Decision-Level and Feature-Level Integration of Remote Sensing and Geospatial Big Data for Urban Land Use Mapping

Information about urban land use is important for urban planning and sustainable development. The emergence of geospatial big data (GBD), increased the availability of remotely sensed (RS) data and the development of new methods for data integration to provide new opportunities for mapping types of urban land use. However, the modes of RS and GBD integration are diverse due to the differences in data, study areas, classifiers, etc. In this context, this study aims to summarize the main methods of data integration and evaluate them via a case study of urban land use mapping in Hangzhou, China. We first categorized the RS and GBD integration methods into decision-level integration (DI) and feature-level integration (FI) and analyzed their main differences by reviewing the existing literature. The two methods were then applied for mapping urban land use types in Hangzhou city, based on urban parcels derived from the OpenStreetMap (OSM) road network, 10 m Sentinel-2A images, and points of interest (POI). The corresponding classification results were validated quantitatively and qualitatively using the same testing dataset. Finally, we illustrated the advantages and disadvantages of both approaches via bibliographic evidence and quantitative analysis. The results showed that: (1) The visual comparison indicates a generally better performance of DI-based classification than FI-based classification; (2) DI-based urban land use mapping is easy to implement, while FI-based land use mapping enables the mixture of features; (3) DI-based and FI-based methods can be used together to improve urban land use mapping, as they have different performances when classifying different types of land use. This study provides an improved understanding of urban land use mapping in terms of the RS and GBD integration strategy.

[1]  Ana Alves,et al.  POI Mining for Land Use Classification: A Case Study , 2020, ISPRS Int. J. Geo Inf..

[2]  Xiaoping Liu,et al.  High-resolution multi-temporal mapping of global urban land using Landsat images based on the Google Earth Engine Platform , 2018 .

[3]  Chao Wang,et al.  Annual large-scale urban land mapping based on Landsat time series in Google Earth Engine and OpenStreetMap data: A case study in the middle Yangtze River basin , 2020 .

[4]  D. Lu,et al.  Use of impervious surface in urban land-use classification , 2006 .

[5]  Giles M. Foody,et al.  Key issues in rigorous accuracy assessment of land cover products , 2019, Remote Sensing of Environment.

[6]  P. Jarreau,et al.  Social media for social change in science , 2018, Science.

[7]  Mei Liu,et al.  A New Remote Sensing Images and Point-of-Interest Fused (RPF) Model for Sensing Urban Functional Regions , 2020, Remote. Sens..

[8]  Karen C. Seto,et al.  A systematic review and assessment of algorithms to detect, characterize, and monitor urban land change , 2020 .

[9]  D. H. Card.  Using known map category marginal frequencies to improve estimates of thematic map accuracy , 1982 .

[10]  Rozenn Dahyot,et al.  Automatic Discovery and Geotagging of Objects from Street View Imagery , 2017, Remote. Sens..

[11]  Li Bijun,et al.  Extracting hierarchical landmarks from urban POI data , 2011, National Remote Sensing Bulletin.

[12]  Jianping Wu,et al.  Integration of nighttime light remote sensing images and taxi GPS tracking data for population surface enhancement , 2018, Int. J. Geogr. Inf. Sci..

[13]  Xiaohua Tong,et al.  Updating of Land Cover Maps and Change Analysis Using GlobeLand30 Product: A Case Study in Shanghai Metropolitan Area, China , 2020, Remote. Sens..

[14]  Gloria Bordogna,et al.  Knowledge and Data-Driven Mapping of Environmental Status Indicators from Remote Sensing and VGI , 2020, Remote. Sens..

[15]  C. Ratti,et al.  Mobile Landscapes: Using Location Data from Cell Phones for Urban Analysis , 2006 .

[16]  Qihao Weng,et al.  Remote sensing of impervious surfaces in the urban areas: Requirements, methods, and trends , 2012 .

[17]  Peng Gong,et al.  Mapping Urban Land Use by Using Landsat Images and Open Social Data , 2016, Remote. Sens..

[18]  T. Carlson,et al.  On the relation between NDVI, fractional vegetation cover, and leaf area index , 1997 .

[19]  Jun Li,et al.  Social Media: New Perspectives to Improve Remote Sensing for Emergency Response , 2017, Proceedings of the IEEE.

[20]  Wei Tu,et al.  Portraying Urban Functional Zones by Coupling Remote Sensing Imagery and Human Sensing Data , 2018, Remote. Sens..

[21]  Giles M. Foody,et al.  Status of land cover classification accuracy assessment , 2002 .

[22]  B. Johnson,et al.  Integrating OpenStreetMap crowdsourced data and Landsat time-series imagery for rapid land use/land cover (LULC) mapping: Case study of the Laguna de Bay area of the Philippines , 2016 .

[23]  Shihong Du,et al.  Heuristic sample learning for complex urban scenes: Application to urban functional-zone mapping with VHR images and POI data , 2020 .

[24]  Chaogui Kang,et al.  Social Sensing: A New Approach to Understanding Our Socioeconomic Environments , 2015 .

[25]  Ricardo J Soares Magalhães,et al.  Land cover change during a period of extensive landscape restoration in Ningxia Hui Autonomous Region, China. , 2017, The Science of the total environment.

[26]  Patrick Hostert,et al.  Remote sensing and geospatial technologies in support of a normative land system science: status and prospects , 2019, Current Opinion in Environmental Sustainability.

[27]  Jianghao Wang,et al.  Urban Land Use Mapping by Combining Remote Sensing Imagery and Mobile Phone Positioning Data , 2018, Remote. Sens..

[28]  Hui Zhang,et al.  Urban Land Use and Land Cover Classification Using Multisource Remote Sensing Images and Social Media Data , 2019, Remote. Sens..

[29]  Hernán D. Rozenfeld,et al.  The Area and Population of Cities: New Insights from a Different Perspective on Cities , 2009, 1001.5289.

[30]  Jiasong Zhu,et al.  Deep learning-based remote and social sensing data fusion for urban region function recognition , 2020 .

[31]  Noor Shaila Sarmin,et al.  A Review of Potentialities and Challenges of Integrating Remote Sensing and GIS with Socioeconomic Data , 2016 .

[32]  William J. Emery,et al.  Exploring semantic elements for urban scene recognition: Deep integration of high-resolution imagery and OpenStreetMap (OSM) , 2019, ISPRS Journal of Photogrammetry and Remote Sensing.

[33]  Xingjian Liu,et al.  Automated identification and characterization of parcels (AICP) with OpenStreetMap and Points of Interest , 2013, ArXiv.

[34]  K. Battle,et al.  A global map of travel time to cities to assess inequalities in accessibility in 2015 , 2018, Nature.

[35]  A. Harti,et al.  Recent advances in the use of public domain satellite imagery for mineral exploration: A review of Landsat-8 and Sentinel-2 applications , 2020 .

[36]  C. Woodcock,et al.  Improvement and expansion of the Fmask algorithm: cloud, cloud shadow, and snow detection for Landsats 4–7, 8, and Sentinel 2 images , 2015 .

[37]  N. Hamm,et al.  Unbalanced Development Characteristics and Driving Mechanisms of Regional Urban Spatial Form: A Case Study of Jiangsu Province, China , 2021, Sustainability.

[38]  Jon Atli Benediktsson,et al.  Spatial technology and social media in remote sensing: challenges and opportunities [point of view] , 2017, Proc. IEEE.

[39]  Hongyang Lu,et al.  Classification of High-Resolution Remote-Sensing Image Using OpenStreetMap Information , 2017, IEEE Geoscience and Remote Sensing Letters.

[40]  Yuqi Bai,et al.  Mapping essential urban land use categories in China (EULUC-China): preliminary results for 2018. , 2020, Science bulletin.

[41]  Bo Huang,et al.  Using multi-source geospatial big data to identify the structure of polycentric cities , 2017 .

[42]  Matthias Drusch,et al.  Sentinel-2: ESA's Optical High-Resolution Mission for GMES Operational Services , 2012 .

[43]  Rob Kitchin,et al.  What makes Big Data, Big Data? Exploring the ontological characteristics of 26 datasets , 2016, Big Data Soc..

[44]  Bing Xu,et al.  Sampling Strategy for Detailed Urban Land Use Classification: A Systematic Analysis in Shenzhen , 2020, Remote. Sens..

[45]  Giles M. Foody,et al.  Good practices for estimating area and assessing accuracy of land change , 2014 .

[46]  Bin Jiang,et al.  Geospatial Big Data Handling Theory and Methods: A Review and Research Challenges , 2015, ArXiv.

[47]  Hermann Kaufmann,et al.  Determination of robust spectral features for identification of urban surface materials in hyperspectral remote sensing data , 2007 .

[48]  Zhe Zhu,et al.  Understanding an urbanizing planet: Strategic directions for remote sensing , 2019, Remote Sensing of Environment.

[49]  Pengbo Liu,et al.  A Methodology to Monitor Urban Expansion and Green Space Change Using a Time Series of Multi-Sensor SPOT and Sentinel-2A Images , 2019, Remote. Sens..

[50]  Li Hou,et al.  Comparison of Machine-Learning Methods for Urban Land-Use Mapping in Hangzhou City, China , 2020, Remote. Sens..

[51]  R. Ilieva,et al.  Social-media data for urban sustainability , 2018, Nature Sustainability.

[52]  Qunying Huang,et al.  Integrating big social data, computing and modeling for spatial social science , 2016 .

[53]  Xiaoping Liu,et al.  Classifying urban land use by integrating remote sensing and social media data , 2017, Int. J. Geogr. Inf. Sci..

[54]  Wei Zhang,et al.  40-Year (1978-2017) human settlement changes in China reflected by impervious surfaces from satellite remote sensing. , 2019, Science bulletin.

[55]  Xiao Xiang Zhu,et al.  Model Fusion for Building Type Classification from Aerial and Street View Images , 2019, Remote. Sens..

[56]  Devis Tuia,et al.  Fine-grained landuse characterization using ground-based pictures: a deep learning solution based on globally available data , 2018, Int. J. Geogr. Inf. Sci..

[57]  Xin Du,et al.  The Combined Use of Remote Sensing and Social Sensing Data in Fine-Grained Urban Land Use Mapping: A Case Study in Beijing, China , 2017, Remote. Sens..

[58]  Ying Wang,et al.  Urban Observation: Integration of Remote Sensing and Social Media Data , 2019, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[59]  Anne Puissant,et al.  The utility of texture analysis to improve per‐pixel classification for high to very high spatial resolution imagery , 2005 .

[60]  Yu Liu,et al.  A framework for mixed-use decomposition based on temporal activity signatures extracted from big geo-data , 2018, Int. J. Digit. Earth.

[61]  Tao Lin,et al.  Mapping Urban Functional Zones by Integrating Very High Spatial Resolution Remote Sensing Imagery and Points of Interest: A Case Study of Xiamen, China , 2018, Remote. Sens..

[62]  Yu Liu,et al.  Integrating multi-source big data to infer building functions , 2017, Int. J. Geogr. Inf. Sci..

[63]  Ming Wu,et al.  Examining Land Cover and Greenness Dynamics in Hangzhou Bay in 1985-2016 Using Landsat Time-Series Data , 2017, Remote. Sens..

[64]  Xiao Xiang Zhu,et al.  Building Instance Classification Using Street View Images , 2018, ISPRS Journal of Photogrammetry and Remote Sensing.

[65]  Qingquan Li,et al.  Functional urban land use recognition integrating multi-source geospatial data and cross-correlations , 2019, Comput. Environ. Urban Syst..