Fine Urban Land Use Identification Based on Fusion of Multi-source Data

LI Linchao; ZHONG Liangjian; SU Qing; REN Lu; DU Bowen

doi:10.3969/j.issn.0258-2724.20230296

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Journal of Southwest Jiaotong University > 2025 >

LI Linchao, ZHONG Liangjian, SU Qing, REN Lu, DU Bowen. Fine Urban Land Use Identification Based on Fusion of Multi-source Data[J]. Journal of Southwest Jiaotong University. doi: 10.3969/j.issn.0258-2724.20230296

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LI Linchao, ZHONG Liangjian, SU Qing, REN Lu, DU Bowen. Fine Urban Land Use Identification Based on Fusion of Multi-source Data[J]. Journal of Southwest Jiaotong University. doi: 10.3969/j.issn.0258-2724.20230296

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Fine Urban Land Use Identification Based on Fusion of Multi-source Data

doi: 10.3969/j.issn.0258-2724.20230296

1.
College of Civil and Transportation Engineering, Shenzhen University, Shenzhen 518060, China
2.
School of Transportation Engineering, Chang’an University, Xi’an 710061, China
3.
School of Computer Science and Engineering, Beihang University, Beijing 100191, China

Received Date: 20 Jun 2023
Rev Recd Date: 16 Nov 2023

Available Online: 17 Jan 2025

Abstract

Abstract

Land use type in China is complex, and it is difficult to accurately identify urban land use type by relying on a single remote sensing image or point of interest (POI) data. To address this issue, a fine identification method combining remote sensing images and POI data was proposed. Firstly, to finely identify urban land parcel functions, a 500-meter grid was selected as the research unit; secondly, POI data were extracted, and kernel density distribution maps of various land uses were generated. Data preprocessing, data segmentation, and data enhancement were performed on remote sensing and POI image data to extract effective information. Finally, the POI kernel density distribution map and high-resolution remote sensing image data were fused together, and the current land use data was used as the label to construct the UNet++ network to classify urban land parcels. The model parameters were optimized using the cosine annealing (CA) algorithm, and the proposed method was tested in Shenzhen City. Migration verification was carried out in Luohu District and Nanshan District. The results show that the average accuracy of the urban land use identification model fused with POI data is 70.6%, which is 6.7% higher than that of the identification model using only remote sensing data; after using the CA algorithm, the model accuracy is increased by 1.5%. The migration verification of the model is carried out, and the average accuracy of the model is 72.6%. This shows that the model is robust. In addition, POI data makes up for the shortcomings of remote sensing images that only involve spectrum, texture, and physical attributes of ground structures, and it can better identify commercial land and public management and service land. The accuracy is 7.5% and 6.0% higher than that of a single data identification model.
- traffic planning,
- land use,
- deep learning,
- multi-source data

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References

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