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The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Articles | Volume XLIII-B1-2020
https://doi.org/10.5194/isprs-archives-XLIII-B1-2020-85-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/isprs-archives-XLIII-B1-2020-85-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
06 Aug 2020
| 06 Aug 2020
SPATIAL RESOLUTION ENHANCEMENT OF LAND COVER MAPPING USING DEEP CONVOLUTIONAL NETS
Q. Yu
Dept. of Geography and Environmental Management, University of Waterloo, Canada
W. Liu
Virtual Reality and Interactive Techniques Institute, East China Jiaotong University, Jiangxi, China
J. Li
Dept. of Geography and Environmental Management, University of Waterloo, Canada
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It is challenging to incorporate the spatial distribution information of input variables when implementing LSTM models for streamflow prediction. This paper presents a novel hybrid modeling approach to predict streamflow while accounting for spatial variability. We evaluated the performance against lumped LSTM predictions in 224 basins across the Great Lakes region in North America. This approach shows promise in predicting streamflow at large ungauged basin.
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