@book {102,
	title = {Integrated Spaced-Based Geospatial System: Strengthening ASEAN{\textquoteright}s Resilience and Connectivity},
	year = {2018},
	publisher = {Economic Research Institute for ASEAN and East Asia},
	organization = {Economic Research Institute for ASEAN and East Asia},
	address = {Jakarta, Indonesia},
	abstract = {In recent decades, regional organisations have become increasingly active in connectivity disasters. This reflects a broader growing trend of intensifying regional cooperation for building resilient communities. However, the potentials of space and geospatial technology and their role in sustainable development and strengthening resilience is not clear. They can improve the efficiency and resilience of industrial operations and effectively address issues in the regional economic integration of the Association of Southeast Asian Nations (ASEAN). This report examines the possibilities and models of transborder mechanisms to deliver geospatial and space-based information from data providers to end users in disaster-affected areas, and financial schemes involving the private sector or public{\textendash}private partnerships to enable the collaborative integration of the technologies in practical ways. It provides vital information about what combinations of technologies have been applied and how they have contributed to the resilience of urban development, infrastructure planning and management, transportation management, and agricultural operations.},
	isbn = {978-602-5460-05-0},
	url = {http://www.eria.org/publications/integrated-space-based-geospatial-system-strengthening-aseans-resilience-and-connectivity/},
	author = {Shibasaki, Ryosuke and Fukuyo, Takayoshi and Miyazaki, Hiroyuki and Verspieren, Quentin and Anbumozhi, Venkatachalam}
}
@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}
}
