يعرض 1 - 10 نتائج من 221 نتيجة بحث عن '"Multi-source remote sensing data"', وقت الاستعلام: 1.47s تنقيح النتائج
  1. 1
    مؤتمر
    Monitoring of Surface Water Area and Water Level Changes in Nine Plateau Lakes in Yunnan and Analysis of Influencing Factors

    المؤلفون: Jiang, Zhiyu, Hong, Liang

    المصدر: 2024 IEEE 6th Advanced Information Management, Communicates, Electronic and Automation Control Conference (IMCEC) Advanced Information Management, Communicates, Electronic and Automation Control Conference (IMCEC), 2024 IEEE 6th. 6:1027-1031 May, 2024

    Relation: 2024 IEEE 6th Advanced Information Management, Communicates, Electronic and Automation Control Conference (IMCEC)

    Full Text Finder
    أضف إلى المفضلة
  2. 2
    مؤتمر
    Remote sensing AI interpretation technology based on deep learning analyzes the damaged buildings in Gaza during the Palestinian-Israeli Conflict

    المؤلفون: Zuo, Tao, Liu, Yixuan, Liu, Heng, Lei, Qiangfang, Chai, Chengfu

    المصدر: 2024 5th International Conference on Geology, Mapping and Remote Sensing (ICGMRS) Geology, Mapping and Remote Sensing (ICGMRS), 2024 5th International Conference on. :7-14 Apr, 2024

    Relation: 2024 5th International Conference on Geology, Mapping and Remote Sensing (ICGMRS)

    أضف إلى المفضلة
  3. 3
    دورية أكاديمية
    A global multimodal flood event dataset with heterogeneous text and multi-source remote sensing images

    المؤلفون: Zhixin Zhang, Yan Ma, Peng Liu

    المصدر: Big Earth Data, Pp 1-27 (2024)

    مصطلحات موضوعية: Flood event, multimodal dataset, deep learning, multi-source remote sensing data, internet data, Geography. Anthropology. Recreation, Geology, QE1-996.5

    وصف الملف: electronic resource

    Relation: https://doaj.org/toc/2096-4471; https://doaj.org/toc/2574-5417

    URL الوصول: https://doaj.org/article/a0a3215ae61f413c95133b94bf13cdeb

    PDF full text from Publisher Full Text Finder
    أضف إلى المفضلة
  4. 4
    دورية أكاديمية
    GOA-optimized deep learning for soybean yield estimation using multi-source remote sensing data

    المؤلفون: Jian Lu, Hongkun Fu, Xuhui Tang, Zhao Liu, Jujian Huang, Wenlong Zou, Hui Chen, Yue Sun, Xiangyu Ning, Jian Li

    المصدر: Scientific Reports, Vol 14, Iss 1, Pp 1-19 (2024)

    مصطلحات موضوعية: GOA, Deep learning framework, Multi-source remote sensing data, Soybean yield estimation, Photosynthesis-related parameters, Medicine, Science

    وصف الملف: electronic resource

    Relation: https://doaj.org/toc/2045-2322

    URL الوصول: https://doaj.org/article/b553cc3485c24f2a9f801d3181e13ba6

    Find this article in full text from Springer Verlag. Find this article in full text from ProQuest Full Text Finder
    أضف إلى المفضلة
  5. 5
    مؤتمر
    Identification of Offshore Oil and Gas Drilling Platforms Based on Multi-source Remote Sensing Data

    المؤلفون: Ma, Caihong, Guan, LinLin, Wang, Dacheng

    المصدر: 2021 7th Annual International Conference on Network and Information Systems for Computers (ICNISC) ICNISC Network and Information Systems for Computers (ICNISC), 2021 7th Annual International Conference on. :384-387 Jul, 2021

    Relation: 2021 7th Annual International Conference on Network and Information Systems for Computers (ICNISC)

    أضف إلى المفضلة
  6. 6
    دورية أكاديمية
    A new multi-source remote sensing image sample dataset with high resolution for flood area extraction: GF-FloodNet

    المؤلفون: Yuwei Zhang, Peng Liu, Lajiao Chen, Mengzhen Xu, Xingyan Guo, Lingjun Zhao

    المصدر: International Journal of Digital Earth, Vol 16, Iss 1, Pp 2522-2554 (2023)

    مصطلحات موضوعية: flood area extraction, dataset construction, multi-source remote sensing data, deep learning, Mathematical geography. Cartography, GA1-1776

    وصف الملف: electronic resource

    Relation: https://doaj.org/toc/1753-8947; https://doaj.org/toc/1753-8955

    URL الوصول: https://doaj.org/article/b9bbd9a97ebb422e9f6aee429e9bba14

    PDF full text from Publisher Full Text Finder
    أضف إلى المفضلة
  7. 7
    مؤتمر
    Smoke Injection Height of Wildfire Event Based on Multi-Source Remote Sensing Data in Yunnan Province, China

    المؤلفون: Wang, Wenjia, Zhang, Qixing, Luo, Jie, Zhao, Ranran, Zhang, Yongming

    المصدر: IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium Geoscience and Remote Sensing Symposium, IGARSS 2020 - 2020 IEEE International. :5497-5500 Sep, 2020

    Relation: IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium

    Full Text Finder
    أضف إلى المفضلة
  8. 8
    مؤتمر
    The Data Preparation Research on Global Multi-Source Synergized Quantitative Remote Sensing Production System

    المؤلفون: Li, Hongyi, Tang, Ping

    المصدر: IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium Geoscience and Remote Sensing Symposium, IGARSS 2019 - 2019 IEEE International. :4340-4343 Jul, 2019

    Relation: IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium

    Full Text Finder
    أضف إلى المفضلة
  9. 9
    دورية أكاديمية
    A novel alpine land cover classification strategy based on a deep convolutional neural network and multi-source remote sensing data in Google Earth Engine

    المؤلفون: Yang Qichi, Wang Lihui, Huang Jinliang, Liu Linzhi, Li Xiaodong, Xiao Fei, Du Yun, Yan Xue, Ling Feng

    المصدر: GIScience & Remote Sensing, Vol 60, Iss 1 (2023)

    مصطلحات موضوعية: alpine land cover mapping, deep convolutional neural network, multi-source remote sensing data, google earth engine, yarlung zangbo river basin, Mathematical geography. Cartography, GA1-1776, Environmental sciences, GE1-350

    وصف الملف: electronic resource

    Relation: https://doaj.org/toc/1548-1603; https://doaj.org/toc/1943-7226

    URL الوصول: https://doaj.org/article/0a4779377c28466f8647d2ea2916934e

    PDF full text from Publisher Full Text Finder
    أضف إلى المفضلة
  10. 10
    دورية أكاديمية
    Geological and evolutionary characteristics of the Gagarin Region on the far side of the Moon

    المؤلفون: SHAO Tianrui, HAN Kunying, JIN Ming, SHI Chenglong, PANG Jianfeng, DING Xiaozhong

    المصدر: Dizhi lixue xuebao, Vol 30, Iss 3, Pp 519-534 (2023)

    مصطلحات موضوعية: lunar geology, gagarin basin, multi-source remote sensing data, geological evolution, Geology, QE1-996.5

    وصف الملف: electronic resource

    Relation: https://doaj.org/toc/1006-6616

    URL الوصول: https://doaj.org/article/ec589ac38eb245628f172ce6423f3871

    Full Text Finder
    أضف إلى المفضلة
أدوات البحث: أحصل على تغذية RSS – أرسل هذا البحث بالبريد الإلكتروني – حفظ البحث – جدول التنبيهات: لا شيء