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Wednesday, April 11 • 1:30pm - 1:45pm
SYMPOSIA-13: Water Inundation Mapping from 2000 by CNN for Southeast China

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AUTHORS: Qi Mao*, Peking University

ABSTRACT: Remote sensing approaches designed to spatially and temporally mapping seasonal inundation provide critical information for many applications which include water accounting, flood control and forecast and groundwater recharge estimation. Deep-learning algorithms have recently become a hotspot in the geoscience and remote sensing. In addition, as the most representative supervised DL model, convolutional neural network(CNN) have outperformed most algorithms in many fields. In this study, a framework of CNN for seasonal inundation mapping would be presented for South China-a key area of China historically, politically and economically. Available 8-day MODIS data from the year of 2000 would be used here. Firstly we labeled the fraction of water cover of a few training samples using higher resolution RS data. Then a convolutional neural network is trained with Tensorflow to generate a time-series of the fraction of water cover for Southeast China. Each pixel would be classified by annual and seasonal fraction of water cover for further analysis with physical and social data. Furthermore, the cross-validation would be implemented. And ground water flow observation would be also used for validation. The results of this study demonstrate the potential for the use of MODIS data for temporal and spatial detection of water inundation and help to understand the change of water inundation in regional scale from 2000. The impact of climate change and human activity on water inundation would be discussed here as well.

Wednesday April 11, 2018 1:30pm - 1:45pm CDT
Water Tower Parlor

Attendees (7)