Simulating urban expansion with interpretable cycle recurrent neural networks
Recent advances in deep learning have brought new opportunities for analyzing land dynamics, and Recurrent Neural Networks (RNNs) presented great potential in predicting land-use and land-cover (LULC) changes by learning the transition rules from time series data. However, implementing RNNs for LULC...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Taylor & Francis Group
2024-12-01
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| Series: | GIScience & Remote Sensing |
| Subjects: | |
| Online Access: | https://www.tandfonline.com/doi/10.1080/15481603.2024.2363576 |
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