based Photography\",\"authors\":\"\",\"doi\":\"10.1016/j.coldregions.2024.104303\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>River ice processes significantly impact various aspects of river systems, such as hydraulics, sediment transport, water quality, and morphology. Therefore, understanding these processes is essential for cold-region river studies, ship navigation, and forecasting ice-induced hazards. Remote sensing and close-range photogrammetry have gained attention in recent years, thanks to the growing accessibility of affordable photogrammetry devices and advances in computer vision. Despite progress, acquiring fast, accurate, and long-term data remains challenging. This study presents a novel application of IceMaskNet, a river ice detection, segmentation, and quantification algorithm, specifically designed for oblique shore-based imagery. Built on an enhanced version of the instance segmentation algorithm, Mask R-CNN, IceMaskNet for oblique shore-based imagery was trained using 1795 manually annotated images of the Dauphin River. The algorithm demonstrates high accuracy in detecting and segmenting various river ice categories, achieving 90 % detection accuracy and 86 % segmentation masking accuracy. The developed algorithm was applied over a set of four years of oblique shore-based imagery along the Dauphin River. The algorithm was used in a case study to efficiently generate quantitative estimate of different ice classes in a section of the Dauphin river from long-term shore-based monitoring, significantly contributing to our understanding of river ice processes. The study shows the complex nature of river ice processes in the Dauphin River, and highlights the influence of factors such as air temperature, river flow, flow velocity, and river hydrodynamic characteristics.</div></div>\",\"PeriodicalId\":10522,\"journal\":{\"name\":\"Cold Regions Science and Technology\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":3.8000,\"publicationDate\":\"2024-09-06\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Cold Regions Science and Technology\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0165232X24001848\",\"RegionNum\":2,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"ENGINEERING, CIVIL\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Cold Regions Science and Technology","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0165232X24001848","RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, CIVIL","Score":null,"Total":0}
River Ice Detection and Classification using Oblique Shore-based Photography
River ice processes significantly impact various aspects of river systems, such as hydraulics, sediment transport, water quality, and morphology. Therefore, understanding these processes is essential for cold-region river studies, ship navigation, and forecasting ice-induced hazards. Remote sensing and close-range photogrammetry have gained attention in recent years, thanks to the growing accessibility of affordable photogrammetry devices and advances in computer vision. Despite progress, acquiring fast, accurate, and long-term data remains challenging. This study presents a novel application of IceMaskNet, a river ice detection, segmentation, and quantification algorithm, specifically designed for oblique shore-based imagery. Built on an enhanced version of the instance segmentation algorithm, Mask R-CNN, IceMaskNet for oblique shore-based imagery was trained using 1795 manually annotated images of the Dauphin River. The algorithm demonstrates high accuracy in detecting and segmenting various river ice categories, achieving 90 % detection accuracy and 86 % segmentation masking accuracy. The developed algorithm was applied over a set of four years of oblique shore-based imagery along the Dauphin River. The algorithm was used in a case study to efficiently generate quantitative estimate of different ice classes in a section of the Dauphin river from long-term shore-based monitoring, significantly contributing to our understanding of river ice processes. The study shows the complex nature of river ice processes in the Dauphin River, and highlights the influence of factors such as air temperature, river flow, flow velocity, and river hydrodynamic characteristics.
期刊介绍:
Cold Regions Science and Technology is an international journal dealing with the science and technical problems of cold environments in both the polar regions and more temperate locations. It includes fundamental aspects of cryospheric sciences which have applications for cold regions problems as well as engineering topics which relate to the cryosphere.
Emphasis is given to applied science with broad coverage of the physical and mechanical aspects of ice (including glaciers and sea ice), snow and snow avalanches, ice-water systems, ice-bonded soils and permafrost.
Relevant aspects of Earth science, materials science, offshore and river ice engineering are also of primary interest. These include icing of ships and structures as well as trafficability in cold environments. Technological advances for cold regions in research, development, and engineering practice are relevant to the journal. Theoretical papers must include a detailed discussion of the potential application of the theory to address cold regions problems. The journal serves a wide range of specialists, providing a medium for interdisciplinary communication and a convenient source of reference.