{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T22:23:22Z","timestamp":1743027802706,"version":"3.40.3"},"publisher-location":"Singapore","reference-count":15,"publisher":"Springer Nature Singapore","isbn-type":[{"type":"print","value":"9789819605699"},{"type":"electronic","value":"9789819605705"}],"license":[{"start":{"date-parts":[[2024,11,30]],"date-time":"2024-11-30T00:00:00Z","timestamp":1732924800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,11,30]],"date-time":"2024-11-30T00:00:00Z","timestamp":1732924800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025]]},"DOI":"10.1007\/978-981-96-0570-5_20","type":"book-chapter","created":{"date-parts":[[2024,11,30]],"date-time":"2024-11-30T00:46:51Z","timestamp":1732927611000},"page":"278-288","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["An Efficient Device Placement Method for\u00a0Distributed Training of\u00a0Multi-branch Neural Network-Based Remote Sensing Interpretation"],"prefix":"10.1007","author":[{"given":"Ao","family":"Long","sequence":"first","affiliation":[]},{"given":"Yuewei","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Xiaohui","family":"Huang","sequence":"additional","affiliation":[]},{"given":"Wei","family":"Han","sequence":"additional","affiliation":[]},{"given":"Runyu","family":"Fan","sequence":"additional","affiliation":[]},{"given":"Yunliang","family":"Chen","sequence":"additional","affiliation":[]},{"given":"Jianxin","family":"Li","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,11,30]]},"reference":[{"key":"20_CR1","doi-asserted-by":"publisher","first-page":"87","DOI":"10.1016\/j.isprsjprs.2023.05.032","volume":"202","author":"W Han","year":"2023","unstructured":"Han, W., et al.: A survey of machine learning and deep learning in remote sensing of geological environment: challenges, advances, and opportunities. ISPRS J. Photogramm. Remote. Sens. 202, 87\u2013113 (2023)","journal-title":"ISPRS J. Photogramm. Remote. Sens."},{"issue":"6","key":"20_CR2","doi-asserted-by":"publisher","DOI":"10.1016\/j.xinn.2023.100519","volume":"4","author":"L Wang","year":"2023","unstructured":"Wang, L., Zuo, B., Le, Y., Chen, Y., Li, J.: Penetrating remote sensing: next-generation remote sensing for transparent earth. The Innovation 4(6), 100519 (2023)","journal-title":"The Innovation"},{"issue":"9","key":"20_CR3","doi-asserted-by":"publisher","first-page":"6690","DOI":"10.1109\/TGRS.2019.2907932","volume":"57","author":"S Li","year":"2019","unstructured":"Li, S., Song, W., Fang, L., Chen, Y., Ghamisi, P., Benediktsson, J.A.: Deep learning for hyperspectral image classification: An overview. IEEE Trans. Geosci. Remote Sens. 57(9), 6690\u20136709 (2019)","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"issue":"7","key":"20_CR4","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3645107","volume":"56","author":"A Bakhtiarnia","year":"2024","unstructured":"Bakhtiarnia, A., Zhang, Q., Iosifidis, A.: Efficient high-resolution deep learning: a survey. ACM Comput. Surv. 56(7), 1\u201335 (2024)","journal-title":"ACM Comput. Surv."},{"key":"20_CR5","doi-asserted-by":"publisher","DOI":"10.1016\/j.rse.2024.114101","volume":"305","author":"H He","year":"2024","unstructured":"He, H., Yan, J., Liang, D., Sun, Z., Li, J., Wang, L.: Time-series land cover change detection using deep learning-based temporal semantic segmentation. Remote Sens. Environ. 305, 114101 (2024)","journal-title":"Remote Sens. Environ."},{"key":"20_CR6","first-page":"1","volume":"60","author":"W Han","year":"2022","unstructured":"Han, W., et al.: Geological remote sensing interpretation using deep learning feature and an adaptive multisource data fusion network. IEEE Trans. Geosci. Remote Sens. 60, 1\u201314 (2022)","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"20_CR7","doi-asserted-by":"publisher","first-page":"4973","DOI":"10.1109\/JSTARS.2020.3019410","volume":"13","author":"R Fan","year":"2020","unstructured":"Fan, R., Feng, R., Wang, L., Yan, J., Zhang, X.: Semi-mcnn: a semisupervised multi-cnn ensemble learning method for urban land cover classification using submeter hrrs images. IEEE J. Selected Topics Appli. Earth Observat. Remote Sensing 13, 4973\u20134987 (2020)","journal-title":"IEEE J. Selected Topics Appli. Earth Observat. Remote Sensing"},{"key":"20_CR8","doi-asserted-by":"publisher","unstructured":"Long, A., Han, W., Huang, X., Li, J., Wang, Y., Chen, J.: Distributed deep learning for big remote sensing data processing on apache spark: geological remote sensing interpretation as a case study. In: Web and Big Data, pp. 96\u2013110. Springer Nature Singapore, Singapore (2024). https:\/\/doi.org\/10.1007\/978-981-97-2303-4_7","DOI":"10.1007\/978-981-97-2303-4_7"},{"key":"20_CR9","unstructured":"Mirhoseini, A., et al.: Device placement optimization with reinforcement learning. In: Precup, D., Teh, Y.W. (eds.) Proceedings of the 34th International Conference on Machine Learning, ICML 2017, Sydney, NSW, Australia, 6-11 August 2017. Proceedings of Machine Learning Research, vol.\u00a070, pp. 2430\u20132439. PMLR (2017)"},{"key":"20_CR10","doi-asserted-by":"crossref","unstructured":"Jeon, B., et al.: Baechi: fast device placement of machine learning graphs. In: Proceedings of the 11th ACM Symposium on Cloud Computing, pp. 416-430 (2020)","DOI":"10.1145\/3419111.3421302"},{"key":"20_CR11","unstructured":"Zheng, L., et al.: Alpa: automating inter- and intra-operator parallelism for distributed deep learning. In: Aguilera, M.K., Weatherspoon, H. (eds.) 16th USENIX Symposium on Operating Systems Design and Implementation, OSDI 2022, Carlsbad, CA, USA, 11-13 July 2022. pp. 559\u2013578. USENIX Association (2022)"},{"key":"20_CR12","unstructured":"Sergeev, A., Balso, M.D.: Horovod: fast and easy distributed deep learning in tensorflow. CoRR abs\/ arXiv: 1802.05799 (2018)"},{"key":"20_CR13","unstructured":"Or-tools (2018). https:\/\/developers.google.com\/optimization"},{"key":"20_CR14","unstructured":"Pytorch-opcounter (2019). https:\/\/github.com\/Lyken17\/pytorch-OpCounter"},{"key":"20_CR15","unstructured":"Bojja\u00a0Venkatakrishnan, S., Gupta, S., Mao, H., Alizadeh, M., et\u00a0al.: Learning generalizable device placement algorithms for distributed machine learning. Adv. Neural Inform. Process. Syst. 32 (2019)"}],"container-title":["Lecture Notes in Computer Science","Web Information Systems Engineering \u2013 WISE 2024"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-96-0570-5_20","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,11,30]],"date-time":"2024-11-30T01:06:58Z","timestamp":1732928818000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-96-0570-5_20"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,11,30]]},"ISBN":["9789819605699","9789819605705"],"references-count":15,"URL":"https:\/\/doi.org\/10.1007\/978-981-96-0570-5_20","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2024,11,30]]},"assertion":[{"value":"30 November 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"WISE","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Web Information Systems Engineering","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Doha","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Qatar","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2 December 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"5 December 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"25","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"wise2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/wise2024-qatar.com\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}