Classifying urban land use by integrating remote sensing and social media data

By Xiaoping Liu, Jialv He, Yao Yao, Jinbao Zhang, Haolin Liang, Huan Wang, Ye Hong. International Journal of Geographical Information Science, 2017

328 citations recorded by the source corpus.

ABSTRACT Urban land use information plays an important role in urban management, government policy-making, and population activity monitoring. However, the accurate classification of urban functional zones is challenging due to the complexity of urban systems. Many studies have focused on urban land use classification by considering features that are extracted from either high spatial resolution (HSR) remote sensing images or social media data, but few studies consider both features due to the lack of available models. In our study, we propose a novel scene classification framework to identify dominant urban land use type at the level of traffic analysis zone by integrating probabilistic topic models and support vector machine. A land use word dictionary inside the framework was built by fusing natural–physical features from HSR images and socioeconomic semantic features from multisource social media data. In addition to comparing with manual interpretation data, we designed several experiments to test the land use classification accuracy of our proposed model with different combinations of previously acquired semantic features. The classification results (overall accuracy = 0.865, Kappa = 0.828) demonstrate the effectiveness of our strategy that blends features extracted from multisource geospatial data as semantic features to train the classification model. This method can be applied to help urban planners analyze fine urban structures and monitor urban land use changes, and additional data from multiple sources will be blended into this proposed framework in the future.

Subfields: Global and Planetary Change, Media Technology, Transportation

View Paper: https://www.semanticscholar.org/paper/9c29e5ecf50155ca506577e02e474b27216f6a33

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. Yao Yao
2. Zhaotang Liang
3. Qingfeng Guan
4. Jinbao Zhang
5. Ruoyu Wang
6. Zehao Yuan
7. Penghua Liu
8. Yongpan Bie

Citations in the corpus

• A human-machine adversarial scoring framework for urban perception assessment using street-view images (2019, background): “Gaode provides nine categories of POI, and this data source was successfully employed in the recognition of urban function patterns (Liu et al .”