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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Vydáno v:	IEEE journal of selected topics in applied earth observations and remote sensing Ročník 14; s. 11429 - 11438
Hlavní autoři:	Pourmohammadi, Pariya, Strager, Michael P., Adjeroh, Donald A.
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Piscataway IEEE 2021 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:	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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Abstract	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.
AbstractList	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.
Author	Pourmohammadi, Pariya Adjeroh, Donald A. Strager, Michael P.
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SubjectTerms	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
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Title	Ensemble Encoder-Decoder Models for Predicting Land Transformation
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