@article {32,
	title = {An Automated Method for Global Urban Area Mapping by Integrating ASTER Satellite Images and GIS Data},
	journal = {Selected Topics in Applied Earth Observations and Remote Sensing, IEEE Journal of},
	volume = {6},
	year = {2013},
	month = {30 November 2012},
	chapter = {1004-1019},
	abstract = {We present an automated classification method for global urban area mapping by integrating satellite images taken by Visible and Near-Infrared Radiometer of Advanced Spaceborne Thermal Emission and Reflection radiometer (ASTER/VNIR) and GIS data derived from existing urban area maps. The method consists of two steps. First, we extracted urban areas from ASTER/VNIR satellite images by using an iterative machine-learning classification method known as Learning with Local and Global Consistency (LLGC). This method is capable of automatically performing classification with a noisy training dataset, in our case, low-resolution urban maps. Therefore, we were able to perform supervised classification of ASTER/VNIR images without using labor-intensive visual interpretation. Second, we integrated the LLGC confidence map with other maps by logistic regression. The logistic regression complemented misclassifications in the LLGC map and provided useful information for further improvement of the model. In an experiment including 194 scenes of ASTER/VNIR images, the integrated maps were developed at a resolution of 15 m resolution, which is much finer than existing maps with resolutions of 300 to 1000 m. The maps achieved an overall accuracy of 90.0\% and a kappa coefficient of 0.565, both of which are higher than or almost equal to the values for major existing global urban area maps.},
	keywords = {ASTER/VNIR, Learning with Local and Global Consistency, logistic regression, urban area mapping},
	issn = {1939-1404},
	doi = {10.1109/JSTARS.2012.2226563},
	author = {Miyazaki, H and Shao, X. and Iwao, K. and Shibasaki, R.}
}
@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}
}
@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}
}
