{"title":"Detection of ringforts from aerial photography using machine learning","authors":"Keith Phelan, D. Riordan","doi":"10.1109/ISSC49989.2020.9180159","DOIUrl":null,"url":null,"abstract":"Ringforts are one of the most populous field monuments in Ireland with approximately 45000 examples surviving to date. Their distribution and dispersal patterns are key to our understanding of the habitation patterns of our ancestors. Due to the nature of these structures and the construction materials used, centuries of abandonment means that they often go unnoticed at ground level, while being easily identified from an aerial perspective. The increased requirements of land use for the development of urban areas, infrastructure and increased industrialised farming practices means that these monuments are under threat. Recent developments in the field of machine learning coupled with access to hi-resolution multi-spectral satellite imagery from Open Data sources, presents the opportunity to investigate the development of a system for the automated detection of these features. If successful, such a system could provide an automated, efficient and cost effective tool for the detection of interference or destruction of known sites as well as the discovery of new ones.","PeriodicalId":351013,"journal":{"name":"2020 31st Irish Signals and Systems Conference (ISSC)","volume":"39 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2020 31st Irish Signals and Systems Conference (ISSC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ISSC49989.2020.9180159","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 4
Abstract
Ringforts are one of the most populous field monuments in Ireland with approximately 45000 examples surviving to date. Their distribution and dispersal patterns are key to our understanding of the habitation patterns of our ancestors. Due to the nature of these structures and the construction materials used, centuries of abandonment means that they often go unnoticed at ground level, while being easily identified from an aerial perspective. The increased requirements of land use for the development of urban areas, infrastructure and increased industrialised farming practices means that these monuments are under threat. Recent developments in the field of machine learning coupled with access to hi-resolution multi-spectral satellite imagery from Open Data sources, presents the opportunity to investigate the development of a system for the automated detection of these features. If successful, such a system could provide an automated, efficient and cost effective tool for the detection of interference or destruction of known sites as well as the discovery of new ones.