Baolin Yang

TL;DR: In this article, a snow cover mapping method based on the satellite data of the Sentinels is proposed, in which the coherence and backscattering coefficient image of Synthetic Aperture Radar (SAR) data (Sentinel-1) is combined with the atmospheric correction result of multispectral data (sentinel-2).

TL;DR: In this paper, a construction method of a road blocking image semantic segmentation sample library for full convolutional neural network training is presented. But the method is not suitable for specific problems of post-disaster undamaged pavement detection and road integrity judgment, and adverse effects of tree and shadow shielding on road blocking information extraction can be effectively overcome.

TL;DR: In this paper, a road blocking information extraction based on deep learning image classification is proposed, and the method comprises the steps: building a road-blocking image classification sample library through employing a disaster typical case image, carrying out the training of a convolutional neural network, and obtaining an initial CNN model CNNmodel0; obtaining a post-disaster image I (x) and a road vector R(x) of the research area x, and detecting road blocking to obtain a to-be-detected sample Dn (x); using the trained network CNNmodel 0

TL;DR: In this paper , the authors explore feasible technologies applicable to different risk lands and develop a practical solution for safe rice production at a regional scale, which can effectively improve the precision level of safe utilization of regional polluted lands and save more than half of the total cost.