@article {140,
	title = {Impacts of disaster and land-use change on food security and adaptation: Evidence from the delta community in Bangladesh},
	journal = {International Journal of Disaster Risk Reduction},
	volume = {78},
	year = {2022},
	month = {Jan-08-2022},
	pages = {103119},
	issn = {22124209},
	doi = {10.1016/j.ijdrr.2022.103119},
	url = {https://www.sciencedirect.com/science/article/abs/pii/S2212420922003387},
	author = {Parven, Afshana and Pal, Indrajit and Witayangkurn, Apichon and Pramanik, Malay and Nagai, Masahiko and Miyazaki, Hiroyuki and Wuthisakkaroon, Chanakan}
}
@proceedings {118,
	title = {Impact of Rainfall on Urban Traffic Flow based on Probe Vehicle Data in Bangkok},
	year = {2019},
	month = {12/2019},
	address = {Chiang Mai, Thailand},
	abstract = {Adverse  weather  frequently  affects  the  capacities  and  travel  speeds  on  roadways,  which  result  in  worsened  traffic  congestion  and  incurred  productivity  loss.  Further,  with  climate  change   predicted   to   increase   rainfall   in   various   cities   in   Southeast Asia, the risk of flood damage in this region is not only anticipated  to  increase  and  affect  urban  function  but  may  also  significantly  aggravate  daily  traffic  flow.  This  study  highlighted  an analysis of the effect of rainfall on urban traffic flow through the  use  of  probe  vehicle  data  and  rainfall  data  in  the  center  of  Bangkok,  which  is  known  in  Southeast  Asia  for  problems  with  respect to maintenance of pumps and drainage channels and for many   flooded   roads   after   heavy   rainfalls.  The  experimental  results  demonstrated  that  the  average  travel  speed  decreased  by  0.02 km/hour per 1 mm of daily rainfall. In particular, at the time of  peak  traffic  demand,  the  travel  speed  was  notably  reduced  when   passengers   preferred   automobile   traffic.   In   2018,   the   economic loss estimate in central Bangkok due to annual rainfall was  approximately  0.01\%  of  the  city{\textquoteright}s  GDP.  Future  rainfall  forecast  data  makes  it  possible  to  assess  the  risk  of  climate  change on urban traffic flow.},
	keywords = {Climate  change, Probe  vehicle  data, Rainfall  impact, Regression model, Travel speed},
	url = {https://saki.siit.tu.ac.th/stud2019/uploads_final/111__18076cee1637baa6dafa754962eb2939/FinalFile_stud19_takano_v7_en.pdf},
	author = {Tsuyoshi Takano and Hiroyoshi Morita and Shinichiro Nakamura and Hiroyuki Miyazaki and Wasan Pattara-atikom and Napaporn Piamsa-nga}
}
@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}
}
@proceedings {92,
	title = {Innovative Technological Approaches for Community Resilience},
	volume = {32},
	year = {2017},
	pages = {S191-S191},
	issn = {1049-023X},
	doi = {10.1017/S1049023X17005039},
	url = {https://www.cambridge.org/core/article/innovative-technological-approaches-for-community-resilience/2CDC3530F65C3A6625D58C1E6E9EAE79},
	author = {Kanbara, Sakiko and Aharonson-Daniel, Limor and Miyazaki, Hiroyuki and Cohen, Odeya and Benin-Goren, Odeda and Yifrah, Dror and Arai, Ayumi}
}
@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}
}
