Multi-type and fine-grained urban green space function mapping based on BERT model and multi-source data fusion

ABSTRACT

Urban green space (UGS) is important to the urban ecological environment. It has physical characteristics and social function characteristics and plays an important role in urban climate change, sustainable development goals (SDG) and residents’ health. However, existing researches mostly focus on the extraction of UGS physical features, neglecting the importance of UGS social functions, resulting in the unresolved problem of multi-type and fine-grained functional mapping of UGS. Therefore, based on natural language processing (NLP) and multi-source data fusion, this paper proposes a multi-type and fine-grained UGS function mapping method. First, the social functional standards of UGS have been re-established, with a total of 19 categories. Second, the semantic information in the POI data name text is extracted using the deep learning model, and the reclassification of the UGS type of POI data is realized. Then, combined with multi-source data, 18 types of UGS are extracted. Finally, combining multi-source data to extract urban road green spaces (GS), a fine-grained UGS functional map of Shanghai is created. The results show that the overall accuracy rate of the method is 93.6%, and the Kappa coefficient is 0.93, which proves that the method has good performance in large-scale spatial UGS classification.

KEYWORDS:

• Urban green space
• deep learning
• natural language learning
• BERT
• crowd-sourced geographic data

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

The data that support the findings of this study are openly available in ‘Zenodo’ at https://zenodo.org/record/8211083.

Additional information

Funding

This work was supported by the National Natural Science Foundation of China [grant number 42201512], the National Natural Science Foundation of China [grant number 41930650] and the National Natural Science Foundation of China [grant number 42371412], the China Postdoctoral Science Foundation [grant number 2021M703511].