@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 {36,
	title = {Development of a Global Built-Up Area Map Using ASTER Satellite Images and Existing GIS Data},
	booktitle = {Global Urban Monitoring and Assessment through Earth Observation},
	year = {2014},
	pages = {121},
	publisher = {CRC Press},
	organization = {CRC Press},
	chapter = {7},
	address = {London},
	isbn = {1466564490},
	author = {Miyazaki, Hiroyuki and Shao, Xiaowei and Iwao, Koki and Shibasaki, Ryosuke}
}
@proceedings {68,
	title = {Development of High-Resolution Population Database from ASTER-Derived Urban Area Map},
	year = {2014},
	month = {2014},
	address = {Changsha, China},
	isbn = {978-1-4799-5757-6},
	doi = {10.1109/EORSA.2014.6927879},
	url = {https://ieeexplore.ieee.org/document/6927879},
	author = {Miyazaki, Hiroyuki and Iwao, Koki and Shibasaki, Ryosuke},
	editor = {Weng, Qihao and Zeng, Siqi and Zhu, Jianjun and Zhu, Chuanqu}
}
@proceedings {67,
	title = {Building High-Resolution Population Dataset with ASTER-derived Urban Area Map and Existing Population Data},
	year = {2013},
	month = {2013},
	address = {Bali, Indonesia},
	author = {Miyazaki, Hiroyuki and Iwao, Koki 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 {Miyazaki2012,
	title = {Automated Construction of Coverage Catalogues of ASTER Satellite Image for Urban Areas of the World},
	volume = {XXXIX},
	number = {B8},
	year = {2012},
	pages = {497{\textendash}500},
	abstract = {We developed an algorithm to determine a combination of satellite images according to observation extent and image quality. The algorithm was for testing necessity for completing coverage of the search extent. The tests excluded unnecessary images with low quality and preserve necessary images with good quality. The search conditions of the satellite images could be extended, indicating the catalogue could be constructed with specified periods required for time series analysis. We applied the method to a database of metadata of ASTER satellite images archived in GEO Grid of National Institute of Advanced Industrial Science and Technology (AIST), Japan. As indexes of populated places with geographical coordinates, we used a database of 3372 populated place of more than 0.1 million populations retrieved from GRUMP Settlement Points, a global gazetteer of cities, which has geographical names of populated places associated with geographical coordinates and population data. From the coordinates of populated places, 3372 extents were generated with radiuses of 30 km, a half of swath of ASTER satellite images. By merging extents overlapping each other, they were assembled into 2214 extents. As a result, we acquired combinations of good quality for 1244 extents, those of low quality for 96 extents, incomplete combinations for 611 extents. Further improvements would be expected by introducing pixel- based cloud assessment and pixel value correction over seasonal variations.},
	keywords = {ASTER, coverage catalogue, gazetteer, metadata database, urban area},
	author = {Miyazaki, Hiroyuki and Iwao, Koki and Shibasaki, Ryosuke}
}
@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}
}
@article {Miyazaki2011,
	title = {Development of a New Ground Truth Database for Global Urban Area Mapping from a Gazetteer},
	journal = {Remote Sensing},
	volume = {3},
	number = {6},
	year = {2011},
	pages = {1177{\textendash}1187},
	address = {Taipei},
	abstract = {We present a practical system for collecting ground truth data needed for global urban area mapping. To collect visually interpreted information effectively, we implemented crowd sourcing, with which the operators collect and send very accurate data generated by visual interpretation through the Internet. The system was constructed with Web Map Service (WMS) and Web Feature Service (WFS), which are standardized scheme of publishing and updating map data over the Internet. Within the system, the conductor and the operators of visual interpretation campaign would save labor and time for transferring data, keeping consistency, and assuring data security. In addition to that, owing to the standardized scheme, the system was flexibly extensible and transferrable to the other purpose. We regard that the system would contribute to improve ground information for global urban area mapping as well as statistical investigation on precision of visual interpretations. 1. INTRODUCTION Global urban area maps, which represent location, area and shape of urban area of the world,},
	keywords = {crowd sourcing, global urban, ground truth database, web feature service, web mapping service},
	issn = {2072-4292},
	doi = {10.3390/rs3061177},
	url = {http://www.mdpi.com/2072-4292/3/6/1177/},
	author = {Miyazaki, Hiroyuki and Iwao, Koki and Shibasaki, Ryosuke}
}
@proceedings {Miyazaki2011a,
	title = {High-Resolution Urban Area Map for 3372 Cities of the World},
	year = {2011},
	pages = {PS{\textendash}3},
	address = {Taipei},
	abstract = {We developed an automated method of image selection and urban area mapping for developing high-resolution global urban area maps. The method was successfully implemented and applied to 3372 cities of more than 0.1 million people of the world. As the result, the algorithm of image selection determined 11802 scenes of ASTER/VNIR satellite images and yielded good combinations for more than 60\% of the cities. For the merged satellite images with the determined combinations, we applied the automated method of urban area mapping in high resolution. The method was consist of semi-supervised classifications by a machine learning method, called Learning with Local and Global Consistency (LLGC), and integrating the LLGC-derived maps and existing maps by logistic regression. As the result, we acquired urban area maps of 15-m resolution originated from ASTER/VNIR images, which is much finer than 500-m resolution of existing urban area maps. The method had still much space to be improved, especially in avoiding cloud contaminations. However, the method would contribute to complete high-resolution urban area maps of the world and realizing Global Earth Observation Systems of System (GEOSS).},
	keywords = {ASTER, automated image selection, automated urban area mapping, global urban area map, Learning with Local and Global Consistency, logistic regression, multi-source classification},
	author = {Miyazaki, Hiroyuki and Itabashi, Koichiro and Shao, Xiaowei and Iwao, Koki and Shibasaki, Ryosuke}
}
@proceedings {84,
	title = {A Method for Constructing Urban Extent Map from ALOS/PALSAR Satellite Data},
	year = {2011},
	month = {2011},
	pages = {TS9-1},
	address = {Taipei},
	abstract = {Currently, global urban extent map of high accuracy and high resolution have been constructed mainly using optical sensor including ASTER/VNIR. However, there are some regions where urban areas are not correctly detected due to cloud cover and similar reflectance among land cover classes. To solve the problems, we used microwave sensor images of ALOS/PALSAR, which has an advantage in enabling observation in all weather conditions. This study aims at examining the possibility of using ALOS/PALSAR images as an alternative data resource for constructing urban extent map. Firstly, to determine useful ALOS/PALSAR observation mode, we examined how often ALOS/PALSAR images are taken in the regions for which an existing method using ASTER/VNIR images could not detect urban area correctly. Secondly, we collected ALOS/PALSAR satellite images, and examined effect of local-incident-angle-corrected images of ALOS/PALSAR taken by Fine Resolution Mode which can reduce distortion of pixel values due to local incident angle. We also performed unsupervised classifications on the ALOS/PALSAR and local-incident-angle-corrected images. Finally, we discussed ground truth datasets for image classification.},
	keywords = {ALOS/PALSAR, classification, microwave sensor, urban extent map},
	url = {http://a-a-r-s.org/acrs/index.php/acrs/acrs-overview/proceedings-1?view=publication\&task=show\&id=1005},
	author = {Itabashi, Koichiro and Miyazaki, Hiroyuki and Iwao, Koki and Nakamura, Kazuki and MATSUOKA, Masashi and Shibasaki, Ryosuke}
}
@conference {43,
	title = {A Study on Global Urban Area Mapping using Satellite Data},
	booktitle = {20th IIS Forum on Broad-Scale Collection and Application of Environment and Disaster Risk Information},
	year = {2011},
	month = {2011},
	address = {Tokyo, Japan},
	author = {Miyazaki, Hiroyuki and Iwao, Koki and Shibasaki, Ryosuke}
}
@conference {44,
	title = {Automatic Mapping of Urban Area in High Resolution with ASTER satellite images},
	booktitle = {19th IIS Forum on Broad-Scale Collection and Application of Environment and Disaster Risk Information},
	year = {2010},
	month = {2010},
	address = {Tokyo, Japan},
	author = {Miyazaki, Hiroyuki and Shao, Xiaowei and Iwao, Koki and Shibasaki, Ryosuke}
}
@proceedings {Miyazaki2010,
	title = {Automatic Mapping of Urban Area in High Resolution with LLGC and Integration with Existing Urban Area Maps},
	year = {2010},
	pages = {1{\textendash}9},
	address = {Guimaraes, Portugal},
	abstract = {We present development of automated algorithm for mapping global urban area in high resolution using ASTER satellite images and coarse-resolution urban area maps. The algorithm consists of two steps: classifying pixels of ASTER satellite images into urban or non-urban by Learning with Global and Local Consistency (LLGC) technique; and integration with existing urban area maps using logistic regression. We implemented the algorithm and demonstrated it against 340 scenes of ASTER satellite images. LLGC trimmed up 500-m-resolution clusters of urban area into 15-m-resoluton clusters. However accuracy assessment on LLGC result showed 75\% user{\textquoteright}s accuracy, 41\% producer{\textquoteright}s accuracy, 94\% overall accuracy and 0.50 kappa coefficient, indicating LLGC had considerable misclassifications due to similarity in surface reflectance among non-vegetative land cover. To complement the misclassifications, we integrated LLGC result with existing urban area maps. Accuracy assessment on result of the integration showed 74\% user{\textquoteright}s accuracy, 43\% producer{\textquoteright}s accuracy, 94\% overall accuracy and 0.51 kappa coefficient, indicating that the results were more accurate than LLGC result and existing urban area maps. We concluded our method would improve global urban area map not only in terms of spatial resolution, but also in that of accuracy.},
	keywords = {ASTER, land cover classification, urban area mapping},
	author = {Miyazaki, Hiroyuki and Shao, Xiaowei and Iwao, Koki and Shibasaki, Ryosuke}
}
@proceedings {Miyazaki2010a,
	title = {Global Urban Area Mapping in High Resolution using ASTER Satellite Images},
	volume = {XXXVIII},
	year = {2010},
	pages = {847{\textendash}852},
	address = {Kyoto},
	abstract = {We present development of automated algorithm for mapping global urban area in high resolution using ASTER satellite images and coarse-resolution urban area maps. The algorithm consists of two steps: classifying pixels of ASTER satellite images into urban or non-urban by Learning with Global and Local Consistency (LLGC) technique; and integration with existing urban area maps using logistic regression. We implemented the algorithm and demonstrated it against 340 scenes of ASTER satellite images. LLGC trimmed up 500-m-resolution clusters of urban area into 15-m-resoluton clusters. However accuracy assessment on LLGC result showed 75\% user{\textquoteright}s accuracy, 41\% producer{\textquoteright}s accuracy, 94\% overall accuracy and 0.50 kappa coefficient, indicating LLGC had considerable misclassifications due to similarity in surface reflectance among non-vegetative land cover. To complement the misclassifications, we integrated LLGC result with existing urban area maps. Accuracy assessment on result of the integration showed 74\% user{\textquoteright}s accuracy, 43\% producer{\textquoteright}s accuracy, 94\% overall accuracy and 0.51 kappa coefficient, indicating that the results were more accurate than LLGC result and existing urban area maps. We concluded our method would improve global urban area map not only in terms of spatial resolution, but also in that of accuracy.},
	keywords = {ASTER, land cover classification, urban area mapping},
	author = {Miyazaki, Hiroyuki and Shao, Xiaowei and Iwao, Koki and Shibasaki, Ryosuke}
}
@proceedings {Miyazaki2010d,
	title = {Global Urban Area Mapping using Global ASTER Satellite Images},
	year = {2010},
	pages = {TS07{\textendash}2},
	address = {Hanoi},
	abstract = {We present development of global urban area map using ASTER satellite images, which has much higher resolution than that of existing global urban area maps. To complete the map for world{\textquoteright}s cities, we developed automated algorithm for mapping urban area in high resolution. The algorithm consists of two steps: classifying pixels of ASTER satellite images into urban or non-urban by Learning with Global and Local Consistency (LLGC) technique; and integration with existing urban area maps using logistic regression. We implemented the algorithm and demonstrated it on 775 scenes of ASTER satellite images. LLGC classified pixels of ASTER satellite images into urban or non-urban in 15-m resolution, though it had considerable amount of misclassification due to similarity in surface reflectance among non-vegetative land cover. To complement the misclassifications, we integrated LLGC results with existing urban area maps using logistic regression. The misclassifications were corrected well, especially in dry zone. We also developed ground truth database using global gazetteer of world{\textquoteright}s cities so that we conduct comprehensive accuracy assessment on the urban area map. We visually interpreted land cover of urban or non-urban on 3734 points coordinates derived from global gazetteer, and combined them with 4211 data points of Degree Confluence Project into a database, which had 2185 data points of urban and 5559 of non-urban. Accuracy assessment using the database indicates that our map is more accurate than existing urban area maps. Finally, we applied the method on broad coverage of ASTER satellite images rather than single one scene. The result showed quite well classification as a whole, indicating possibility of developing global urban area map in high-resolution; however considerable problems due to availability of cloud-free satellite image is still remained.},
	keywords = {ASTER, cover classification, high resolution, land, LLGC, logistic regression, urban area mapping},
	author = {Miyazaki, Hiroyuki and Shao, Xiaowei and Iwao, Koki and Shibasaki, Ryosuke}
}
@proceedings {54,
	title = {High-Resolution Urban Area Mapping of World{\textquoteright}s Cities Using Satellite Images},
	volume = {19},
	year = {2010},
	month = {2010},
	pages = {1C-3},
	address = {Kyoto, Japan},
	author = {Miyazaki, Hiroyuki and Iwao, Koki and Shibasaki, Ryosuke}
}
@proceedings {37,
	title = {A method for detecting and mapping Urban Area by ALOS/PALSAR data},
	year = {2010},
	month = {2010},
	address = {Hanoi, Vietnam},
	abstract = {Currently, global urban extent map of high accuracy and resolution have been constructed mainly using optical sensor including ASTER/VNIR. However, there are some regions where urban area is not detected because of cloud cover and similar reflectance among land cover classes. In the present work, by using ALOS/PALSAR, a microwave sensor, we proposed a method for detecting urban area which cannot be detected by ASTER/VNIR optical sensor and developing urban extent map in high accuracy and resolution. We mainly used satellite images taken by Fine Resolution Mode of ALOS/PALSAR. Local-incident-angle corrected images by Fine Resolution Mode were used for this method. The proposed method consists of sampling pixel values and ground truth data at urban and non-urban area from ALOS/PALSAR images; constructing classifier based on the pixel values and ground truth data; and classifying pixels into urban or non-urban area. We compared the results with urban extent map derived from ASTER/VNIR optical sensor images, and evaluated the possibility of using ALOS/PALSAR data for developing urban extent map. In addition, we examined accuracy improvement of detecting urban area using both ASTER/VNIR and ALOS/PALSAR images. The proposed method could classify regions which were misclassified by ASTER/VNIR optical sensor images, and develop urban extent map in high accuracy and resolution.},
	keywords = {ALOS PALSAR urban extent map high resolution},
	author = {Itabashi, Koichiro and Miyazaki, Hiroyuki and Iwao, Koki and Nakamura, Kazuki and Shibasaki, Ryosuke}
}
@proceedings {Miyazaki2009c,
	title = {Assessing Validity of Global Gazetteers as Ground Truth Data for Urban Mapping},
	year = {2009},
	pages = {TS17{\textendash}01},
	address = {Beijing},
	author = {Miyazaki, Hiroyuki and Iwao, Koki and Shibasaki, Ryosuke}
}
@conference {52,
	title = {Assessment of Usability of Gazetteer as Ground Truth Data},
	booktitle = {Japan Society of Photogrammetry and Remote Sensing Spring Meeting},
	year = {2009},
	month = {2009},
	pages = {I-1},
	address = {Yokohama, Japan},
	author = {Miyazaki, Hiroyuki and Iwao, Koki and Shibasaki, Ryosuke}
}
@conference {57,
	title = {Developing Global Urban Extent Map of High Resolution with ASTER Satellite Images},
	booktitle = {7th International Conference on Urban Climate},
	year = {2009},
	month = {2009},
	pages = {4-16},
	address = {Yokohama, Japan},
	author = {Miyazaki, Hiroyuki and Iwao, Koki and Shibasaki, Ryosuke}
}
@conference {42,
	title = {Development of New Urban Extent Map with Integration of ASTER Satellite Images and Existing Urban Extent Maps},
	booktitle = {1st Geoinformation Student Forum in Kansai},
	year = {2009},
	month = {2009},
	address = {Kyoto, Japan},
	author = {Miyazaki, Hiroyuki and Iwao, Koki and Shibasaki, Ryosuke}
}
@article {MIYAZAKIHiroyuki:2009-05-08,
	title = {A Method for Developing Urban Extent Map of High Accuracy and Resolution by Integrating ASTER/VNIR Images and Existing Urban Extent Maps},
	journal = {Journal of the Japan Society of Photogrammetry and Remote Sensing},
	volume = {48},
	number = {2},
	year = {2009},
	pages = {82{\textendash}96},
	publisher = {日本写真測量学会},
	issn = {02855844},
	url = {http://ci.nii.ac.jp/naid/10025572312/en/},
	author = {Miyazaki, Hiroyuki and Tanaka, Ayako and Iwao, Koki and Shibasaki, Ryosuke}
}
@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}
}
@conference {50,
	title = {Validation on Availability of Gazetteer for Global Urban Mapping},
	booktitle = {16th Remote Sensing Forum, Society of Instrument and Control Engineers},
	year = {2009},
	month = {2009},
	address = {Tokyo, Japan},
	author = {Miyazaki, Hiroyuki and Iwao, Koki and Shibasaki, Ryosuke}
}
@proceedings {55,
	title = {Developing Urban Extent Map of High Accuracy and Resolution by Integrating ASTER satellite Images and Existing Urban Extent Maps},
	volume = {17},
	year = {2008},
	month = {2008},
	pages = {89-92},
	address = {Tokyo, Japan},
	author = {Miyazaki, Hiroyuki and Iwao, Koki and Shibasaki, Ryosuke}
}
@conference {51,
	title = {Development of New Urban Extent Map with lntegration of ASTER/VNIR Classified Map and Existing Urban Extent Maps},
	booktitle = {ASTER Workshop - ASTER Science Team Meeting},
	year = {2008},
	month = {2008},
	pages = {10-11},
	address = {Tokyo, Japan},
	author = {Miyazaki, Hiroyuki and Iwao, Koki and Shibasaki, Ryosuke}
}
@proceedings {53,
	title = {Study on Automatic Global Urban Mapping Method using Satellite Imagery and the Existing Land Cover Data},
	year = {2008},
	month = {2008},
	pages = {S-4},
	address = {Yokohama, Japan},
	author = {Miyazaki, Hiroyuki and Tanaka, Ayako and Iwao, Koki and Shibasaki, Ryosuke}
}
