Environmental remote sensingDeep learningParameter retrievalNeural networkVarious forms of machine learning (ML) methods have historically played a valuable role in environmental remote sensing research. With an increasing amount of "big data" from earth observation and rapid advances in ML, increasing ...
1、Deep learning in environmental remote sensing: Achievements and challenges 2、ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci 3、Review on remote sensing methods for landslide detectionusing machine and deep learning 4、Deep Learning for Medicine and Remote Sensing: A Brief Review 5、Deep ...
作者: ER Sensing,D Learning,P Retrieval,N Network,Remote Sensing of Environment: An Interdisciplinary Journal 摘要: Various forms of machine learning (ML) methods have historically played a valuable role in environmental remote sensing research. With an increasing amount of "big data" from earth.....
Zhang, “Deep learning in environmental remote sensing: Achievements and challenges,” Remote Sensing of Environment, vol. 241, no. January, p. 111716, 2020. [17] L. Ma, Y. Liu, X. Zhang, Y. Ye, G. Yin, and B. A. Johnson, “Deep learning in remote sensing applications: A meta...
报告题目:Deep learning for cryosphere remote sensing 报告人:刘琳 副教授 主持人:王康 青年研究员 报告时间:2022年11月7日 下午3:00—4:30 腾讯会议:363-977-547 主办单位:地理科学学院、地理信息科学教育部重点实验室 主讲人简介 Lin ...
DEEP learningLANDSLIDE hazard analysisSENTINEL-1 (Artificial satellite)REMOTE sensingRECURRENT neural networksSYNTHETIC aperture radar MA Hussain,Z Chen,Y Zheng,... - 《Remote Sensing》 被引量: 0发表: 2023年 A Comparative Study of Deep Learning and Traditional Methods for Environmental Remote Sensing ...
Physics, deep learning, remote sensing, time series, interpretation Learn more about the benefits of publishing in a special issue: https://www.elsevier.com/authors/submit-your-paper/special-issues Interested in becoming a gu...
An ensemble machine learning model for water quality estimation in coastal area based on remote sensing imagery 2022, Journal of Environmental Management Citation Excerpt : These methods are time-consuming and costly (Guo et al., 2021a,b). Besides, the results from sampling points cannot well rep...
LoveDA -> A Remote Sensing Land-Cover Dataset for Domain Adaptive Semantic Segmentation Satellite Imagery Semantic Segmentation with CNN -> 7 different segmentation classes, DeepGlobe Land Cover Classification Challenge dataset, with repo Aerial Semantic Segmentation using U-Net Deep Learning Model medium...
Recent research has demonstrated the value of Deep Learning (DL) methods for improving ENSO prediction as well as Complex Networks (CN) for understanding teleconnections. However, gaps in predictive understanding of ENSO-driven river flows include the black box nature of DL, the use of simple ...