{"title":"GeoAI-未来就在这里!","authors":"M. Chandler","doi":"10.15353/acmla.n172.5458","DOIUrl":null,"url":null,"abstract":"Artificial Intelligence (AI) in the geospatial data sphere has been around for some time - albeit under different monikers, including \"deep learning\" and \"machine learning\". Both commercial and open-source software have options for the current brand of AI, and these are discussed. Some machine learning training models are also openly available for use. Whether any of this will be relevant tomorrow is given cursory consideration.","PeriodicalId":35718,"journal":{"name":"Association of Canadian Map Libraries and Archives Bulletin","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2023-08-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"GeoAI- The future was here!\",\"authors\":\"M. Chandler\",\"doi\":\"10.15353/acmla.n172.5458\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Artificial Intelligence (AI) in the geospatial data sphere has been around for some time - albeit under different monikers, including \\\"deep learning\\\" and \\\"machine learning\\\". Both commercial and open-source software have options for the current brand of AI, and these are discussed. Some machine learning training models are also openly available for use. Whether any of this will be relevant tomorrow is given cursory consideration.\",\"PeriodicalId\":35718,\"journal\":{\"name\":\"Association of Canadian Map Libraries and Archives Bulletin\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2023-08-03\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Association of Canadian Map Libraries and Archives Bulletin\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.15353/acmla.n172.5458\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q4\",\"JCRName\":\"Earth and Planetary Sciences\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Association of Canadian Map Libraries and Archives Bulletin","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.15353/acmla.n172.5458","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"Earth and Planetary Sciences","Score":null,"Total":0}
Artificial Intelligence (AI) in the geospatial data sphere has been around for some time - albeit under different monikers, including "deep learning" and "machine learning". Both commercial and open-source software have options for the current brand of AI, and these are discussed. Some machine learning training models are also openly available for use. Whether any of this will be relevant tomorrow is given cursory consideration.
期刊介绍：
This is an index to the ACML Proceedings for conferences held 1967 - 1976, and to the Bulletin (originally Newsletter) from 1968 to the present. The include articles, reports, minutes, news items, and reviews of books, atlases, maps, microforms and software. ACMLA publishes facsimiles of maps of Canadian interest, a number of which have been reproduced on Bulletin covers, and are therefore included in the index.
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