@article {87,
	title = {Identification of Village Building via Google Earth Images and Supervised Machine Learning Methods},
	journal = {Remote Sensing},
	volume = {8},
	year = {2016},
	pages = {271},
	type = {Journal Article},
	abstract = {In this study, a method based on supervised machine learning is proposed to identify village buildings from open high-resolution remote sensing images. We select Google Earth (GE) RGB images to perform the classification in order to examine its suitability for village mapping, and investigate the feasibility of using machine learning methods to provide automatic classification in such fields. By analyzing the characteristics of GE images, we design different features on the basis of two kinds of supervised machine learning methods for classification: adaptive boosting (AdaBoost) and convolutional neural networks (CNN). To recognize village buildings via their color and texture information, the RGB color features and a large number of Haar-like features in a local window are utilized in the AdaBoost method; with multilayer trained networks based on gradient descent algorithms and back propagation, CNN perform the identification by mining deeper information from buildings and their neighborhood. Experimental results from the testing area at Savannakhet province in Laos show that our proposed AdaBoost method achieves an overall accuracy of 96.22\% and the CNN method is also competitive with an overall accuracy of 96.30\%.},
	issn = {2072-4292},
	url = {http://www.mdpi.com/2072-4292/8/4/271},
	author = {Guo, Zhiling and Shao, Xiaowei and Xu, Yongwei and Miyazaki, Hiroyuki and Ohira, Wataru and Shibasaki, Ryosuke}
}
@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 {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}
}
@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}
}
