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Inferring the heterogeneous effect of urban land use on building height with causal machine learning
GIScience & Remote Sensing ( IF 6.7 ) Pub Date : 2024-02-25 , DOI: 10.1080/15481603.2024.2321695 Yimin Chen 1 , Jing Chen 1 , Shuai Zhao 1 , Xiaocong Xu 1 , Xiaoping Liu 1 , Xinchang Zhang 2 , Honghui Zhang 3
GIScience & Remote Sensing ( IF 6.7 ) Pub Date : 2024-02-25 , DOI: 10.1080/15481603.2024.2321695 Yimin Chen 1 , Jing Chen 1 , Shuai Zhao 1 , Xiaocong Xu 1 , Xiaoping Liu 1 , Xinchang Zhang 2 , Honghui Zhang 3
Affiliation
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 lan...
中文翻译:
利用因果机器学习推断城市土地利用对建筑高度的异质影响
机器学习已成为土地利用变化建模的重要方法。然而,传统的机器学习算法在捕捉网络中因果关系的能力方面受到限制。
更新日期:2024-02-28
中文翻译:
利用因果机器学习推断城市土地利用对建筑高度的异质影响
机器学习已成为土地利用变化建模的重要方法。然而,传统的机器学习算法在捕捉网络中因果关系的能力方面受到限制。