CJRS’ Special Issue on Deep Learning for Environmental Applications of Remote Sensing Data
(2021). CJRS’ Special Issue on Deep Learning for Environmental Applications of Remote Sensing Data. Canadian Journal of Remote Sensing: Vol. 47, Deep Learning for Environmental Applications of Remote Sensing Data, pp. 159-161.
This Special Issue covers a broad range of topics, such as transfer learning, design of new Deep Neural Network (DNN), CNN, and GAN models, as well as a wide range of applications (Table 1), including agriculture (four papers), natural resources (three papers), marine environments (two papers), change detection (one paper), and disaster damage detection (one paper).
This Special Issue covers a broad range of topics, such as transfer learning, design of new Deep Neural Network (DNN), CNN, and GAN models, as well as a wide range of applications (Table 1), including agriculture (four papers), natural resources (three papers), marine environments (two papers), change detection (one paper), and disaster damage detection (one paper).
CJRS’ Special Issue on Deep Learning for Environmental Applications of Remote Sensing Data