@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 {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}
}
