Inferring the heterogeneous effect of urban land use on building height with causal machine learning
Machine learning has become an important approach for land use change modeling. However, conventional machine learning algorithms are limited in their ability to capture causal relationships in land use change, which are important knowledge for planners and decision makers. In this study, we showcas...
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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/10.1080/15481603.2024.2321695 |
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