Ensemble Encoder-Decoder Models for Predicting Land Transformation

Land development is a dynamic and complex processinfluenced by a system of interconnected driving variables. Predicting such a process is important in mitigating severe climate situations and improving the resiliency of communities. Current predictive models in land transformation have not paid a se...

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Vydané v:IEEE journal of selected topics in applied earth observations and remote sensing Ročník 14; s. 11429 - 11438
Hlavní autori: Pourmohammadi, Pariya, Strager, Michael P., Adjeroh, Donald A.
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Piscataway IEEE 2021
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:
Artificial neural networks
Biological system modeling
Climate models
Coders
Computational modeling
Convolutional neural networks
Convolutional neural networks (CNNs)
Cubes
Data models
developed land expansion
evidence fusion
Genetic transformation
Land development
land transformation prediction
Neural networks
Performance prediction
Prediction models
Predictive models
Scientific visualization
Solid modeling
Stability
Three-dimensional displays
Transformations
Two dimensional models
Watersheds
ISSN:1939-1404, 2151-1535
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Shrnutí:Land development is a dynamic and complex processinfluenced by a system of interconnected driving variables. Predicting such a process is important in mitigating severe climate situations and improving the resiliency of communities. Current predictive models in land transformation have not paid a serious attention to capturing and exploiting the interchannel relationships. Moreover, these models often have problems with generalization, which results in poor performance during testing. In this study, we use a novel multichannel data cube, constructed from socioeconomic attributes, terrain characteristics, and landscape traits, to predict land transformation in a watershed in the US. In particular, we introduce methods for projecting impervious land transformations using these data cubes, using 2-D and 3-D convolutional neural networks (CNNs) and their ensembles. We apply fusion at decision, score, and feature levels to improve the generalization ability and robustness of the proposed predictive models. Performance is assessed using the Dice coefficient, receiver operating characteristic curves, data visualization, and running time. Our study shows that the use of 2-D and 3-D CNN ensembles improved the performance of the models in terms of model stability, precision and recall, and Dice coefficient.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
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content type line 14
ISSN:1939-1404
2151-1535
DOI:10.1109/JSTARS.2021.3120659