SALT: A multifeature ensemble learning framework for mapping urban functional zones from VGI data and VHR images

自编码 自发地理信息 人工智能 计算机科学 降维 城市规划 模式识别(心理学) 机器学习 深度学习 数据科学 工程类 土木工程
作者
Hao Wu,Wenting Luo,Anqi Lin,Fanghua Hao,Ana-Maria Olteanu-Raimond,Lanfa Liu,Yan Li
出处
期刊:Computers, Environment and Urban Systems [Elsevier BV]
卷期号:100: 101921-101921 被引量:27
标识
DOI:10.1016/j.compenvurbsys.2022.101921
摘要

Urban functional zone mapping is essential for providing deeper insights into urban morphology and improving urban planning. The emergence of Volunteered Geographic Information (VGI), which provides abundant semantic data, offers a great opportunity to enrich land use information extracted from remote sensing (RS) images. Taking advantage of very-high-resolution (VHR) images and VGI data, this work proposed a SATL multifeature ensemble learning framework for mapping urban functional zones that integrated 65 features from the shapes of building objects, attributes of points of interest (POIs) tags, locations of cellphone users and textures of VHR images. The dimensionality of SALT features was reduced by the autoencoder, and the compressed features were applied to train the ensemble learning model composed of multiple classifiers for optimizing the urban functional zone classification. The effectiveness of the proposed framework was tested in an urbanized region of Nanchang City. The results indicated that the SALT features considering population dynamics and building shapes are comprehensive and feasible for urban functional zone mapping. The autoencoder has been proven efficient for dimension reduction of the original SALT features as it significantly improves the classification of urban functional zones. Moreover, the ensemble learning outperforms other machine learning models in terms of the accuracy and robustness when dealing with multi-classification tasks.
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