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

Asamaporn Sitthi, Masahiko Nagai, Matthew Dailey and Sarawut Ninsawat
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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

Sustainability, 2016, vol. 8, issue 9, 1-22

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.

Keywords: social sensing; flickr images; naive Bayes classifier; land use/land cover (LULC) feature extraction; image segmentation; majority distribution (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2016
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)

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