Identifying Urban Functional Areas in China’s Changchun City from Sentinel-2 Images and Social Sensing Data
Abstract
:1. Introduction
2. Study Area and Data Sources
2.1. Study Area
2.2. Data Collection
2.2.1. Remote Sensing Image
2.2.2. Social Sensing Data
2.2.3. Urban Function Type Definition
3. Methodology
3.1. Sentinel 2A Data Processing and Feature Extraction
3.2. POI Data Processing and Feature Extraction
3.3. Mobile Phone Signaling Data Processing and Feature Extraction
3.4. Prediction Model
4. Results
4.1. The Overall Identification Performance of Different Combinations
4.2. Parameter Tuning of Random Forest
4.3. Spatial Layout of Urban Functional Areas in Changchun
5. Discussion
6. Conclusions
Supplementary Materials
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
- Zhou, T.; Liu, X.; Qian, Z.; Chen, H.; Tao, F. Automatic Identification of the Social Functions of Areas of Interest (AOIs) Using the Standard Hour-Day-Spectrum Approach. ISPRS Int. J. Geo-Inf. 2019, 9, 7. [Google Scholar] [CrossRef] [Green Version]
- Lin, T.; Sun, C.; Li, X.; Zhao, Q.; Zhang, G.; Ge, R.; Ye, H.; Huang, N.; Yin, K. Spatial pattern of urban functional landscapes along an urban–rural gradient: A case study in Xiamen City, China. Int. J. Appl. Earth Obs. 2016, 46, 22–30. [Google Scholar] [CrossRef]
- Chen, Y.; Liu, X.; Li, X.; Liu, X.; Yao, Y.; Hu, G.; Xu, X.; Pei, F. Delineating urban functional areas with building-level social media data: A dynamic time warping (DTW) distance based k -medoids method. Landsc. Urban Plan. 2017, 160, 48–60. [Google Scholar] [CrossRef]
- Heiden, U.; Heldens, W.; Roessner, S.; Segl, K.; Esch, T.; Mueller, A. Urban structure type characterization using hyperspectral remote sensing and height information. Landsc. Urban Plan. 2012, 105, 361–375. [Google Scholar] [CrossRef]
- Matsuoka, R.H.; Kaplan, R. People needs in the urban landscape: Analysis of Landscape and Urban Planning contributions. Landsc. Urban Plan. 2008, 84, 7–19. [Google Scholar] [CrossRef]
- Gu, C.; Hu, L.; Cook, I.G. China’s urbanization in 1949–2015: Processes and driving forces. Chin. Geogr. Sci. 2017, 27, 847–859. [Google Scholar] [CrossRef]
- Martínez, L. Health differences in an unequal city. Cities 2021, 108, 102976. [Google Scholar] [CrossRef]
- Zhang, X.; Du, S.; Wang, Q. Hierarchical semantic cognition for urban functional zones with VHR satellite images and POI data. ISPRS J. Photogramm. Remote Sens. 2017, 132, 170–184. [Google Scholar] [CrossRef]
- Li, C.; Wang, J.; Wang, L.; Hu, L.; Gong, P. Comparison of Classification Algorithms and Training Sample Sizes in Urban Land Classification with Landsat Thematic Mapper Imagery. Remote Sens. 2014, 6, 964–983. [Google Scholar] [CrossRef] [Green Version]
- Gao, S.; Janowicz, K.; Couclelis, H. Extracting urban functional regions from points of interest and human activities on location-based social networks. Trans. GIS 2017, 21, 446–467. [Google Scholar] [CrossRef]
- Herold, M.; Couclelis, H.; Clarke, K.C. The role of spatial metrics in the analysis and modeling of urban land use change. Comput. Environ. Urban. Syst. 2005, 29, 369–399. [Google Scholar] [CrossRef]
- Wu, S.; Qiu, X.; Usery, E.L.; Wang, L. Using Geometrical, Textural, and Contextual Information of Land Parcels for Classification of Detailed Urban Land Use. Ann. Am. Assoc. Geogr. 2009, 99, 76–98. [Google Scholar] [CrossRef]
- Lu, Y.; Liu, Y. Pervasive location acquisition technologies: Opportunities and challenges for geospatial studies. Comput. Environ. Urban Syst. 2012, 36, 105–108. [Google Scholar] [CrossRef]
- Liu, Y.; Liu, X.; Gao, S.; Gong, L.; Kang, C.; Zhi, Y.; Chi, G.; Shi, L. Social Sensing: A New Approach to Understanding Our Socioeconomic Environments. Ann. Am. Assoc. Geogr. 2015, 105, 512–530. [Google Scholar] [CrossRef]
- Ferreira, A.P.G.; Silva, T.H.; Loureiro, A.A.F. Uncovering spatiotemporal and semantic aspects of tourists mobility using social sensing. Comput. Commun. 2020, 160, 240–252. [Google Scholar] [CrossRef]
- Chen, Z.; Huang, X.; Arisona, S.M.; Schmitt, G.; Batty, M. Inferring building functions from a probabilistic model using public transportation data. Comput. Environ. Urban. Syst. 2014, 48, 124–137. [Google Scholar]
- Cui, H.; Wu, L.; Hu, S.; Lu, R.; Wang, S. Recognition of Urban Functions and Mixed Use Based on Residents’ Movement and Topic Generation Model: The Case of Wuhan, China. Remote Sens. 2020, 12, 2889. [Google Scholar] [CrossRef]
- Jendryke, M.; Balz, T.; McClure, S.C.; Liao, M. Putting people in the picture: Combining big location-based social media data and remote sensing imagery for enhanced contextual urban information in Shanghai. Comput. Environ. Urban Syst. 2017, 62, 99–112. [Google Scholar] [CrossRef] [Green Version]
- Dong, X.; Xu, Y.; Huang, L.; Liu, Z.; Xu, Y.; Zhang, K.; Hu, Z.; Wu, G. Exploring Impact of Spatial Unit on Urban Land Use Mapping with Multisource Data. Remote Sens. 2020, 12, 3597. [Google Scholar] [CrossRef]
- Zhang, Y.; Li, Q.; Huang, H.; Wu, W.; Du, X.; Wang, H. The Combined Use of Remote Sensing and Social Sensing Data in Fine-Grained Urban Land Use Mapping: A Case Study in Beijing, China. Remote Sens. 2017, 9, 865. [Google Scholar] [CrossRef] [Green Version]
- Wang, Y.; Gu, Y.; Dou, M.; Qiao, M. Using Spatial Semantics and Interactions to Identify Urban Functional Regions. ISPRS Int. J. Geo-Inf. 2018, 7, 130. [Google Scholar] [CrossRef] [Green Version]
- Zhang, Y.; Marshall, S.; Cao, M.; Manley, E.; Chen, H. Discovering the evolution of urban structure using smart card data: The case of London. Cities 2021, 112, 103157. [Google Scholar] [CrossRef]
- Zhen, F.; Cao, Y.; Qin, X.; Wang, B. Delineation of an urban agglomeration boundary based on Sina Weibo microblog ‘check-in’ data: A case study of the Yangtze River Delta. Cities 2017, 60, 180–191. [Google Scholar] [CrossRef]
- Long, Y.; Zhang, Y.; Cui, C. Identifying commuting pattern of Beijing using bus smart card data. Acta Geogr. Sin. 2012, 67, 1339–1352. [Google Scholar]
- Lane, N.D.; Miluzzo, E.; Lu, H.; Peeble, D.; Choudhury, T.; Campbell, A. A Survey of Mobile Phone Sensing. IEEE Commun. Mag. 2010, 48, 140–150. [Google Scholar] [CrossRef]
- Ahas, R.; Silm, S.; Järv, O.; Saluveer, E.; Tiru, M. Using Mobile Positioning Data to Model Locations Meaningful to Users of Mobile Phones. J. Urban Technol. 2010, 17, 3–27. [Google Scholar] [CrossRef]
- Sevtsuk, A.; Ratti, C. Does Urban Mobility Have a Daily Routine? Learning from the Aggregate Data of Mobile Networks. J. Urban Technol. 2010, 17, 41–60. [Google Scholar] [CrossRef]
- Alexander, L.; Jiang, S.; Murga, M.; González, M.C. Origin–destination trips by purpose and time of day inferred from mobile phone data. Transport. Res. C-Emer. 2015, 58, 240–250. [Google Scholar] [CrossRef]
- Tu, W.; Hu, Z.; Li, L.; Cao, J.; Jiang, J.; Li, Q.; Li, Q. Portraying Urban Functional Zones by Coupling Remote Sensing Imagery and Human Sensing Data. Remote Sens. 2018, 10, 141. [Google Scholar] [CrossRef] [Green Version]
- Pei, T.; Sobolevsky, S.; Ratti, C.; Shaw, S.-L.; Li, T.; Zhou, C. A new insight into land use classification based on aggregated mobile phone data. Int. J. Geogr. Inf. Sci. 2014, 28, 1988–2007. [Google Scholar] [CrossRef] [Green Version]
- Ma, Q.; Gong, Z.; Kang, J.; Tao, R.; Dang, A. Measuring Functional Urban Shrinkage with Multi-Source Geospatial Big Data: A Case Study of the Beijing-Tianjin-Hebei Megaregion. Remote Sens. 2020, 12, 2513. [Google Scholar] [CrossRef]
- Chang, S.; Jiang, Q.; Zhao, Y. Integrating CFD and GIS into the Development of Urban Ventilation Corridors: A Case Study in Changchun City, China. Sustainability 2018, 10, 1814. [Google Scholar] [CrossRef] [Green Version]
- Jia, M.; Wang, Z.; Mao, D.; Ren, C.; Wang, C.; Wang, Y. Rapid, robust, and automated mapping of tidal flats in China using time series Sentinel-2 images and Google Earth Engine. Remote Sens. Environ. 2021, 255, 112285. [Google Scholar] [CrossRef]
- Roy, D.P.; Li, J.; Zhang, H.K.; Yan, L. Best practices for the reprojection and resampling of Sentinel-2 Multi Spectral Instrument Level 1C data. Remote Sens. Lett. 2016, 7, 1023–1032. [Google Scholar] [CrossRef]
- Zong, L.; He, S.; Lian, J.; Bie, Q.; Wang, X.; Dong, J.; Xie, Y. Detailed Mapping of Urban Land Use Based on Multi-Source Data: A Case Study of Lanzhou. Remote Sens. 2020, 12, 1987. [Google Scholar] [CrossRef]
- The Sentinels Scientific Data Hub. Available online: https://scihub.copernicus.eu/ (accessed on 21 January 2021).
- Chang, S.; Wang, Z.; Mao, D.; Guan, K.; Jia, M.; Chen, C. Mapping the Essential Urban Land Use in Changchun by Applying Random Forest and Multi-Source Geospatial Data. Remote Sens. 2020, 12, 2488. [Google Scholar] [CrossRef]
- Wang, B.; Wang, L.; Liu, Y.; Yang, B.; Huang, X.; Yang, M. Characteristics of jobs-housing spatial distribution in Beijing based on mobile phone signaling data. Prog. Geog. 2020, 39, 2028–2042. [Google Scholar] [CrossRef]
- Gong, P.; Chen, B.; Li, X.; Liu, H.; Wang, J.; Bai, Y.; Chen, J.; Chen, X.; Fang, L.; Feng, S.; et al. Mapping essential urban land use categories in China (EULUC-China): Preliminary results for 2018. Sci. Bull. 2020, 65, 182–187. [Google Scholar] [CrossRef] [Green Version]
- Su, M.; Guo, R.; Chen, B.; Hong, W.; Wang, J.; Feng, Y.; Xu, B. Sampling Strategy for Detailed Urban Land Use Classification: A Systematic Analysis in Shenzhen. Remote Sens. 2020, 12, 1497. [Google Scholar] [CrossRef]
- Introduction of Sen2cor. Available online: http://step.esa.int/main/snap-supported-plugins/sen2cor/ (accessed on 22 January 2021).
- Mao, D.; Luo, L.; Wang, Z.; Wilson, M.C.; Zeng, Y.; Wu, B.; Wu, J. Conversions between natural wetlands and farmland in China: A multiscale geospatial analysis. Sci. Total Environ. 2018, 634, 550–560. [Google Scholar] [CrossRef]
- Wang, J.; Hadjikakou, M.; Bryan, B.A. Consistent, accurate, high resolution, long time-series mapping of built-up land in the North China Plain. GISci. Remote Sens. 2021, 1–17. [Google Scholar] [CrossRef]
- Mao, D.; Wang, Z.; Du, B.; Li, L.; Tian, Y.; Jia, M.; Zeng, Y.; Song, K.; Jiang, M.; Wang, Y. National wetland mapping in China: A new product resulting from object-based and hierarchical classification of Landsat 8 OLI images. ISPRS J. Photogramm. Remote Sens. 2020, 164, 11–25. [Google Scholar] [CrossRef]
- Tu, Y.; Chen, B.; Zhang, T.; Xu, B. Regional Mapping of Essential Urban Land Use Categories in China: A Segmentation-Based Approach. Remote Sens. 2020, 12, 1058. [Google Scholar] [CrossRef] [Green Version]
- Moody, A.; Johnson, D.M. Land-Surface Phenologies from AVHRR Using the Discrete Fourier Transform. Remote Sens. Environ. 2001, 75, 305–323. [Google Scholar] [CrossRef]
- Roerink, G.J.; Menenti, M.; Verhoef, W. Reconstructing cloudfree NDVI composites using Fourier analysis of time series. Int. J. Remote Sens. 2000, 21, 1911–1917. [Google Scholar] [CrossRef]
- Pastor-Guzman, J.; Dash, J.; Atkinson, P.M. Remote sensing of mangrove forest phenology and its environmental drivers. Remote Sens. Environ. 2018, 205, 71–84. [Google Scholar] [CrossRef] [Green Version]
- Collins, L.; Mccarthy, G.; Mellor, A.; Newell, G.; Smith, L. Training data requirements for fire severity mapping using Landsat imagery and random forest. Remote Sens. Environ. 2020, 245, 111839. [Google Scholar] [CrossRef]
- Ghimire, B.; Rogan, J.; Miller, J. Contextual land-cover classification: Incorporating spatial dependence in land-cover classification models using random forests and the Getis statistic. Remote Sens. Lett. 2010, 1, 45–54. [Google Scholar] [CrossRef] [Green Version]
- Kranjčić, N.; Medak, D.; Župan, R.; Rezo, M. Machine Learning Methods for Classification of the Green Infrastructure in City Areas. ISPRS Int. J. Geo-Inf. 2019, 8, 463. [Google Scholar] [CrossRef] [Green Version]
- Chawla, N.V.; Bowyer, K.W.; Hall, L.O.; Kegelmeyer, W.P. SMOTE: Synthetic Minority Over-sampling Technique. J. Artif. Intell. Res. 2002, 16, 321–357. [Google Scholar] [CrossRef]
- Blagus, R.; Lusa, L. SMOTE for high-dimensional class-imbalanced data. BMC Bioinform. 2013, 14, 106. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Sheykhmousa, M.; Mahdianpari, M.; Ghanbari, H.; Mohammadimanesh, F.; Ghamisi, P.; Homayouni, S. Support Vector Machine Versus Random Forest for Remote Sensing Image Classification: A Meta-Analysis and Systematic Review. IEEE J.-STARS 2020, 13, 6308–6325. [Google Scholar] [CrossRef]
- Hayes, M.M.; Miller, S.N.; Murphy, M.A. High-resolution landcover classification using Random Forest. Remote Sens. Lett. 2014, 5, 112–121. [Google Scholar] [CrossRef]
- Wang, Y.; Feng, L. A new hybrid feature selection based on multi-filter weights and multi-feature weights. Appl. Intell. 2019, 49, 4033–4057. [Google Scholar] [CrossRef]
- Wu, L.; Kim, S.K. Exploring the equality of accessing urban green spaces: A comparative study of 341 Chinese cities. Ecol. Indic. 2021, 121, 107080. [Google Scholar] [CrossRef]
- Pan, Z.; Wang, J. Spatially heterogeneity response of ecosystem services supply and demand to urbanization in China. Ecol. Eng. 2021, 169, 106303. [Google Scholar] [CrossRef]
- Jia, T.; Jiang, B. Exploring Human Activity Patterns Using Taxicab Static Points. ISPRS Int. J. Geo-Inf. 2012, 1, 89–107. [Google Scholar] [CrossRef] [Green Version]
- Ahas, R.; Aasa, A.; Silm, S.; Aunap, R.; Kalle, H.; Mark, Ü. Mobile Positioning in Space–Time Behaviour Studies: Social Positioning Method Experiments in Estonia. Cartogr. Geogr. Inf. Sci. 2007, 34, 259–273. [Google Scholar] [CrossRef]
Function Types | Training Samples | Testing Samples |
---|---|---|
Residential | 110 | 52 |
Commercial | 8 | 9 |
Industrial | 64 | 25 |
Public | 30 | 12 |
Green space | 40 | 17 |
Total | 252 | 115 |
Data Source | Feature Description | Feature Name | Count |
---|---|---|---|
Sentinel-2A | Mean of blue, green, red, near-infrared, and mid-infrared bands | B2_mean, B3_mean, B4_mean, B8_mean, B11_mean | 5 |
Texture mean value of blue, green, and red bands | B2_ent, B2_corr, B2_asm, B3_ent, B3_corr, B3_asm, B4_ent, B4_corr, B4_asm | 9 | |
Mean value of NDVI and NDBI | NDVI_mean, NDBI_mean | 2 | |
Baidu POI | Total POI and total POI types | POI_Num, POI_Type | 2 |
Mean kernel density of POI | Des_Res, Des_Com, Des_Ind, Des_Pub, Des_Gre | 5 | |
Mobile signaling data | The coefficients of discrete Fourier transform of weekday data | Week_c0, Week_a1, Week_a2, Week_a3, Week_a4, Week_a5, Week_b1, Week_b2, Week_b3, Week_b4, Week_b5 | 11 |
Coefficients of discrete Fourier transform for weekend data | Wend_c0, Wend_a1, Wend_a2, Wend_b1, Wend_b2 | 5 |
Combination | Feature Source |
---|---|
S1 | Sentinel-2A |
S2 | Baidu POI |
S3 | Mobile Signaling data |
S4 | Mobile Signaling data and Baidu POI |
S5 | Sentinel-2A and Baidu POI |
S6 | Sentinel-2A and Mobile Signaling data |
S7 | Sentinel-2A, Mobile Signaling data and Baidu POI |
Actual Category | |||||||
---|---|---|---|---|---|---|---|
Residential | Commercial | Industrial | Public | Green Space | Precision | ||
Predicted category | Residential | 39 | 2 | 1 | 2 | 0 | 88.64% |
Commercial | 0 | 5 | 1 | 0 | 0 | 83.33% | |
Industrial | 7 | 0 | 19 | 0 | 0 | 73.08% | |
Public | 3 | 2 | 3 | 10 | 1 | 52.63% | |
Green space | 3 | 0 | 1 | 0 | 16 | 80.00% | |
Recall | 75.00% | 55.56% | 76.00% | 83.33% | 94.12% | ||
F1 | 81.25% | 66.67% | 74.51% | 64.52% | 86.49% | ||
F1_macro = 74.69% |
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Chang, S.; Wang, Z.; Mao, D.; Liu, F.; Lai, L.; Yu, H. Identifying Urban Functional Areas in China’s Changchun City from Sentinel-2 Images and Social Sensing Data. Remote Sens. 2021, 13, 4512. https://doi.org/10.3390/rs13224512
Chang S, Wang Z, Mao D, Liu F, Lai L, Yu H. Identifying Urban Functional Areas in China’s Changchun City from Sentinel-2 Images and Social Sensing Data. Remote Sensing. 2021; 13(22):4512. https://doi.org/10.3390/rs13224512
Chicago/Turabian StyleChang, Shouzhi, Zongming Wang, Dehua Mao, Fusheng Liu, Lina Lai, and Hao Yu. 2021. "Identifying Urban Functional Areas in China’s Changchun City from Sentinel-2 Images and Social Sensing Data" Remote Sensing 13, no. 22: 4512. https://doi.org/10.3390/rs13224512