{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T04:34:22Z","timestamp":1750221262395,"version":"3.41.0"},"publisher-location":"New York, NY, USA","reference-count":20,"publisher":"ACM","license":[{"start":{"date-parts":[[2018,10,9]],"date-time":"2018-10-09T00:00:00Z","timestamp":1539043200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2018,10,9]]},"DOI":"10.1145\/3264746.3264791","type":"proceedings-article","created":{"date-parts":[[2018,10,11]],"date-time":"2018-10-11T13:19:23Z","timestamp":1539263963000},"page":"53-58","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["Development of vegetation mapping with deep convolutional neural network"],"prefix":"10.1145","author":[{"given":"Sae-Han","family":"Suh","sequence":"first","affiliation":[{"name":"South Dakota State Univ."}]},{"given":"Ji-Eun","family":"Jhang","sequence":"additional","affiliation":[{"name":"South Dakota State Univ."}]},{"given":"Kwanghee","family":"Won","sequence":"additional","affiliation":[{"name":"South Dakota State Univ."}]},{"given":"Sung-Y.","family":"Shin","sequence":"additional","affiliation":[{"name":"South Dakota State Univ."}]},{"given":"Chang Oan","family":"Sung","sequence":"additional","affiliation":[{"name":"Indiana University Southeast"}]}],"member":"320","published-online":{"date-parts":[[2018,10,9]]},"reference":[{"key":"e_1_3_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1162\/neco_a_00990"},{"key":"e_1_3_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"e_1_3_2_1_3_1","volume-title":"The effectiveness of data augmentation in image classification using deep learning (No. 300). Technical report","author":"Wang J.","year":"2017","unstructured":"Wang , J. , & Perez , L. ( 2017 ). The effectiveness of data augmentation in image classification using deep learning (No. 300). Technical report . Wang, J., & Perez, L. (2017). The effectiveness of data augmentation in image classification using deep learning (No. 300). Technical report."},{"key":"e_1_3_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2011.5995347"},{"key":"e_1_3_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2011.2162423"},{"key":"e_1_3_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-46493-0_38"},{"key":"e_1_3_2_1_7_1","volume-title":"Return of the devil in the details: Delving deep into convolutional nets. arXiv preprint arXiv.1405.3531","author":"Chatfield K.","year":"2014","unstructured":"Chatfield , K. , Simonyan , K. , Vedaldi , A. , & Zisserman , A. ( 2014 ). Return of the devil in the details: Delving deep into convolutional nets. arXiv preprint arXiv.1405.3531 . Chatfield, K., Simonyan, K., Vedaldi, A., & Zisserman, A. (2014). Return of the devil in the details: Delving deep into convolutional nets. arXiv preprint arXiv.1405.3531."},{"volume-title":"Do We Need More Training Data or Better Models for Object Detection? In BMVC (Vol. 3","author":"Zhu X.","key":"e_1_3_2_1_8_1","unstructured":"Zhu , X. , Vondrick , C. , Ramanan , D. , & Fowlkes , C. C. (2012, September ). Do We Need More Training Data or Better Models for Object Detection? In BMVC (Vol. 3 , p. 5). Zhu, X., Vondrick, C., Ramanan, D., & Fowlkes, C. C. (2012, September). Do We Need More Training Data or Better Models for Object Detection? In BMVC (Vol. 3, p. 5)."},{"key":"e_1_3_2_1_9_1","volume-title":"Improving Deep Learning using Generic Data Augmentation. arXiv preprint arXiv:1708.06020","author":"Taylor L.","year":"2017","unstructured":"Taylor , L. , & Nitschke , G. ( 2017 ). Improving Deep Learning using Generic Data Augmentation. arXiv preprint arXiv:1708.06020 . Taylor, L., & Nitschke, G. (2017). Improving Deep Learning using Generic Data Augmentation. arXiv preprint arXiv:1708.06020."},{"key":"e_1_3_2_1_10_1","volume-title":"Advances in neural information processing systems (pp. 1097--1105).","author":"Krizhevsky A.","year":"2012","unstructured":"Krizhevsky , A. , Sutskever , I. , & Hinton , G. E. ( 2012 ). Imagenet classification with deep convolutional neural networks . In Advances in neural information processing systems (pp. 1097--1105). Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2012). Imagenet classification with deep convolutional neural networks. In Advances in neural information processing systems (pp. 1097--1105)."},{"key":"e_1_3_2_1_11_1","volume-title":"Big data deep learning: challenges and perspectives","author":"Chen X. W.","year":"2014","unstructured":"Chen , X. W. , & Lin , X. ( 2014 ). Big data deep learning: challenges and perspectives . IEEE access, 2, 514--525. Chen, X. W., & Lin, X. (2014). Big data deep learning: challenges and perspectives. IEEE access, 2, 514--525."},{"key":"e_1_3_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.3390\/s16081222"},{"key":"e_1_3_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.5555\/2627435.2670313"},{"key":"e_1_3_2_1_14_1","volume-title":"End-to-end Training for Whole Image Breast Cancer Diagnosis using An All Convolutional Design. arXiv preprint arXiv:1708.09427","author":"Shen L.","year":"2017","unstructured":"Shen , L. ( 2017 ). End-to-end Training for Whole Image Breast Cancer Diagnosis using An All Convolutional Design. arXiv preprint arXiv:1708.09427 . Shen, L. (2017). End-to-end Training for Whole Image Breast Cancer Diagnosis using An All Convolutional Design. arXiv preprint arXiv:1708.09427."},{"key":"e_1_3_2_1_15_1","volume-title":"Multi-scale context aggregation by dilated convolutions. arXiv preprint arXiv:1511.07122","author":"Yu F.","year":"2015","unstructured":"Yu , F. , & Koltun , V. ( 2015 ). Multi-scale context aggregation by dilated convolutions. arXiv preprint arXiv:1511.07122 . Yu, F., & Koltun, V. (2015). Multi-scale context aggregation by dilated convolutions. arXiv preprint arXiv:1511.07122."},{"key":"e_1_3_2_1_16_1","volume-title":"The impact of imbalanced training data for convolutional neural networks","author":"Masko D.","year":"2015","unstructured":"Masko , D. , & Hensman , P. ( 2015 ). The impact of imbalanced training data for convolutional neural networks . Masko, D., & Hensman, P. (2015). The impact of imbalanced training data for convolutional neural networks."},{"key":"e_1_3_2_1_17_1","volume-title":"Precision agriculture and sustainability. Precision agriculture, 5(4), 359--387","author":"Bongiovanni R.","year":"2004","unstructured":"Bongiovanni , R. , & Lowenberg-DeBoer , J. ( 2004 ). Precision agriculture and sustainability. Precision agriculture, 5(4), 359--387 . Bongiovanni, R., & Lowenberg-DeBoer, J. (2004). Precision agriculture and sustainability. Precision agriculture, 5(4), 359--387."},{"key":"e_1_3_2_1_18_1","doi-asserted-by":"publisher","DOI":"10.1145\/3129676.3129703"},{"key":"e_1_3_2_1_19_1","volume-title":"Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556","author":"Simonyan K.","year":"2014","unstructured":"Simonyan , K. , & Zisserman , A. ( 2014 ). Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 . Simonyan, K., & Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556."},{"key":"e_1_3_2_1_20_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11263-015-0816-y"}],"event":{"name":"RACS '18: International Conference on Research in Adaptive and Convergent Systems","sponsor":["SIGAPP ACM Special Interest Group on Applied Computing","KISM Korean Institute of Smart Media"],"location":"Honolulu Hawaii","acronym":"RACS '18"},"container-title":["Proceedings of the 2018 Conference on Research in Adaptive and Convergent Systems"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3264746.3264791","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3264746.3264791","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T02:10:55Z","timestamp":1750212655000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3264746.3264791"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,10,9]]},"references-count":20,"alternative-id":["10.1145\/3264746.3264791","10.1145\/3264746"],"URL":"https:\/\/doi.org\/10.1145\/3264746.3264791","relation":{},"subject":[],"published":{"date-parts":[[2018,10,9]]},"assertion":[{"value":"2018-10-09","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}