@article {94,
	title = {A CNN-Based Method of Vehicle Detection from Aerial Images Using Hard Example Mining},
	journal = {Remote Sensing},
	volume = {10},
	year = {2018},
	pages = {124},
	type = {Journal Article},
	issn = {2072-4292},
	url = {http://www.mdpi.com/2072-4292/10/1/124},
	author = {Koga, Yohei and Miyazaki, Hiroyuki and Shibasaki, Ryosuke}
}
@proceedings {88,
	title = {Counting Vehicles By Deep Neural Network In High Resolution Satellite Images},
	year = {2016},
	author = {Koga, Yohei and Miyazaki, Hiroyuki and Shibasaki, Ryosuke}
}
@booklet {66,
	title = {Crowdsourcing for urban area mapping},
	howpublished = {Asia Geospatial Digest},
	year = {2013},
	month = {2013},
	url = {http://geospatialworld.net/Paper/Application/ArticleView.aspx?aid=30462},
	author = {Kimijima, Satomi and Nagai, Masahiko and Miyazaki, Hiroyuki and Shibasaki, Ryosuke and Iwao, Koki}
}
@proceedings {78,
	title = {Crowd-Sourcing GIS for Global Urban Area Mapping},
	year = {2012},
	month = {2012},
	pages = {A4-2},
	address = {Pattaya, Thailand},
	abstract = {We present an internet-based data development, called crowd sourcing, for global urban area mapping. Crowd sourcing is an approach in which non-expert people, called crowds, join a project of producing data with simple procedures. To introducing crowd sourcing for our global urban area mapping, we constructed a crowd sourcing platform with open source GIS software and developed a ground truth data development system the platform. The data development system was for producing ground truth data by digitizing boundaries of urban area with visual interpretation of satellite images. By using the system, we successfully developed over 160,000 records of boundary data in five month. We had an experiment with operation of the system to measure working time by several sizes of work unit: 80 km {\texttimes} 80 km, 20 km {\texttimes} 20 km, and 10 km {\texttimes} 10 km. Medians of working time were 87.2, 6.2, and 1.4 hours, respectively. Our result would be helpful for estimate total working time of crowd sourcing of ground truth data by visual interpretation and would contribute to progress of data-intensive studies of geospatial information, remote sensing, and photogrammetry.},
	keywords = {ASTER, crowd sourcing, urban area mapping, Web-GIS},
	author = {Miyazaki, Hiroyuki and Kimijima, Satomi and Nagai, Masahiko and Iwao, Koki and Shibasaki, Ryosuke}
}
@conference {40,
	title = {A Crowd-Sourcing System with Web Mapping Systems for Ground Information Database of Global Urban Area Mapping},
	booktitle = {3rd Geospatial Information Forum for Students and Young Engineers},
	year = {2011},
	month = {2011},
	address = {Osaka, Japan},
	author = {Miyazaki, Hiroyuki and Iwao, Koki and Shibasaki, Ryosuke}
}
