<?xml version="1.0" encoding="UTF-8"?><xml><records><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%">Maharjan, Nisha</style></author><author><style face="normal" font="default" size="100%">Miyazaki, Hiroyuki</style></author><author><style face="normal" font="default" size="100%">Pati, Bipun Man</style></author><author><style face="normal" font="default" size="100%">Dailey, Matthew N.</style></author><author><style face="normal" font="default" size="100%">Shrestha, Sangam</style></author><author><style face="normal" font="default" size="100%">Nakamura, Tai</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Detection of River Plastic Using UAV Sensor Data and Deep Learning</style></title><secondary-title><style face="normal" font="default" size="100%">Remote Sensing</style></secondary-title><short-title><style face="normal" font="default" size="100%">Remote Sensing</style></short-title></titles><dates><year><style  face="normal" font="default" size="100%">2022</style></year><pub-dates><date><style  face="normal" font="default" size="100%">Jan-07-2022</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://www.mdpi.com/2072-4292/14/13/3049</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">14</style></volume><pages><style face="normal" font="default" size="100%">3049</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><issue><style face="normal" font="default" size="100%">13</style></issue></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%">Parven, Afshana</style></author><author><style face="normal" font="default" size="100%">Pal, Indrajit</style></author><author><style face="normal" font="default" size="100%">Witayangkurn, Apichon</style></author><author><style face="normal" font="default" size="100%">Pramanik, Malay</style></author><author><style face="normal" font="default" size="100%">Nagai, Masahiko</style></author><author><style face="normal" font="default" size="100%">Miyazaki, Hiroyuki</style></author><author><style face="normal" font="default" size="100%">Wuthisakkaroon, Chanakan</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Impacts of disaster and land-use change on food security and adaptation: Evidence from the delta community in Bangladesh</style></title><secondary-title><style face="normal" font="default" size="100%">International Journal of Disaster Risk Reduction</style></secondary-title><short-title><style face="normal" font="default" size="100%">International Journal of Disaster Risk Reduction</style></short-title></titles><dates><year><style  face="normal" font="default" size="100%">2022</style></year><pub-dates><date><style  face="normal" font="default" size="100%">Jan-08-2022</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://www.sciencedirect.com/science/article/abs/pii/S2212420922003387</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">78</style></volume><pages><style face="normal" font="default" size="100%">103119</style></pages><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>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>5</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Kanbara, Sakiko</style></author><author><style face="normal" font="default" size="100%">Pandey, Apsara</style></author><author><style face="normal" font="default" size="100%">Estuar, Maria Regina E.</style></author><author><style face="normal" font="default" size="100%">Lee, Hyeon Ju</style></author><author><style face="normal" font="default" size="100%">Miyazaki, Hiroyuki</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Chan, Emily Ying Yang</style></author><author><style face="normal" font="default" size="100%">Shaw, Rajib</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">EpiNurse, Health Monitoring by Local Nurses on Nepal Earth Quake 2015</style></title><secondary-title><style face="normal" font="default" size="100%">Public Health and Disasters: Health Emergency and Disaster Risk Management in Asia</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2020</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://doi.org/10.1007/978-981-15-0924-7_15</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">Springer Singapore</style></publisher><pub-location><style face="normal" font="default" size="100%">Singapore</style></pub-location><pages><style face="normal" font="default" size="100%">229 - 244</style></pages><isbn><style face="normal" font="default" size="100%">978-981-15-0924-7</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">The name, EpiNurse, refers to local nurses who perform epidemiological surveillance and care to ensure human security in and communicate with health authorities on the health status of communities. They collect and report epidemiological information by using easy-to-use technology in communities in which access to health information of the populace is hindered by catastrophic accidents or other geo-/socio-political reasons. EpiNurse Nepal Project (August 2015–April 2016) carried out epidemiological surveillances at 24 camps in 9 districts after the 2015 Nepal earthquake. By applying ICT and questionnaire, EpiNurse member nurses collected shelter-related data and information, such as location and timestamps, and geo-tagged photos. Identification of typical health behavior patterns and comparative information about differences between community and temporary shelters provided insights into the health security assessment. This initiative experiments how nurse should collect and deliver the health emergency information on their own local culture, lifestyle, and perceptions. The potential of EpiNurse concept lies not only in producing innovative research outcomes by improving or optimizing existing ICT application in health sector, but also in promoting research knowledge and exchange of ideas regarding social issues and challenges in the field of health emergency and community resilience. The most critical challenge in practice relates to collecting and storing data, which later would have been generated into reasonable health security index information to be used for predicting the likelihood of occurrence of health emergency events. It is necessary to apply human behavior modeling using geospatial technology in order to create data transferring modules for first responders and civilian populations regarding DRR and behaviors.</style></abstract></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%">Peungnumsai, Apantri</style></author><author><style face="normal" font="default" size="100%">Miyazaki, Hiroyuki</style></author><author><style face="normal" font="default" size="100%">Witayangkurn, Apichon</style></author><author><style face="normal" font="default" size="100%">Kim, Sohee Minsun</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">A Grid-Based Spatial Analysis for Detecting Supply–Demand Gaps of Public Transports: A Case Study of the Bangkok Metropolitan Region</style></title><secondary-title><style face="normal" font="default" size="100%">Sustainability</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2020</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://www.mdpi.com/2071-1050/12/24/10382</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">12</style></volume><pages><style face="normal" font="default" size="100%">10382</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Public transport service has been promoted to reduce the problems of traffic congestion and environmental impacts due to car dependency. Several public transportation modes are available in Bangkok Metropolitan Region (BMR) such as buses, heavy rails, vans, boats, taxis, and trains while in some areas have fewer modes of public transport available. The disparity of public transport service negatively impacts social equity. This study aims to identify the gaps between public transport supply and demand and to demonstrate introduced indicators to assess the public transport performance incorporating transport capacity and equilibrium access aspects. Supply index was used to evaluate the level of service, and the demand index was applied to estimate travel needs. Furthermore, the Lorenz curves and the Gini coefficients were used to measure the equity of public transport. The results highlight that more than half of the BMR population is living in low-supply high-demand areas for public transportation. Moreover, the equitable access analysis has identified that the high-income population has better access to public transport than the low-income population. The results suggest that public transport gaps and equity indicate the inclusiveness of public transportation, as well as to the areas where to improve the public transport service. Thus, the methodology used in this study can be applied to another city or region similar to BMR.</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%">Masanobu Kii</style></author><author><style face="normal" font="default" size="100%">Apantri Peungnumsai</style></author><author><style face="normal" font="default" size="100%">Varameth Vichiensan</style></author><author><style face="normal" font="default" size="100%">Hiroyuki Miyazaki</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Effect of Public Transport Network on Urban Core and the Future Perspective in Bangkok, Thailand</style></title><secondary-title><style face="normal" font="default" size="100%">2019 First International Conference on Smart Technology &amp; Urban Development (STUD)</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">location probability</style></keyword><keyword><style  face="normal" font="default" size="100%">network  centrality</style></keyword><keyword><style  face="normal" font="default" size="100%">point of interest</style></keyword><keyword><style  face="normal" font="default" size="100%">railway  network</style></keyword><keyword><style  face="normal" font="default" size="100%">urban  core</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2019</style></year><pub-dates><date><style  face="normal" font="default" size="100%">12/2019</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://ieeexplore.ieee.org/document/9018769</style></url></web-urls></urls><pub-location><style face="normal" font="default" size="100%">Chiang Mai, Thailand</style></pub-location><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%"> City  center  is  an  essential  components  of  urban  structure   that   rules   urban   activities   including   economy,   transport,  and  social  interactions.  In  Bangkok,  Thailand,  the  railway network is expanding and the expansion is expected to  affect  the  city  center  locations.  In  this  study  we  attempt  to  capture   the   effect   of   public   transport   network   on   the   accumulation of three types of urban core facilities based on the spatial statistical approach, and estimate the future perspective of locations of those facilities. As a result we found that expected number of facilities in current urban core in Bangkok decreases and  the  number  of  facilities  at  stations  on  planned  railways  increases under certain conditions. The results can be utilized to estimate the future travel pattern and residential locations. </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%">Tsuyoshi Takano</style></author><author><style face="normal" font="default" size="100%">Hiroyoshi Morita</style></author><author><style face="normal" font="default" size="100%">Shinichiro Nakamura</style></author><author><style face="normal" font="default" size="100%">Hiroyuki Miyazaki</style></author><author><style face="normal" font="default" size="100%">Wasan Pattara-atikom</style></author><author><style face="normal" font="default" size="100%">Napaporn Piamsa-nga</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Impact of Rainfall on Urban Traffic Flow based on Probe Vehicle Data in Bangkok</style></title><secondary-title><style face="normal" font="default" size="100%">First International Conference on Smart Technology &amp; Urban Development (STUD 2019)</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Climate  change</style></keyword><keyword><style  face="normal" font="default" size="100%">Probe  vehicle  data</style></keyword><keyword><style  face="normal" font="default" size="100%">Rainfall  impact</style></keyword><keyword><style  face="normal" font="default" size="100%">Regression model</style></keyword><keyword><style  face="normal" font="default" size="100%">Travel speed</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2019</style></year><pub-dates><date><style  face="normal" font="default" size="100%">12/2019</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://saki.siit.tu.ac.th/stud2019/uploads_final/111__18076cee1637baa6dafa754962eb2939/FinalFile_stud19_takano_v7_en.pdf</style></url></web-urls></urls><pub-location><style face="normal" font="default" size="100%">Chiang Mai, Thailand</style></pub-location><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">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’s  GDP.  Future  rainfall  forecast  data  makes  it  possible  to  assess  the  risk  of  climate  change on urban traffic flow.</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%">Apantri Peungnumsai</style></author><author><style face="normal" font="default" size="100%">Hiroyuki Miyazaki</style></author><author><style face="normal" font="default" size="100%">Apichon Witayangkurn</style></author><author><style face="normal" font="default" size="100%">Masanobu Kii</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">A Review of MATSim: A Pilot Study of Chatuchak, Bangkok</style></title><secondary-title><style face="normal" font="default" size="100%">First International Conference on Smart Technology &amp; Urban Development (STUD 2019)</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Agent-based modeling</style></keyword><keyword><style  face="normal" font="default" size="100%">Road transportation</style></keyword><keyword><style  face="normal" font="default" size="100%">Transportation</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2019</style></year><pub-dates><date><style  face="normal" font="default" size="100%">12/2019</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://jiist.aiat.or.th/assets/uploads/1588686091433SRGAmjiist.aiat.or.th-23.pdf</style></url></web-urls></urls><pub-location><style face="normal" font="default" size="100%">Chiang Mai, Thailand</style></pub-location><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Transportation is one of the basic infrastructures that has become an important factor for urban planning and development. In order to develop a better transportation system can lead to better infrastructure, studying the traffic system, current situation and its behavior, is necessary. However, to reveal every object and its dynamic that happens in the traffic system is impossible without a tool and techniques. MATSim is a simulation model software used to assign the traffic between origins and destinations. Most of MATSim applications have been used for developed countries. Nevertheless, Bangkok is one of several cities challenging on the over-saturated situation on road traffic. To check the situation, the simulation can be used to explore highly concentrated traffic flow. Thus, the objective of this study is to examine the applicability of the Multi-Agents Transportation Simulation (MATSim) framework to Bangkok situation. For the travel demand forecasting, it commonly referred to as the four step model. And MATSim framework is one model for the fourth step of the model which is traffic assignment or route assignment. Therefore, this study explored MATSim by experimenting with two plans of agents represented by people travelling from home to work and work to home over Chatuchak district, Bangkok. The sample size of agents using in the simulation are 10, 100, and 500 agents. The results show the traffic flow differently because of the volume of agent effect on the traffic flow.</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%">Bidur Devkota</style></author><author><style face="normal" font="default" size="100%">Hiroyuki Miyazaki</style></author><author><style face="normal" font="default" size="100%">Niraj Pahari</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Utilizing User Generated Contents to describe Tourism Areas of Interest</style></title><secondary-title><style face="normal" font="default" size="100%">2019 First International Conference on Smart Technology &amp; Urban Development (STUD)</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Flickr</style></keyword><keyword><style  face="normal" font="default" size="100%">TFIDF</style></keyword><keyword><style  face="normal" font="default" size="100%">Tourism Area of Interest</style></keyword><keyword><style  face="normal" font="default" size="100%">Twitter</style></keyword><keyword><style  face="normal" font="default" size="100%">User Generated Contents</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2019</style></year><pub-dates><date><style  face="normal" font="default" size="100%">12/2019</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://ieeexplore.ieee.org/document/9018810</style></url></web-urls></urls><pub-location><style face="normal" font="default" size="100%">Chiang Mai, Thailand</style></pub-location><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">The use of available place databases (like GeoNamesand traditional maps) to obtain descriptive keywords of a user defined place is not possible because such data sources mainlymaintain location definitions of the well-known places only.Traditional sources may not be updated dynamically and maynot ensure diverse information. Additionally, they do not give anyinformation on the popularity, e.g., which is more popular amongthe places indexed by the same keyword. A bottom-up approach,based on real user attention, can address these problems. Wepropose a method to describe tourism area of interest (TAOI) byaggregating user generated social media text. We match the cooccurrence of important keywords in a particular location andselect such words to describe TAOIs. We applied the proposedmethod to data on micro blogging service Twitter and photosharing service Flickr and confirmed that our method made itpossible to extract TAOI description. The recommended bottomup approach enables the extraction of valuable information thatis not possible by using traditional top-down approaches.</style></abstract></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%">Kanbara, Sakiko</style></author><author><style face="normal" font="default" size="100%">Ngatu, Nlandu Roger</style></author><author><style face="normal" font="default" size="100%">Pokhrel, Tara</style></author><author><style face="normal" font="default" size="100%">Pandey, Apsara</style></author><author><style face="normal" font="default" size="100%">Sharma, Chandrakara</style></author><author><style face="normal" font="default" size="100%">Lee, Hyeon J</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><author><style face="normal" font="default" size="100%">Nojima, Sayumi</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">The 2015 Nepal Earthquake Disaster: Is the Threat of Occurrence of Communicable Disease Epidemic Over?</style></title><secondary-title><style face="normal" font="default" size="100%">International Journal of Indonesian National Nurses Association (IJINNA)</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2018</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://ijinna-ppni.org/ijinna2/index.php/IJINNA/article/view/32</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">1</style></volume><pages><style face="normal" font="default" size="100%">105-110</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><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>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Peungnumsai, Apantri</style></author><author><style face="normal" font="default" size="100%">Witayangkurn, Apichon</style></author><author><style face="normal" font="default" size="100%">Nagai, Masahiko</style></author><author><style face="normal" font="default" size="100%">Miyazaki, Hiroyuki</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">A Taxi Zoning Analysis Using Large-Scale Probe Data: A Case Study for Metropolitan Bangkok</style></title><secondary-title><style face="normal" font="default" size="100%">The Review of Socionetwork Strategies</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2018</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://doi.org/10.1007/s12626-018-0019-4</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">12</style></volume><pages><style face="normal" font="default" size="100%">21-45</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><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>5</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Weng, Qihao</style></author><author><style face="normal" font="default" size="100%">Gamba, Paolo</style></author><author><style face="normal" font="default" size="100%">Mountrakis, Giorgos</style></author><author><style face="normal" font="default" size="100%">Pesaresi, Martino</style></author><author><style face="normal" font="default" size="100%">Lu, Linlin</style></author><author><style face="normal" font="default" size="100%">Kemper, Thomas</style></author><author><style face="normal" font="default" size="100%">Heinzel, Johannes</style></author><author><style face="normal" font="default" size="100%">Xian, George</style></author><author><style face="normal" font="default" size="100%">Jin, Huiran</style></author><author><style face="normal" font="default" size="100%">Miyazaki, Hiroyuki</style></author><author><style face="normal" font="default" size="100%">Xu, Bing</style></author><author><style face="normal" font="default" size="100%">Quresh, Salman</style></author><author><style face="normal" font="default" size="100%">Keramitsoglou, Iphigenia</style></author><author><style face="normal" font="default" size="100%">Ban, Yifang</style></author><author><style face="normal" font="default" size="100%">Esch, Thomas</style></author><author><style face="normal" font="default" size="100%">Roth, Achim</style></author><author><style face="normal" font="default" size="100%">Elvidge, Christopher D.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Urban Observing Sensors</style></title><secondary-title><style face="normal" font="default" size="100%">Global Urban Monitoring and Assessment through Earth Observation</style></secondary-title><tertiary-title><style face="normal" font="default" size="100%">Remote Sensing Applications Series</style></tertiary-title></titles><dates><year><style  face="normal" font="default" size="100%">2014</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2014</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://dx.doi.org/10.1201/b17012-6</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">CRC Press</style></publisher><pages><style face="normal" font="default" 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