Recognizing urban functional zones by a hierarchical fusion method considering landscape features and human activities,Transactions in GIS

当前位置： X-MOL 学术 › Trans. GIS › 论文详情

Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)

Recognizing urban functional zones by a hierarchical fusion method considering landscape features and human activities
Transactions in GIS ( IF 2.1 ) Pub Date : 2020-06-17 , DOI: 10.1111/tgis.12642
Huimin Liu ₁ , Yiyuan Xu ₁ , Jianbo Tang ₁ , Min Deng ₁ , Jincai Huang _{1,

2} , Wentao Yang ₃ , Fang Wu ₄

Affiliation

Functional zones are basic spatial units of urban management and urban planning and they provide the basic space where human activities occur. The concept of urban functional zones is closely related to two basic factors: urban landscape environment and human activities. The former provides the basic space for human activity and influences urban land use at a coarse scale, and the latter indicates the differentiation of functions in local urban areas. In previous studies, the hierarchical correspondence and interaction between urban landscape and human activities have not been given full consideration in the cognition of urban functional zones, which would influence the accuracy and interpretability of the results. Therefore, a hierarchical fusion method considering urban landscape and human activity patterns based on multi‐source data is proposed in this article. We introduce a new cognition framework. First, a land use classification based on urban landscape features from remote sensing images is extracted and used as the fundamental results of urban land uses. Then, fine‐grained functional semantics of local urban areas are recognized based on human activity patterns extracted from crowdsourced data (i.e., points of interest and taxi trajectories). Finally, the above results at different scales are fused with hierarchical constraints, which means that the information from both urban landscape and human activities can be fully utilized to obtain a refined and fine‐grained map of urban functional zones. Wuhan, China was chosen as an example to evaluate the performance of the proposed method. Results show that the overall accuracy of the proposed method is 82.51% (accuracies of the mixed functional zones and single functional zones are 77.93 and 87.96%, respectively) and compared with state‐of‐the‐art methods, the proposed method performed better for the recognition of mixed functional zones.

更新日期：2020-06-17

点击分享查看原文

点击收藏

公开下载

阅读更多本刊最新论文