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Exploring Land Use and Land Cover of Geotagged Social-Sensing Images Using Naive Bayes Classifier

Author

Listed:
  • Asamaporn Sitthi

    (Remote Sensing and GIS, School of Engineering and Technology, Asian Institute of Technology, P.O. Box 4, Klong Luang, Pathumthani 12120, Thailand)

  • Masahiko Nagai

    (Remote Sensing and GIS, School of Engineering and Technology, Asian Institute of Technology, P.O. Box 4, Klong Luang, Pathumthani 12120, Thailand)

  • Matthew Dailey

    (Computer Science and Information Management, School of Engineering and Technology, Asian Institute of Technology, P.O. Box 4, Klong Luang, Pathumthani 12120, Thailand)

  • Sarawut Ninsawat

    (Remote Sensing and GIS, School of Engineering and Technology, Asian Institute of Technology, P.O. Box 4, Klong Luang, Pathumthani 12120, Thailand)

Abstract

Online social media crowdsourced photos contain a vast amount of visual information about the physical properties and characteristics of the earth’s surface. Flickr is an important online social media platform for users seeking this information. Each day, users generate crowdsourced geotagged digital imagery containing an immense amount of information. In this paper, geotagged Flickr images are used for automatic extraction of low-level land use/land cover (LULC) features. The proposed method uses a naive Bayes classifier with color, shape, and color index descriptors. The classified images are mapped using a majority filtering approach. The classifier performance in overall accuracy, kappa coefficient, precision, recall, and f-measure was 87.94%, 82.89%, 88.20%, 87.90%, and 88%, respectively. Labeled-crowdsourced images were filtered into a spatial tile of a 30 m × 30 m resolution using the majority voting method to reduce geolocation uncertainty from the crowdsourced data. These tile datasets were used as training and validation samples to classify Landsat TM5 images. The supervised maximum likelihood method was used for the LULC classification. The results show that the geotagged Flickr images can classify LULC types with reasonable accuracy and that the proposed approach improves LULC classification efficiency if a sufficient spatial distribution of crowdsourced data exists.

Suggested Citation

  • Asamaporn Sitthi & Masahiko Nagai & Matthew Dailey & Sarawut Ninsawat, 2016. "Exploring Land Use and Land Cover of Geotagged Social-Sensing Images Using Naive Bayes Classifier," Sustainability, MDPI, vol. 8(9), pages 1-22, September.
  • Handle: RePEc:gam:jsusta:v:8:y:2016:i:9:p:921-:d:77874
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    References listed on IDEAS

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    1. Jacinto Estima & Marco Painho, 2014. "Photo Based Volunteered Geographic Information Initiatives: A Comparative Study of their Suitability for Helping Quality Control of Corine Land Cover," International Journal of Agricultural and Environmental Information Systems (IJAEIS), IGI Global, vol. 5(3), pages 73-89, July.
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    Cited by:

    1. Thomas Dax & Karin Schroll & Ingrid Machold & Martyna Derszniak-Noirjean & Bernd Schuh & Mailin Gaupp-Berghausen, 2021. "Land Abandonment in Mountain Areas of the EU: An Inevitable Side Effect of Farming Modernization and Neglected Threat to Sustainable Land Use," Land, MDPI, vol. 10(6), pages 1-17, June.
    2. Megersa Kebede Leta & Tamene Adugna Demissie & Jens Tränckner, 2021. "Modeling and Prediction of Land Use Land Cover Change Dynamics Based on Land Change Modeler (LCM) in Nashe Watershed, Upper Blue Nile Basin, Ethiopia," Sustainability, MDPI, vol. 13(7), pages 1-24, March.
    3. Chuanrong Zhang & Xinba Li, 2022. "Land Use and Land Cover Mapping in the Era of Big Data," Land, MDPI, vol. 11(10), pages 1-22, September.
    4. Tuqiang Zhou & Junyi Zhang & Dashzeveg Baasansuren, 2018. "A Hybrid HFACS-BN Model for Analysis of Mongolian Aviation Professionals’ Awareness of Human Factors Related to Aviation Safety," Sustainability, MDPI, vol. 10(12), pages 1-20, November.
    5. Bidur Devkota & Hiroyuki Miyazaki & Apichon Witayangkurn & Sohee Minsun Kim, 2019. "Using Volunteered Geographic Information and Nighttime Light Remote Sensing Data to Identify Tourism Areas of Interest," Sustainability, MDPI, vol. 11(17), pages 1-29, August.

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