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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2021
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oai:doaj.org-article:ba72a4acdabb4772a1d4a1fcf1a7a3972021-11-20T00:00:19ZEnsemble Encoder–Decoder Models for Predicting Land Transformation2151-153510.1109/JSTARS.2021.3120659https://doaj.org/article/ba72a4acdabb4772a1d4a1fcf1a7a3972021-01-01T00:00:00Zhttps://ieeexplore.ieee.org/document/9580600/https://doaj.org/toc/2151-1535Land 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.Pariya PourmohammadiMichael P. StragerDonald A. AdjerohIEEEarticleConvolutional neural networks (CNNs)developed land expansionevidence fusionland transformation predictionOcean engineeringTC1501-1800Geophysics. Cosmic physicsQC801-809ENIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 14, Pp 11429-11438 (2021) |
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Convolutional neural networks (CNNs) developed land expansion evidence fusion land transformation prediction Ocean engineering TC1501-1800 Geophysics. Cosmic physics QC801-809 |
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Convolutional neural networks (CNNs) developed land expansion evidence fusion land transformation prediction Ocean engineering TC1501-1800 Geophysics. Cosmic physics QC801-809 Pariya Pourmohammadi Michael P. Strager Donald A. Adjeroh Ensemble Encoder–Decoder Models for Predicting Land Transformation |
description |
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. |
format |
article |
author |
Pariya Pourmohammadi Michael P. Strager Donald A. Adjeroh |
author_facet |
Pariya Pourmohammadi Michael P. Strager Donald A. Adjeroh |
author_sort |
Pariya Pourmohammadi |
title |
Ensemble Encoder–Decoder Models for Predicting Land Transformation |
title_short |
Ensemble Encoder–Decoder Models for Predicting Land Transformation |
title_full |
Ensemble Encoder–Decoder Models for Predicting Land Transformation |
title_fullStr |
Ensemble Encoder–Decoder Models for Predicting Land Transformation |
title_full_unstemmed |
Ensemble Encoder–Decoder Models for Predicting Land Transformation |
title_sort |
ensemble encoder–decoder models for predicting land transformation |
publisher |
IEEE |
publishDate |
2021 |
url |
https://doaj.org/article/ba72a4acdabb4772a1d4a1fcf1a7a397 |
work_keys_str_mv |
AT pariyapourmohammadi ensembleencoderx2013decodermodelsforpredictinglandtransformation AT michaelpstrager ensembleencoderx2013decodermodelsforpredictinglandtransformation AT donaldaadjeroh ensembleencoderx2013decodermodelsforpredictinglandtransformation |
_version_ |
1718419860430520320 |