@article {141,
	title = {Detection of River Plastic Using UAV Sensor Data and Deep Learning},
	journal = {Remote Sensing},
	volume = {14},
	year = {2022},
	month = {Jan-07-2022},
	pages = {3049},
	doi = {10.3390/rs14133049},
	url = {https://www.mdpi.com/2072-4292/14/13/3049},
	author = {Maharjan, Nisha and Miyazaki, Hiroyuki and Pati, Bipun Man and Dailey, Matthew N. and Shrestha, Sangam and Nakamura, Tai}
}
@booklet {128,
	title = {Vec2Instance: Parameterization for Deep Instance Segmentation},
	year = {2020},
	author = {N. Lakmal Deshapriya and Matthew N. Dailey and Manzul Kumar Hazarika and Hiroyuki Miyazaki}
}
@proceedings {111,
	title = {Applications of SENTINEL-1 Synthetic Aperture Radar Imagery for Floods Damage Assessment: a Case Study of Nakhon SI Thammarat, Thailand},
	volume = {42},
	year = {2019},
	author = {Dadhich, Gautam and Miyazaki, Hiroyuki and Babel, Mukand}
}
@proceedings {104,
	title = {Disaggregate Hotel Evaluation by Using Diverse Aspects from User Reviews},
	year = {2019},
	month = {Feb},
	abstract = {Experienced opinions about products and services can guide a potential user for a better purchase decision. Fine-grained aspect level opinions embedded within reviews must be explored to discover experienced users{\textquoteright} latent opinion about the aspects (i.e. features of products like cost, value for money, etc.) and their relative importance. In this paper, we present an unsupervised approach for discovering coherent hotel aspects based on the user attention. This model effectively integrates techniques like topic modeling and word embeddings along with the frequent noun-adjective co-occurrence statistics to automatically discover coherent hotel aspects. Further supervised methods are used to understand the user{\textquoteright}s relative emphasis on the aspects and finally rank the hotels. This method does not assume any predefined seed words and discovers coherent level aspects by directly using user attention and word co-occurrence statistics in addition to topic modeling and word embeddings. The performance evaluation of this method was done by collecting various hotel reviews from multiple travel websites. Results show that the proposed methods improved the baseline performance up to 90\%. Hence, the results thus obtained are very promising and indicate that the system is simple, scalable and most of all accurate in ranking hotels based on the latent aspects expressed in the user reviews.},
	keywords = {Adaptation models, aspects, Coherence, coherent hotel aspects, customer satisfaction, data mining, Data models, disaggregate hotel evaluation, diverse aspects, Estimation, Feature extraction, fine-grained aspect level opinions, frequent noun-adjective co-occurrence statistics, hotel industry, hotel ranking, hotel reviews, latent aspects, Predictive models, purchase decision, statistical analysis, supervised methods, topic modeling, unsupervised learning, user attention, user reviews, word co-occurrence statistics, word embeddings},
	doi = {10.1109/BIGCOMP.2019.8679332},
	author = {B. Devkota and K. Kim and C. Zhuang and H. Miyazaki}
}
@article {109,
	title = {Using Volunteered Geographic Information and Nighttime Light Remote Sensing Data to Identify Tourism Areas of Interest},
	journal = {Sustainability},
	volume = {11},
	year = {2019},
	pages = {4718},
	abstract = {Easy, economical, and near-real-time identification of tourism areas of interest is useful for tourism planning and management. Numerous studies have been accomplished to analyze and evaluate the tourism conditions of a place using free and near-real-time data sources such as social media. This study demonstrates the potential of volunteered geographic information, mainly Twitter and OpenStreetMap, for discovering tourism areas of interest. Active tweet clusters generated using Density-Based Spatial Clustering of Applications with Noise (DBSCAN) clustering algorithm and building footprint information are used to identify touristic places that ensure the availability of basic essential facilities for travelers. Furthermore, an investigation is made to examine the usefulness of nighttime light remotely sensed data to recognize such tourism areas. The study successfully discovered important tourism areas in urban and remote regions in Nepal which have relatively low social media penetration. The effectiveness of the proposed framework is examined using the F1 measure. The accuracy assessment showed F1 score of 0.72 and 0.74 in the selected regions. Hence, the outcomes of this study can provide a valuable reference for various stakeholders such as tourism planners, urban planners, and so on.},
	issn = {2071-1050},
	doi = {10.3390/su11174718},
	url = {https://www.mdpi.com/2071-1050/11/17/4718},
	author = {Devkota, Bidur and Miyazaki, Hiroyuki and Witayangkurn, Apichon and Kim, Sohee Minsun}
}
@proceedings {113,
	title = {Utilizing User Generated Contents to describe Tourism Areas of Interest},
	year = {2019},
	month = {12/2019},
	address = {Chiang Mai, Thailand},
	abstract = {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.},
	keywords = {Flickr, TFIDF, Tourism Area of Interest, Twitter, User Generated Contents},
	url = {https://ieeexplore.ieee.org/document/9018810},
	author = {Bidur Devkota and Hiroyuki Miyazaki and Niraj Pahari}
}
@conference {105,
	title = {Building type classification in Mozambique using mobile phone data, high-res satellite images, night-time light data and digital surface model},
	booktitle = {Mapping Urban Areas from Space Conference 2018},
	year = {2018},
	month = {10/2018},
	address = {Roma, Italy},
	abstract = {According to the UN DESA report {\textquotedblleft}World Population Prospects: The 2015 Revision{\textquotedblright}, The world population is expected to grow 33\% by the year 2050. With the highest rate of population growth, Africa is expected to account for more than the half of the world{\textquoteright}s population growth between 2015 and 2050. The study area presented in this paper is the Republic of Mozambique, an African country with 70\% of its population of 28 million (2016) living and working in rural areas. The Real gross domestic product (GDP) of the country was 3.7\% in 2017 shows it{\textquoteright}s struggle of poor macroeconomic stability and investment of private sector.

High income countries often have extensive mapping resources and expertise to create reliable and accurate building maps and population databases, but across the low-income regions of the world, relevant data are either lacking or are of poor quality. For low-income regions of the world, accurate maps of human population distribution together with the knowledge of building types and its quantitative measures can play an essential part in planning for elections, calculating per-capita gross domestic product (GDP), poverty mapping, city planning, disaster management amongst countless other applications.

The rapid growth in availability of high resolution satellite imagery, computing power and expansion of geospatial analysis tools over the past decade are providing new opportunities to solve such problems. The use of high resolution images, geospatial data and road network together with state of the art machine learning technology can improve the understanding of human population distribution and building type estimation, which is necessary to predict the future infrastructure management for increasing population because it can be expanded to a bigger scale easily unlike the traditionally used method based on human visual interpretation and survey data collection.

In this paper, we proposed a methodology to classify the types of buildings in three classes; high- rise residential buildings, low-rise residential and non-residential buildings. We have used state- of-the-art machine learning algorithm on the combination of mobile phone sample data collected from a survey, high resolution satellite images, digital surface model and night time light data to extract the building footprints and classify the types of the buildings. The comparing results indicated that our methodology classified types of buildings efficiently with the accuracy of 84\%.},
	keywords = {digital surface model, high-rise residential buildings, low-rise residential, non-residential buildings},
	url = {http://muas2018.esa.int/agenda/},
	author = {Dwivedi, Uttam and Miyazaki, Hiroyuki and Shibasaki, Ryosuke}
}
@proceedings {99,
	title = {Development of Population Distribution Map and Automated Human Settlement Map Using High Resolution Remote Sensing Images},
	year = {2018},
	doi = {10.1109/IGARSS.2018.8517827},
	url = {https://ieeexplore.ieee.org/abstract/document/8517827},
	author = {Uttam Dwivedi and Zhiling Guo and Hiroyuki Miyazaki and Mohamed Batran and Ryosuke Shibasaki}
}
@proceedings {103,
	title = {An Exploratory Study on the Generation and Distribution of Geotagged Tweets in Nepal},
	year = {2018},
	month = {Oct},
	abstract = {Social media platforms contribute a huge part of the content available on the world wide web today. These platforms act as a rich source of real time data from live human sensors. These media disseminate spatiotemporal public opinion regarding a range of events, activities and human information behaviors. This paper explores the active user locations and spatial penetration of popular microblogging platform, Twitter, in Nepal. A heatmap visualization is used to show the intensity and distribution of the spatial patterns of Twitter activities in different parts of Nepal. Clustering is a popular technique for knowledge discovery, so spatial clustering is applied to groups tweets spatially into different classes. Such spatial clustering helps in the identification of areas of similar twitter activities and shows the distribution of the spatial patterns in different parts of Nepal. Tweet clusters are observed mainly in the main cities and the tourism centers. Further, an examination of the twitter data shared by the local Nepalese people and the foreigners are shown. This study contributes the research line by providing insights to better understand the spatiotemporal patterns and hotspots of tweets in Nepal. Such patterns and hotspots have an immense practical value that can be attributable to a place in order to derive meaningful insights related to various domains like a disease, crime, tourism, etc.},
	keywords = {active user locations, clustering, Conferences, data mining, geotagged tweets, hotspots, human information behaviors, Kernel, live human sensors, Media, microblogging platform, Nepal, pattern clustering, Security, social media, social media platforms, social networking (online), spatial clustering, spatial distribution, spatial patterns, spatial penetration, spatiotemporal patterns, spatiotemporal public opinion, time data, travel industry, tweet clusters, Twitter, twitter activities, twitter data, Urban areas, world wide web today},
	doi = {10.1109/CCCS.2018.8586827},
	author = {B. Devkota and H. Miyazaki}
}
@proceedings {97,
	title = {Development of Social Media Data Collection System and Its Preliminary Analysis},
	year = {2017},
	address = {New Delhi, India},
	author = {Devkota, Bidur and Miyazaki, Hiroyuki}
}
@proceedings {86,
	title = {Latest Developments of the Isprs Student Consortium},
	year = {2016},
	abstract = {The International Society for Photogrammetry and Remote Sensing (ISPRS) Student Consortium (SC) is a network for young professionals studying or working within the fields of photogrammetry, remote sensing, Geographical Information Systems (GIS), and other related geo-spatial sciences. The main goal of the network is to provide means for information exchange for its young members and thus help promote and integrate youth into the ISPRS. Over the past four years the Student Consortium has successfully continued to fulfil its mission in both formal and informal ways. The formal means of communication of the SC are its website, newsletter, e-mail announcements and summer schools, while its informal ones are multiple social media outlets and various social activities during student related events. The newsletter is published every three to four months and provides both technical and experiential content relevant for the young people in the ISPRS. The SC has been in charge or at least has helped with organizing one or more summer schools every year. The organization{\textquoteright}s e-mail list has over 1,100 subscribers, its website hosts over 1,300 members from 100 countries across the entire globe, and its public Facebook group currently has over 4,500 joined visitors, who connect among one another and share information relevant for their professional careers. These numbers show that the Student Consortium has grown into a significant online-united community. The paper will present the organization{\textquoteright}s on-going and past activities for the last four years, its current priorities and a strategic plan and aspirations for the future four-year period. },
	url = {http://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLI-B6/79/2016/isprs-archives-XLI-B6-79-2016.pdf},
	author = {Detchev, I. and Kanjir, U. and Reyes, S.R. and Miyazaki, H. and Aktas, A.F.}
}
@article {63,
	title = {Unsupervised Global Urban Area Mapping via Automatic Labeling from ASTER and PALSAR Satellite Images},
	journal = {Remote Sensing},
	volume = {7},
	year = {2015},
	month = {2015},
	pages = {2171-2192},
	isbn = {2072-4292},
	url = {http://www.mdpi.com/2072-4292/7/2/2171},
	author = {Duan, Yulin and Shao, Xiaowei and Shi, Yun and Miyazaki, Hiroyuki and Iwao, Koki and Shibasaki, Ryosuke}
}
