{"title":"利用因果机器学习推断城市土地利用对建筑高度的异质性影响","authors":"Yimin Chen, Jing Chen, Shuai Zhao, Xiaocong Xu, Xiaoping Liu, Xinchang Zhang, Honghui Zhang","doi":"10.1080/15481603.2024.2321695","DOIUrl":null,"url":null,"abstract":"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...","PeriodicalId":55091,"journal":{"name":"GIScience & Remote Sensing","volume":null,"pages":null},"PeriodicalIF":6.0000,"publicationDate":"2024-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Inferring the heterogeneous effect of urban land use on building height with causal machine learning\",\"authors\":\"Yimin Chen, Jing Chen, Shuai Zhao, Xiaocong Xu, Xiaoping Liu, Xinchang Zhang, Honghui Zhang\",\"doi\":\"10.1080/15481603.2024.2321695\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"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...\",\"PeriodicalId\":55091,\"journal\":{\"name\":\"GIScience & Remote Sensing\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":6.0000,\"publicationDate\":\"2024-02-25\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"GIScience & Remote Sensing\",\"FirstCategoryId\":\"89\",\"ListUrlMain\":\"https://doi.org/10.1080/15481603.2024.2321695\",\"RegionNum\":2,\"RegionCategory\":\"地球科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"GEOGRAPHY, PHYSICAL\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"GIScience & Remote Sensing","FirstCategoryId":"89","ListUrlMain":"https://doi.org/10.1080/15481603.2024.2321695","RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"GEOGRAPHY, PHYSICAL","Score":null,"Total":0}
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 lan...
期刊介绍：
GIScience & Remote Sensing publishes original, peer-reviewed articles associated with geographic information systems (GIS), remote sensing of the environment (including digital image processing), geocomputation, spatial data mining, and geographic environmental modelling. Papers reflecting both basic and applied research are published.
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