@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 {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}
}
@inbook {64,
	title = {Urban Observing Sensors},
	booktitle = {Global Urban Monitoring and Assessment through Earth Observation},
	series = {Remote Sensing Applications Series},
	year = {2014},
	note = {doi:10.1201/b17012-6},
	month = {2014},
	pages = {49-80},
	publisher = {CRC Press},
	organization = {CRC Press},
	isbn = {978-1-4665-6449-7},
	url = {http://dx.doi.org/10.1201/b17012-6},
	author = {Weng, Qihao and Gamba, Paolo and Mountrakis, Giorgos and Pesaresi, Martino and Lu, Linlin and Kemper, Thomas and Heinzel, Johannes and Xian, George and Jin, Huiran and Miyazaki, Hiroyuki and Xu, Bing and Quresh, Salman and Keramitsoglou, Iphigenia and Ban, Yifang and Esch, Thomas and Roth, Achim and Elvidge, Christopher D.}
}
