@inbook {137,
	title = {Management of Health- and Disaster-Related Data},
	booktitle = {Disaster Nursing, Primary Health Care and Communication in Uncertainty},
	year = {2022},
	pages = {285{\textendash}296},
	publisher = {Springer International Publishing},
	organization = {Springer International Publishing},
	address = {Cham},
	abstract = {Prolonged health emergencies and disasters greatly affect health and well-being of individuals and communities. Past experiences on extreme emergencies and disasters have taught communities the value of preparedness. Information is key in responding to health crises especially in areas where health capacity is challenged. This chapter explains the necessity of identifying appropriate health and disaster data and proposes its transformation to information needed for decision-making. It presents different examples of systems and datasets that were used for the management of response during disasters and extreme emergencies. By introducing examples from Japan and Philippines, this chapter also points out that aside from medical data, nonmedical data, such as lifestyle and hygiene information, are necessary to protect the health of disaster victims.},
	isbn = {978-3-030-98297-3},
	doi = {10.1007/978-3-030-98297-3_25},
	url = {https://doi.org/10.1007/978-3-030-98297-3_25},
	author = {Estuar, Maria Regina and Miyagawa, Shoko and Pulmano, Christian and Victorino, John Noel and Ohta, Sachiko and Miyazaki, Hiroyuki and Kanbara, Sakiko},
	editor = {Kanbara, Sakiko and Miyagawa, Shoko and Miyazaki, Hiroyuki}
}
@article {127,
	title = {Malaria rapid diagnostic test (HRP2/pLDH) positivity, incidence, care accessibility and impact of community WASH Action programme in DR Congo: mixed method study involving 625 households},
	volume = {20},
	year = {2021},
	month = {2021/02/27},
	pages = {117},
	abstract = {Malaria is one of the most prevalent and deadliest illnesses in sub-Saharan Africa. Despite recent gains made towards its control, many African countries still have endemic malaria transmission. This study aimed to assess malaria burden at household level in Kongo central province, Democratic Republic of Congo (DRC), and the impact of community participatory Water, Sanitation and Hygiene (WASH) Action programme.},
	isbn = {1475-2875},
	url = {https://doi.org/10.1186/s12936-021-03647-9},
	author = {Ngatu, Nlandu Roger and Muzembo, Basilua Andre and Choomplang, Nattadech and Kanbara, Sakiko and Wumba, Roger and Ikeda, Mitsunori and Mbelambela, Etongola Papy and Muchanga, Sifa Marie-Joelle and Suzuki, Tomoko and Wada, Koji and Al Mahfuz, Hasan and Sugishita, Tomohiko and Miyazaki, Hiroyuki and Ikeda, Shunya and Hirao, Tomohiro}
}
@article {120,
	title = {A Method for Vehicle Detection in High-Resolution Satellite Images that Uses a Region-Based Object Detector and Unsupervised Domain Adaptation},
	journal = {Remote Sensing},
	volume = {12},
	year = {2020},
	pages = {575},
	abstract = {Recently, object detectors based on deep learning have become widely used for vehicle detection and contributed to drastic improvement in performance measures. However, deep learning requires much training data, and detection performance notably degrades when the target area of vehicle detection (the target domain) is different from the training data (the source domain). To address this problem, we propose an unsupervised domain adaptation (DA) method that does not require labeled training data, and thus can maintain detection performance in the target domain at a low cost. We applied Correlation alignment (CORAL) DA and adversarial DA to our region-based vehicle detector and improved the detection accuracy by over 10\% in the target domain. We further improved adversarial DA by utilizing the reconstruction loss to facilitate learning semantic features. Our proposed method achieved slightly better performance than the accuracy achieved with the labeled training data of the target domain. We demonstrated that our improved DA method could achieve almost the same level of accuracy at a lower cost than non-DA methods with a sufficient amount of labeled training data of the target domain.},
	issn = {2072-4292},
	doi = {10.3390/rs12030575},
	url = {https://www.mdpi.com/2072-4292/12/3/575},
	author = {Koga, Yohei and Miyazaki, Hiroyuki and Shibasaki, Ryosuke}
}
@article {124,
	title = {Methodology and Application of Spatial Vulnerability Assessment for Evacuation Shelters in Disaster Planning},
	journal = {Sustainability},
	volume = {12},
	year = {2020},
	pages = {7355},
	abstract = {Evacuation shelters are the most important means for safeguarding people in hazardous areas and situations, and thus minimizing losses, particularly those due to a disaster. Therefore, evacuation shelter assignment and evacuation planning are some of the critical factors for reducing vulnerability and increasing resilience in disaster risk reduction. However, an imbalance of shelter distribution and spatial heterogeneity of a population are the critical issues limiting the accessibility of evacuation shelters in real situations. In this study, we propose a methodology for spatial assessment to reduce vulnerability and evaluate the spatial distribution of both shelter demand and resources, considering spatial accessibility. The method was applied to the case study of Mabi, in the context of a disaster caused by the 2018 flooding. We applied this approach to evaluate the area and identified the vulnerability of the evacuation shelters and the residents. The proposed method revealed that 54.55\% of the designated evacuation shelters and 59\% of the total population were physically vulnerable to the flood. The results highlight, using GIS maps, that the total shelter capacity was significantly decreased to 43.86\%. The outcome assessment addressed specific vulnerable shelters and the imbalance between the demand for and resources of each shelter. Accordingly, this study provides practical information and a valuable reference for supporting local governments and stakeholders to improve future disaster planning, prevention, and preparedness.},
	issn = {2071-1050},
	doi = {10.3390/su12187355},
	url = {https://www.mdpi.com/2071-1050/12/18/7355},
	author = {Sritart, Hiranya and Miyazaki, Hiroyuki and Kanbara, Sakiko and Hara, Takashi}
}
@inbook {106,
	title = {Measurement of Inter- and Intra-city Connectivity Using Vehicle Probe Data},
	booktitle = {Measuring Connectivity Within and Among Cities in ASEAN},
	number = {26},
	year = {2019},
	publisher = {JETRO Bangkok/IDE-JETRO},
	organization = {JETRO Bangkok/IDE-JETRO},
	chapter = {2},
	address = {Bangkok},
	abstract = {This chapter analyzes intra- and  inter-city connectivity using the vehicle probe  data  for  selected  48-hour  slots  in  March  and  September  in  2017  and  2018.  I demonstrate the potential analyses by aggregating the probe data of commercial vehicles with  overlay  to  geographical  extents  of  the  majo r  cit ies  ident ified  by  night-time  light  satellite image data.The cit ies  could  be classified  into  more  vehicles  in  the  daytime  or  night time, which were likely associated with drivers{\textquoteright} preference on traffic conditions by the time. Some cities indicated notable changes of driving speeds by the time, possibly owing to traffic condition with  people{\textquoteright}s  commuting  as  well  as  transport  infrastructure,  such as highways. More than half of the vehicles were traveling only two cities within the 48-hour periods, which were possibly shuttle trips between two cities. Some cities in the large  industrial  areas  and  inland  cities  indicated  high  proportion  of  vehicles  were  travelling more than two cities, indicating contribution to connectivity among the city.},
	keywords = {Connectivity, Probe data, Urban},
	url = {https://www.ide.go.jp/English/Publish/Download/Brc/26.html},
	author = {Hiroyuki Miyazaki}
}
@proceedings {112,
	title = {Multi-Hazard Risk Assessment in Urban Planning and Development Using AHP},
	volume = {4238},
	year = {2019},
	pages = {363{\textendash}371},
	author = {Sharma, A and Miyazaki, H}
}
@proceedings {84,
	title = {A Method for Constructing Urban Extent Map from ALOS/PALSAR Satellite Data},
	year = {2011},
	month = {2011},
	pages = {TS9-1},
	address = {Taipei},
	abstract = {Currently, global urban extent map of high accuracy and high resolution have been constructed mainly using optical sensor including ASTER/VNIR. However, there are some regions where urban areas are not correctly detected due to cloud cover and similar reflectance among land cover classes. To solve the problems, we used microwave sensor images of ALOS/PALSAR, which has an advantage in enabling observation in all weather conditions. This study aims at examining the possibility of using ALOS/PALSAR images as an alternative data resource for constructing urban extent map. Firstly, to determine useful ALOS/PALSAR observation mode, we examined how often ALOS/PALSAR images are taken in the regions for which an existing method using ASTER/VNIR images could not detect urban area correctly. Secondly, we collected ALOS/PALSAR satellite images, and examined effect of local-incident-angle-corrected images of ALOS/PALSAR taken by Fine Resolution Mode which can reduce distortion of pixel values due to local incident angle. We also performed unsupervised classifications on the ALOS/PALSAR and local-incident-angle-corrected images. Finally, we discussed ground truth datasets for image classification.},
	keywords = {ALOS/PALSAR, classification, microwave sensor, urban extent map},
	url = {http://a-a-r-s.org/acrs/index.php/acrs/acrs-overview/proceedings-1?view=publication\&task=show\&id=1005},
	author = {Itabashi, Koichiro and Miyazaki, Hiroyuki and Iwao, Koki and Nakamura, Kazuki and MATSUOKA, Masashi and Shibasaki, Ryosuke}
}
@proceedings {37,
	title = {A method for detecting and mapping Urban Area by ALOS/PALSAR data},
	year = {2010},
	month = {2010},
	address = {Hanoi, Vietnam},
	abstract = {Currently, global urban extent map of high accuracy and resolution have been constructed mainly using optical sensor including ASTER/VNIR. However, there are some regions where urban area is not detected because of cloud cover and similar reflectance among land cover classes. In the present work, by using ALOS/PALSAR, a microwave sensor, we proposed a method for detecting urban area which cannot be detected by ASTER/VNIR optical sensor and developing urban extent map in high accuracy and resolution. We mainly used satellite images taken by Fine Resolution Mode of ALOS/PALSAR. Local-incident-angle corrected images by Fine Resolution Mode were used for this method. The proposed method consists of sampling pixel values and ground truth data at urban and non-urban area from ALOS/PALSAR images; constructing classifier based on the pixel values and ground truth data; and classifying pixels into urban or non-urban area. We compared the results with urban extent map derived from ASTER/VNIR optical sensor images, and evaluated the possibility of using ALOS/PALSAR data for developing urban extent map. In addition, we examined accuracy improvement of detecting urban area using both ASTER/VNIR and ALOS/PALSAR images. The proposed method could classify regions which were misclassified by ASTER/VNIR optical sensor images, and develop urban extent map in high accuracy and resolution.},
	keywords = {ALOS PALSAR urban extent map high resolution},
	author = {Itabashi, Koichiro and Miyazaki, Hiroyuki and Iwao, Koki and Nakamura, Kazuki and Shibasaki, Ryosuke}
}
@article {MIYAZAKIHiroyuki:2009-05-08,
	title = {A Method for Developing Urban Extent Map of High Accuracy and Resolution by Integrating ASTER/VNIR Images and Existing Urban Extent Maps},
	journal = {Journal of the Japan Society of Photogrammetry and Remote Sensing},
	volume = {48},
	number = {2},
	year = {2009},
	pages = {82{\textendash}96},
	publisher = {日本写真測量学会},
	issn = {02855844},
	url = {http://ci.nii.ac.jp/naid/10025572312/en/},
	author = {Miyazaki, Hiroyuki and Tanaka, Ayako and Iwao, Koki and Shibasaki, Ryosuke}
}
