@proceedings {116,
	title = {Urban Growth Modeling using Historical Landsat Satellite Data Archive on Google Earth Engine},
	year = {2019},
	month = {12/2019},
	address = {Chiang Mai, Thailand},
	abstract = {This paper presents a pilot of data analysis for urban growth modeling using historical Landsat satellite data archive on Google Earth Engine and SLEUTH cellular automata model. The systems were organized for non-expert so that it could be useful for other applications. The developed system was applied to urban growth modeling for the cities of Hue, Ha Giang, and Vinh Yen in Viet Nam. Although the results indicated that further tuning will be needed in applying SLEUTH for urban growth modeling, the system was well established enabling users to efficiently polish the quality of the modeling results.},
	url = {https://ieeexplore.ieee.org/document/9018846},
	author = {Hiroyuki Miyazaki and Himanshu Bhushan and Kotone Wakiya}
}
@article {109,
	title = {Using Volunteered Geographic Information and Nighttime Light Remote Sensing Data to Identify Tourism Areas of Interest},
	journal = {Sustainability},
	volume = {11},
	year = {2019},
	pages = {4718},
	abstract = {Easy, economical, and near-real-time identification of tourism areas of interest is useful for tourism planning and management. Numerous studies have been accomplished to analyze and evaluate the tourism conditions of a place using free and near-real-time data sources such as social media. This study demonstrates the potential of volunteered geographic information, mainly Twitter and OpenStreetMap, for discovering tourism areas of interest. Active tweet clusters generated using Density-Based Spatial Clustering of Applications with Noise (DBSCAN) clustering algorithm and building footprint information are used to identify touristic places that ensure the availability of basic essential facilities for travelers. Furthermore, an investigation is made to examine the usefulness of nighttime light remotely sensed data to recognize such tourism areas. The study successfully discovered important tourism areas in urban and remote regions in Nepal which have relatively low social media penetration. The effectiveness of the proposed framework is examined using the F1 measure. The accuracy assessment showed F1 score of 0.72 and 0.74 in the selected regions. Hence, the outcomes of this study can provide a valuable reference for various stakeholders such as tourism planners, urban planners, and so on.},
	issn = {2071-1050},
	doi = {10.3390/su11174718},
	url = {https://www.mdpi.com/2071-1050/11/17/4718},
	author = {Devkota, Bidur and Miyazaki, Hiroyuki and Witayangkurn, Apichon and Kim, Sohee Minsun}
}
@proceedings {113,
	title = {Utilizing User Generated Contents to describe Tourism Areas of Interest},
	year = {2019},
	month = {12/2019},
	address = {Chiang Mai, Thailand},
	abstract = {The use of available place databases (like GeoNamesand traditional maps) to obtain descriptive keywords of a user defined place is not possible because such data sources mainlymaintain location definitions of the well-known places only.Traditional sources may not be updated dynamically and maynot ensure diverse information. Additionally, they do not give anyinformation on the popularity, e.g., which is more popular amongthe places indexed by the same keyword. A bottom-up approach,based on real user attention, can address these problems. Wepropose a method to describe tourism area of interest (TAOI) byaggregating user generated social media text. We match the cooccurrence of important keywords in a particular location andselect such words to describe TAOIs. We applied the proposedmethod to data on micro blogging service Twitter and photosharing service Flickr and confirmed that our method made itpossible to extract TAOI description. The recommended bottomup approach enables the extraction of valuable information thatis not possible by using traditional top-down approaches.},
	keywords = {Flickr, TFIDF, Tourism Area of Interest, Twitter, User Generated Contents},
	url = {https://ieeexplore.ieee.org/document/9018810},
	author = {Bidur Devkota and Hiroyuki Miyazaki and Niraj Pahari}
}
@article {63,
	title = {Unsupervised Global Urban Area Mapping via Automatic Labeling from ASTER and PALSAR Satellite Images},
	journal = {Remote Sensing},
	volume = {7},
	year = {2015},
	month = {2015},
	pages = {2171-2192},
	isbn = {2072-4292},
	url = {http://www.mdpi.com/2072-4292/7/2/2171},
	author = {Duan, Yulin and Shao, Xiaowei and Shi, Yun and Miyazaki, Hiroyuki and Iwao, Koki and Shibasaki, Ryosuke}
}
@inbook {64,
	title = {Urban Observing Sensors},
	booktitle = {Global Urban Monitoring and Assessment through Earth Observation},
	series = {Remote Sensing Applications Series},
	year = {2014},
	note = {doi:10.1201/b17012-6},
	month = {2014},
	pages = {49-80},
	publisher = {CRC Press},
	organization = {CRC Press},
	isbn = {978-1-4665-6449-7},
	url = {http://dx.doi.org/10.1201/b17012-6},
	author = {Weng, Qihao and Gamba, Paolo and Mountrakis, Giorgos and Pesaresi, Martino and Lu, Linlin and Kemper, Thomas and Heinzel, Johannes and Xian, George and Jin, Huiran and Miyazaki, Hiroyuki and Xu, Bing and Quresh, Salman and Keramitsoglou, Iphigenia and Ban, Yifang and Esch, Thomas and Roth, Achim and Elvidge, Christopher D.}
}
@conference {49,
	title = {Urban Extent Mapping with Textural Information of Satellite Image},
	booktitle = {18th IIS Forum on Broad-Scale Collection and Application of Environment and Disaster Risk Information},
	year = {2009},
	month = {2009},
	address = {Tokyo, Japan},
	author = {Miyazaki, Hiroyuki and Iwao, Koki and Shibasaki, Ryosuke}
}
