@proceedings {90,
	title = {An Automated Method for Time-Series Human Settlement Mapping using Landsat Data and Existing Land Cover Maps},
	year = {2016},
	author = {Miyazaki, H. and Nagai, M. and Shibasaki, R.}
}
@proceedings {89,
	title = {Development of an automated system for building detection from high-resolution satellite images},
	year = {2016},
	doi = {10.1109/EORSA.2016.7552806},
	author = {Miyazaki, H. and Kuwata, K. and Ohira, W. and Guo, Z. and Shao, X. and Xu, Y. and Shibasaki, R.}
}
@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.}
}
