<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>5</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Estuar, Maria Regina</style></author><author><style face="normal" font="default" size="100%">Miyagawa, Shoko</style></author><author><style face="normal" font="default" size="100%">Pulmano, Christian</style></author><author><style face="normal" font="default" size="100%">Victorino, John Noel</style></author><author><style face="normal" font="default" size="100%">Ohta, Sachiko</style></author><author><style face="normal" font="default" size="100%">Miyazaki, Hiroyuki</style></author><author><style face="normal" font="default" size="100%">Kanbara, Sakiko</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Kanbara, Sakiko</style></author><author><style face="normal" font="default" size="100%">Miyagawa, Shoko</style></author><author><style face="normal" font="default" size="100%">Miyazaki, Hiroyuki</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Management of Health- and Disaster-Related Data</style></title><secondary-title><style face="normal" font="default" size="100%">Disaster Nursing, Primary Health Care and Communication in Uncertainty</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2022</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://doi.org/10.1007/978-3-030-98297-3_25</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">Springer International Publishing</style></publisher><pub-location><style face="normal" font="default" size="100%">Cham</style></pub-location><pages><style face="normal" font="default" size="100%">285–296</style></pages><isbn><style face="normal" font="default" size="100%">978-3-030-98297-3</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">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.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Hiroyuki Miyazaki</style></author><author><style face="normal" font="default" size="100%">Wataru Ohira</style></author><author><style face="normal" font="default" size="100%">Satoshi Kaneko</style></author><author><style face="normal" font="default" size="100%">Ryosuke Shibasaki</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Development of an Automated Settlement Mapping System using High-Resolution Satellite Images and Deep Learning</style></title><secondary-title><style face="normal" font="default" size="100%">The 60th Annual Meeting for the Japanese Society of Tropical Medicine</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2019</style></year><pub-dates><date><style  face="normal" font="default" size="100%">11/2019</style></date></pub-dates></dates><pub-location><style face="normal" font="default" size="100%">Okinawa, Japan</style></pub-location><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">We developed an automated system of building with the use of high-resolution satellite images and deep learning, comprising a geospatial data management system, an image data processing system, and a quality control system. The system development has achieved the component of the geospatial data management and image data processing, and performed building mapping of some large extents, while the development of quality control systems is ongoing. Because we developed the system with open-source and web-based software, anyone can participate in the preparation of training data just only with a computer and the Internet. The system is expected to be a platform for the large-scale mapping of buildings and other ground objects with international collaborations of local partners. The building maps developed by this system are expected to be a basis of analyzing demography with possible risks and impacts of communicable diseases.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>10</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Miyazaki, H.</style></author><author><style face="normal" font="default" size="100%">Kuwata, K.</style></author><author><style face="normal" font="default" size="100%">Ohira, W.</style></author><author><style face="normal" font="default" size="100%">Guo, Z.</style></author><author><style face="normal" font="default" size="100%">Shao, X.</style></author><author><style face="normal" font="default" size="100%">Xu, Y.</style></author><author><style face="normal" font="default" size="100%">Shibasaki, R.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Development of an automated system for building detection from high-resolution satellite images</style></title><secondary-title><style face="normal" font="default" size="100%">2016 4th International Workshop on Earth Observation and Remote Sensing Applications (EORSA)</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2016</style></year></dates><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Guo, Zhiling</style></author><author><style face="normal" font="default" size="100%">Shao, Xiaowei</style></author><author><style face="normal" font="default" size="100%">Xu, Yongwei</style></author><author><style face="normal" font="default" size="100%">Miyazaki, Hiroyuki</style></author><author><style face="normal" font="default" size="100%">Ohira, Wataru</style></author><author><style face="normal" font="default" size="100%">Shibasaki, Ryosuke</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Identification of Village Building via Google Earth Images and Supervised Machine Learning Methods</style></title><secondary-title><style face="normal" font="default" size="100%">Remote Sensing</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2016</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.mdpi.com/2072-4292/8/4/271</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">8</style></volume><pages><style face="normal" font="default" size="100%">271</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">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%.</style></abstract><work-type><style face="normal" font="default" size="100%">Journal Article</style></work-type></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>10</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Oba, A</style></author><author><style face="normal" font="default" size="100%">Miyazaki, H</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Activities of the Studnt Forum of the Geoinformation Forum Japan</style></title><secondary-title><style face="normal" font="default" size="100%">International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">academic activities</style></keyword><keyword><style  face="normal" font="default" size="100%">geoinformation forum</style></keyword><keyword><style  face="normal" font="default" size="100%">japan</style></keyword><keyword><style  face="normal" font="default" size="100%">student forum</style></keyword><keyword><style  face="normal" font="default" size="100%">youth network</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2012</style></year></dates><number><style face="normal" font="default" size="100%">September</style></number><volume><style face="normal" font="default" size="100%">XXXIX</style></volume><pages><style face="normal" font="default" size="100%">153–154</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">This reports a history and future prospects of the activities by the Student Forum of the Geoinformation Forum Japan. For growths of academic fields, active communications among students and young scientists are indispensable. Several academic communities in geoinformation fields are established by youths and play important roles of building networks over schools and institutes. The networks are expected to be innovative cooperation after the youths achieve their professions. Although academic communities are getting fixed growth particularly in Japan, youths had gotten little opportunities to make contacts with youths themselves. To promote gotten youth activities among geoinformation fields, in 1998, we started a series of programs that named the Student Forum of the Geoinformation Forum Japan involving students and young scientists within the annual conferences, Geoinformation Forum Japan. The programs have provided opportunities to do presentation their studies by posters, some events, and motivations to create networks among students and young scientists. From 2009, some members of our activities set additional conference in west area of Japan. Thus our activities are spread within Japan. As a result of these achievements, the number of youth dedicating to the programs keeps growing. From 2009, it’s getting international gradually, however, almost all the participants are still Japanese. To keep and expand the network, we are planning to make some nodes with some Asian youth organizations in the field of geoinformation. This paper is concluded with proposals and future prospects on the Student Forum of the Geoinformation Forum Japan.</style></abstract></record></records></xml>