Chao Zeng

TL;DR: In this paper, a unified spatial-temporal-spectral framework based on a deep convolutional neural network (CNN) was proposed for missing information reconstruction in remote sensing images.

TL;DR: Wang et al. as mentioned in this paper developed a generalized regression neural network (GRNN) model to estimate PM2.5 concentrations at a national scale, and different assessment experiments are undertaken in time and space, to comprehensively evaluate and compare the performance of widely used models.

TL;DR: The proposed model employs a unified deep CNN combined with spatial–temporal–spectral supplementary information to solve three typical missing information reconstruction tasks: 1) dead lines in Aqua Moderate Resolution Imaging Spectroradiometer band 6; 2) the Landsat Enhanced Thematic Mapper Plus scan line corrector-off problem; and 3) thick cloud removal.

TL;DR: In this article, a 5-layer structured deep belief network (DBN) is employed to better capture the complicated and non-linear relationships between air temperature and different predictor variables, and the DBN model was implemented for 0.01° daily maximum air temperature mapping across China.

TL;DR: This study makes the first attempt to employ deep learning for Ta mapping mainly based on space remote sensing and ground station observations and employs a 5-layers structured deep belief network to better capture the complicated and non-linear relationships between Ta and different predictor variables.