Art
J-GLOBAL ID:202102265882967738   Reference number:21A0292647

Identifying the vegetation type in Google Earth images using a convolutional neural network: a case study for Japanese bamboo forests

畳込みニューラルネットワークを用いたGoogle地球画像における植生タイプの同定:日本竹林の事例研究【JST・京大機械翻訳】
Author (4):
Material:
Volume: 20  Issue:Page: 1-14  Publication year: 2020 
JST Material Number: U7365A  ISSN: 1472-6785  Document type: Article
Article type: 原著論文  Country of issue: United Kingdom (GBR)  Language: ENGLISH (EN)
Abstract/Point:
Abstract/Point
Japanese summary of the article(about several hundred characters).
All summary is available on JDreamIII(charged).
On J-GLOBAL, this item will be available after more than half a year after the record posted. In addtion, medical articles require to login to MyJ-GLOBAL.
Classifying and mapping vegeta...
   To see more with JDream III (charged).   {{ this.onShowAbsJLink("http://jdream3.com/lp/jglobal/index.html?docNo=21A0292647&from=J-GLOBAL&jstjournalNo=U7365A") }}
Thesaurus term:
Thesaurus term/Semi thesaurus term
Keywords indexed to the article.
All keywords is available on JDreamIII(charged).
On J-GLOBAL, this item will be available after more than half a year after the record posted. In addtion, medical articles require to login to MyJ-GLOBAL.

Semi thesaurus term:
Thesaurus term/Semi thesaurus term
Keywords indexed to the article.
All keywords is available on JDreamIII(charged).
On J-GLOBAL, this item will be available after more than half a year after the record posted. In addtion, medical articles require to login to MyJ-GLOBAL.
, 【Automatic Indexing@JST】
JST classification (3):
JST classification
Category name(code) classified by JST.
Graphic and image processing in general  ,  Forest mensuration(=dendrometry)  ,  Artificial intelligence 
Reference (29):
  • Mapping species distribution: spatial inference and prediction; 2009; CR1; J Franklin; citation_publisher=Cambridge University Press
  • J Plant Ecol; Remote sensing imagery in vegetation mapping: a review; Y Xie, Z Sha, M Yu; 1; 2008; 9-23; 10.1093/jpe/rtm005; citation_id=CR2
  • Drucker H, Burges CJ, Kauffman L, Smola A, Vapnik V. Support vector regression machines. Neural information processing systems 9. eds Mozer MC, Jordan JI & Petsche T. pp. 155-161, MIT Press 1997.
  • Foundat Trends Mach Learn; Learning deep architectures for AI; Y Bengio; 2; 2009; 1-127; 10.1561/2200000006; citation_id=CR4
  • Goodfellow I, Bengio Y, Courville A. Deep Learning. MIT press. 2016.
more...

Return to Previous Page