@conference {131,
	title = {Adapting Vehicle Detector to Target Domain by Adversarial Prediction Alignment},
	booktitle = {IGARSS 2021 - 2021 IEEE International Geoscience and Remote Sensing Symposium2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS},
	year = {2021},
	publisher = {IEEE},
	organization = {IEEE},
	address = {Brussels, Belgium},
	doi = {10.1109/IGARSS47720.2021.9554416},
	url = {https://ieeexplore.ieee.org/document/9554416/http://xplorestaging.ieee.org/ielx7/9553015/9553016/09554416.pdf?arnumber=9554416},
	author = {Koga, Yohei and Miyazaki, Hiroyuki and Shibasaki, Ryosuke}
}
@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}
}
@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}
}
@article {94,
	title = {A CNN-Based Method of Vehicle Detection from Aerial Images Using Hard Example Mining},
	journal = {Remote Sensing},
	volume = {10},
	year = {2018},
	pages = {124},
	type = {Journal Article},
	issn = {2072-4292},
	url = {http://www.mdpi.com/2072-4292/10/1/124},
	author = {Koga, Yohei and Miyazaki, Hiroyuki and Shibasaki, Ryosuke}
}
@inbook {95,
	title = {Development of On-Demand Human Settlement Mapping System Using Historical Satellite Archives},
	booktitle = {Urban Remote Sensing},
	year = {2018},
	pages = {15},
	isbn = {0429888554},
	author = {Miyazaki, Hiroyuki and Shibasaki, Ryosuke}
}
@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 {88,
	title = {Counting Vehicles By Deep Neural Network In High Resolution Satellite Images},
	year = {2016},
	author = {Koga, Yohei and Miyazaki, Hiroyuki and Shibasaki, Ryosuke}
}
@proceedings {91,
	title = {Development of Time-Series Human Settlement Mapping System using Historical Landsat Archive},
	year = {2016},
	pages = {1385-1388},
	author = {Miyazaki, Hiroyuki and Nagai, Masahiko and Shibasaki, Ryosuke}
}
@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}
}
@proceedings {39,
	title = {Preliminary Development of Time-Series Human Settlement Maps using Landsat Data},
	volume = {36},
	year = {2015},
	month = {2015},
	pages = {SP.FR3.3},
	address = {Manila, Philippines},
	author = {Miyazaki, Hiroyuki and Shibasaki, Ryosuke}
}
@article {61,
	title = {Reviews of Geospatial Information Technology and Collaborative Data Delivery for Disaster Risk Management},
	journal = {ISPRS International Journal of Geo-Information},
	volume = {4},
	year = {2015},
	month = {2015},
	pages = {1936},
	isbn = {2220-9964},
	url = {http://www.mdpi.com/2220-9964/4/4/1936},
	author = {Miyazaki, Hiroyuki and Nagai, Masahiko and Shibasaki, Ryosuke}
}
@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}
}
@inbook {36,
	title = {Development of a Global Built-Up Area Map Using ASTER Satellite Images and Existing GIS Data},
	booktitle = {Global Urban Monitoring and Assessment through Earth Observation},
	year = {2014},
	pages = {121},
	publisher = {CRC Press},
	organization = {CRC Press},
	chapter = {7},
	address = {London},
	isbn = {1466564490},
	author = {Miyazaki, Hiroyuki and Shao, Xiaowei and Iwao, Koki and Shibasaki, Ryosuke}
}
@proceedings {68,
	title = {Development of High-Resolution Population Database from ASTER-Derived Urban Area Map},
	year = {2014},
	month = {2014},
	address = {Changsha, China},
	isbn = {978-1-4799-5757-6},
	doi = {10.1109/EORSA.2014.6927879},
	url = {https://ieeexplore.ieee.org/document/6927879},
	author = {Miyazaki, Hiroyuki and Iwao, Koki and Shibasaki, Ryosuke},
	editor = {Weng, Qihao and Zeng, Siqi and Zhu, Jianjun and Zhu, Chuanqu}
}
@proceedings {67,
	title = {Building High-Resolution Population Dataset with ASTER-derived Urban Area Map and Existing Population Data},
	year = {2013},
	month = {2013},
	address = {Bali, Indonesia},
	author = {Miyazaki, Hiroyuki and Iwao, Koki and Shibasaki, Ryosuke}
}
@booklet {66,
	title = {Crowdsourcing for urban area mapping},
	howpublished = {Asia Geospatial Digest},
	year = {2013},
	month = {2013},
	url = {http://geospatialworld.net/Paper/Application/ArticleView.aspx?aid=30462},
	author = {Kimijima, Satomi and Nagai, Masahiko and Miyazaki, Hiroyuki and Shibasaki, Ryosuke and Iwao, Koki}
}
@proceedings {Miyazaki2012,
	title = {Automated Construction of Coverage Catalogues of ASTER Satellite Image for Urban Areas of the World},
	volume = {XXXIX},
	number = {B8},
	year = {2012},
	pages = {497{\textendash}500},
	abstract = {We developed an algorithm to determine a combination of satellite images according to observation extent and image quality. The algorithm was for testing necessity for completing coverage of the search extent. The tests excluded unnecessary images with low quality and preserve necessary images with good quality. The search conditions of the satellite images could be extended, indicating the catalogue could be constructed with specified periods required for time series analysis. We applied the method to a database of metadata of ASTER satellite images archived in GEO Grid of National Institute of Advanced Industrial Science and Technology (AIST), Japan. As indexes of populated places with geographical coordinates, we used a database of 3372 populated place of more than 0.1 million populations retrieved from GRUMP Settlement Points, a global gazetteer of cities, which has geographical names of populated places associated with geographical coordinates and population data. From the coordinates of populated places, 3372 extents were generated with radiuses of 30 km, a half of swath of ASTER satellite images. By merging extents overlapping each other, they were assembled into 2214 extents. As a result, we acquired combinations of good quality for 1244 extents, those of low quality for 96 extents, incomplete combinations for 611 extents. Further improvements would be expected by introducing pixel- based cloud assessment and pixel value correction over seasonal variations.},
	keywords = {ASTER, coverage catalogue, gazetteer, metadata database, urban area},
	author = {Miyazaki, Hiroyuki and Iwao, Koki and Shibasaki, Ryosuke}
}
@proceedings {78,
	title = {Crowd-Sourcing GIS for Global Urban Area Mapping},
	year = {2012},
	month = {2012},
	pages = {A4-2},
	address = {Pattaya, Thailand},
	abstract = {We present an internet-based data development, called crowd sourcing, for global urban area mapping. Crowd sourcing is an approach in which non-expert people, called crowds, join a project of producing data with simple procedures. To introducing crowd sourcing for our global urban area mapping, we constructed a crowd sourcing platform with open source GIS software and developed a ground truth data development system the platform. The data development system was for producing ground truth data by digitizing boundaries of urban area with visual interpretation of satellite images. By using the system, we successfully developed over 160,000 records of boundary data in five month. We had an experiment with operation of the system to measure working time by several sizes of work unit: 80 km {\texttimes} 80 km, 20 km {\texttimes} 20 km, and 10 km {\texttimes} 10 km. Medians of working time were 87.2, 6.2, and 1.4 hours, respectively. Our result would be helpful for estimate total working time of crowd sourcing of ground truth data by visual interpretation and would contribute to progress of data-intensive studies of geospatial information, remote sensing, and photogrammetry.},
	keywords = {ASTER, crowd sourcing, urban area mapping, Web-GIS},
	author = {Miyazaki, Hiroyuki and Kimijima, Satomi and Nagai, Masahiko and Iwao, Koki and Shibasaki, Ryosuke}
}
@conference {40,
	title = {A Crowd-Sourcing System with Web Mapping Systems for Ground Information Database of Global Urban Area Mapping},
	booktitle = {3rd Geospatial Information Forum for Students and Young Engineers},
	year = {2011},
	month = {2011},
	address = {Osaka, Japan},
	author = {Miyazaki, Hiroyuki and Iwao, Koki and Shibasaki, Ryosuke}
}
@article {Miyazaki2011,
	title = {Development of a New Ground Truth Database for Global Urban Area Mapping from a Gazetteer},
	journal = {Remote Sensing},
	volume = {3},
	number = {6},
	year = {2011},
	pages = {1177{\textendash}1187},
	address = {Taipei},
	abstract = {We present a practical system for collecting ground truth data needed for global urban area mapping. To collect visually interpreted information effectively, we implemented crowd sourcing, with which the operators collect and send very accurate data generated by visual interpretation through the Internet. The system was constructed with Web Map Service (WMS) and Web Feature Service (WFS), which are standardized scheme of publishing and updating map data over the Internet. Within the system, the conductor and the operators of visual interpretation campaign would save labor and time for transferring data, keeping consistency, and assuring data security. In addition to that, owing to the standardized scheme, the system was flexibly extensible and transferrable to the other purpose. We regard that the system would contribute to improve ground information for global urban area mapping as well as statistical investigation on precision of visual interpretations. 1. INTRODUCTION Global urban area maps, which represent location, area and shape of urban area of the world,},
	keywords = {crowd sourcing, global urban, ground truth database, web feature service, web mapping service},
	issn = {2072-4292},
	doi = {10.3390/rs3061177},
	url = {http://www.mdpi.com/2072-4292/3/6/1177/},
	author = {Miyazaki, Hiroyuki and Iwao, Koki and Shibasaki, Ryosuke}
}
@proceedings {Miyazaki2011a,
	title = {High-Resolution Urban Area Map for 3372 Cities of the World},
	year = {2011},
	pages = {PS{\textendash}3},
	address = {Taipei},
	abstract = {We developed an automated method of image selection and urban area mapping for developing high-resolution global urban area maps. The method was successfully implemented and applied to 3372 cities of more than 0.1 million people of the world. As the result, the algorithm of image selection determined 11802 scenes of ASTER/VNIR satellite images and yielded good combinations for more than 60\% of the cities. For the merged satellite images with the determined combinations, we applied the automated method of urban area mapping in high resolution. The method was consist of semi-supervised classifications by a machine learning method, called Learning with Local and Global Consistency (LLGC), and integrating the LLGC-derived maps and existing maps by logistic regression. As the result, we acquired urban area maps of 15-m resolution originated from ASTER/VNIR images, which is much finer than 500-m resolution of existing urban area maps. The method had still much space to be improved, especially in avoiding cloud contaminations. However, the method would contribute to complete high-resolution urban area maps of the world and realizing Global Earth Observation Systems of System (GEOSS).},
	keywords = {ASTER, automated image selection, automated urban area mapping, global urban area map, Learning with Local and Global Consistency, logistic regression, multi-source classification},
	author = {Miyazaki, Hiroyuki and Itabashi, Koichiro and Shao, Xiaowei and Iwao, Koki and Shibasaki, Ryosuke}
}
@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}
}
@conference {43,
	title = {A Study on Global Urban Area Mapping using Satellite Data},
	booktitle = {20th IIS Forum on Broad-Scale Collection and Application of Environment and Disaster Risk Information},
	year = {2011},
	month = {2011},
	address = {Tokyo, Japan},
	author = {Miyazaki, Hiroyuki and Iwao, Koki and Shibasaki, Ryosuke}
}
@conference {44,
	title = {Automatic Mapping of Urban Area in High Resolution with ASTER satellite images},
	booktitle = {19th IIS Forum on Broad-Scale Collection and Application of Environment and Disaster Risk Information},
	year = {2010},
	month = {2010},
	address = {Tokyo, Japan},
	author = {Miyazaki, Hiroyuki and Shao, Xiaowei and Iwao, Koki and Shibasaki, Ryosuke}
}
@proceedings {Miyazaki2010,
	title = {Automatic Mapping of Urban Area in High Resolution with LLGC and Integration with Existing Urban Area Maps},
	year = {2010},
	pages = {1{\textendash}9},
	address = {Guimaraes, Portugal},
	abstract = {We present development of automated algorithm for mapping global urban area in high resolution using ASTER satellite images and coarse-resolution urban area maps. The algorithm consists of two steps: classifying pixels of ASTER satellite images into urban or non-urban by Learning with Global and Local Consistency (LLGC) technique; and integration with existing urban area maps using logistic regression. We implemented the algorithm and demonstrated it against 340 scenes of ASTER satellite images. LLGC trimmed up 500-m-resolution clusters of urban area into 15-m-resoluton clusters. However accuracy assessment on LLGC result showed 75\% user{\textquoteright}s accuracy, 41\% producer{\textquoteright}s accuracy, 94\% overall accuracy and 0.50 kappa coefficient, indicating LLGC had considerable misclassifications due to similarity in surface reflectance among non-vegetative land cover. To complement the misclassifications, we integrated LLGC result with existing urban area maps. Accuracy assessment on result of the integration showed 74\% user{\textquoteright}s accuracy, 43\% producer{\textquoteright}s accuracy, 94\% overall accuracy and 0.51 kappa coefficient, indicating that the results were more accurate than LLGC result and existing urban area maps. We concluded our method would improve global urban area map not only in terms of spatial resolution, but also in that of accuracy.},
	keywords = {ASTER, land cover classification, urban area mapping},
	author = {Miyazaki, Hiroyuki and Shao, Xiaowei and Iwao, Koki and Shibasaki, Ryosuke}
}
@proceedings {Miyazaki2010a,
	title = {Global Urban Area Mapping in High Resolution using ASTER Satellite Images},
	volume = {XXXVIII},
	year = {2010},
	pages = {847{\textendash}852},
	address = {Kyoto},
	abstract = {We present development of automated algorithm for mapping global urban area in high resolution using ASTER satellite images and coarse-resolution urban area maps. The algorithm consists of two steps: classifying pixels of ASTER satellite images into urban or non-urban by Learning with Global and Local Consistency (LLGC) technique; and integration with existing urban area maps using logistic regression. We implemented the algorithm and demonstrated it against 340 scenes of ASTER satellite images. LLGC trimmed up 500-m-resolution clusters of urban area into 15-m-resoluton clusters. However accuracy assessment on LLGC result showed 75\% user{\textquoteright}s accuracy, 41\% producer{\textquoteright}s accuracy, 94\% overall accuracy and 0.50 kappa coefficient, indicating LLGC had considerable misclassifications due to similarity in surface reflectance among non-vegetative land cover. To complement the misclassifications, we integrated LLGC result with existing urban area maps. Accuracy assessment on result of the integration showed 74\% user{\textquoteright}s accuracy, 43\% producer{\textquoteright}s accuracy, 94\% overall accuracy and 0.51 kappa coefficient, indicating that the results were more accurate than LLGC result and existing urban area maps. We concluded our method would improve global urban area map not only in terms of spatial resolution, but also in that of accuracy.},
	keywords = {ASTER, land cover classification, urban area mapping},
	author = {Miyazaki, Hiroyuki and Shao, Xiaowei and Iwao, Koki and Shibasaki, Ryosuke}
}
@proceedings {Miyazaki2010d,
	title = {Global Urban Area Mapping using Global ASTER Satellite Images},
	year = {2010},
	pages = {TS07{\textendash}2},
	address = {Hanoi},
	abstract = {We present development of global urban area map using ASTER satellite images, which has much higher resolution than that of existing global urban area maps. To complete the map for world{\textquoteright}s cities, we developed automated algorithm for mapping urban area in high resolution. The algorithm consists of two steps: classifying pixels of ASTER satellite images into urban or non-urban by Learning with Global and Local Consistency (LLGC) technique; and integration with existing urban area maps using logistic regression. We implemented the algorithm and demonstrated it on 775 scenes of ASTER satellite images. LLGC classified pixels of ASTER satellite images into urban or non-urban in 15-m resolution, though it had considerable amount of misclassification due to similarity in surface reflectance among non-vegetative land cover. To complement the misclassifications, we integrated LLGC results with existing urban area maps using logistic regression. The misclassifications were corrected well, especially in dry zone. We also developed ground truth database using global gazetteer of world{\textquoteright}s cities so that we conduct comprehensive accuracy assessment on the urban area map. We visually interpreted land cover of urban or non-urban on 3734 points coordinates derived from global gazetteer, and combined them with 4211 data points of Degree Confluence Project into a database, which had 2185 data points of urban and 5559 of non-urban. Accuracy assessment using the database indicates that our map is more accurate than existing urban area maps. Finally, we applied the method on broad coverage of ASTER satellite images rather than single one scene. The result showed quite well classification as a whole, indicating possibility of developing global urban area map in high-resolution; however considerable problems due to availability of cloud-free satellite image is still remained.},
	keywords = {ASTER, cover classification, high resolution, land, LLGC, logistic regression, urban area mapping},
	author = {Miyazaki, Hiroyuki and Shao, Xiaowei and Iwao, Koki and Shibasaki, Ryosuke}
}
@proceedings {54,
	title = {High-Resolution Urban Area Mapping of World{\textquoteright}s Cities Using Satellite Images},
	volume = {19},
	year = {2010},
	month = {2010},
	pages = {1C-3},
	address = {Kyoto, Japan},
	author = {Miyazaki, Hiroyuki and Iwao, Koki 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}
}
@proceedings {Miyazaki2009c,
	title = {Assessing Validity of Global Gazetteers as Ground Truth Data for Urban Mapping},
	year = {2009},
	pages = {TS17{\textendash}01},
	address = {Beijing},
	author = {Miyazaki, Hiroyuki and Iwao, Koki and Shibasaki, Ryosuke}
}
@conference {52,
	title = {Assessment of Usability of Gazetteer as Ground Truth Data},
	booktitle = {Japan Society of Photogrammetry and Remote Sensing Spring Meeting},
	year = {2009},
	month = {2009},
	pages = {I-1},
	address = {Yokohama, Japan},
	author = {Miyazaki, Hiroyuki and Iwao, Koki and Shibasaki, Ryosuke}
}
@conference {57,
	title = {Developing Global Urban Extent Map of High Resolution with ASTER Satellite Images},
	booktitle = {7th International Conference on Urban Climate},
	year = {2009},
	month = {2009},
	pages = {4-16},
	address = {Yokohama, Japan},
	author = {Miyazaki, Hiroyuki and Iwao, Koki and Shibasaki, Ryosuke}
}
@conference {42,
	title = {Development of New Urban Extent Map with Integration of ASTER Satellite Images and Existing Urban Extent Maps},
	booktitle = {1st Geoinformation Student Forum in Kansai},
	year = {2009},
	month = {2009},
	address = {Kyoto, Japan},
	author = {Miyazaki, Hiroyuki and Iwao, Koki 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}
}
@conference {49,
	title = {Urban Extent Mapping with Textural Information of Satellite Image},
	booktitle = {18th IIS Forum on Broad-Scale Collection and Application of Environment and Disaster Risk Information},
	year = {2009},
	month = {2009},
	address = {Tokyo, Japan},
	author = {Miyazaki, Hiroyuki and Iwao, Koki and Shibasaki, Ryosuke}
}
@conference {50,
	title = {Validation on Availability of Gazetteer for Global Urban Mapping},
	booktitle = {16th Remote Sensing Forum, Society of Instrument and Control Engineers},
	year = {2009},
	month = {2009},
	address = {Tokyo, Japan},
	author = {Miyazaki, Hiroyuki and Iwao, Koki and Shibasaki, Ryosuke}
}
@proceedings {55,
	title = {Developing Urban Extent Map of High Accuracy and Resolution by Integrating ASTER satellite Images and Existing Urban Extent Maps},
	volume = {17},
	year = {2008},
	month = {2008},
	pages = {89-92},
	address = {Tokyo, Japan},
	author = {Miyazaki, Hiroyuki and Iwao, Koki and Shibasaki, Ryosuke}
}
@conference {51,
	title = {Development of New Urban Extent Map with lntegration of ASTER/VNIR Classified Map and Existing Urban Extent Maps},
	booktitle = {ASTER Workshop - ASTER Science Team Meeting},
	year = {2008},
	month = {2008},
	pages = {10-11},
	address = {Tokyo, Japan},
	author = {Miyazaki, Hiroyuki and Iwao, Koki and Shibasaki, Ryosuke}
}
@article {Miyazaki2008,
	title = {Estimation of infuluence of grazing activities against vegetation with multi-temporal satellite images on Qinghai-Tibet plateau [in Japanese]},
	journal = {Papers on environmental information science},
	volume = {22},
	year = {2008},
	pages = {565{\textendash}570},
	abstract = {In Qinghai-Tibet Plateau, west part of China, there are serious land degradations caused by overgrazing and climatic change, and mapping the influence is urgently needed. This study proposes method for estimation of influence by grazing activities on grassland with 16-day dataset derived from MODIS satellite imagery and meteorological observation dataset. The result of application on Maduo-Xian in Qnghai province shows estimated influence is correspond to actual condition recognized with field study, and degraded land, grazed land and conserved land are discriminated with the estimation. In addition, the period of grazing at grazing land for summer or winter is estimated with time series profile of the estimation. The method is expected to be developed for observation on conditions of grazing land.},
	keywords = {grazing activities, MODIS, multi-temporal satellite images, Qinghai-Tibet Plateau},
	url = {http://ci.nii.ac.jp/naid/40016406716/en/},
	author = {Miyazaki, Hiroyuki and Shibasaki, Ryosuke and Yan, Wanglin and Zhao, Zhizhong}
}
@proceedings {53,
	title = {Study on Automatic Global Urban Mapping Method using Satellite Imagery and the Existing Land Cover Data},
	year = {2008},
	month = {2008},
	pages = {S-4},
	address = {Yokohama, Japan},
	author = {Miyazaki, Hiroyuki and Tanaka, Ayako and Iwao, Koki and Shibasaki, Ryosuke}
}
@proceedings {47,
	title = {Analysis of the Geographical Process of Grazing Impacts in Tibetan Plateau using Time-series Satellite Images},
	year = {2007},
	month = {2007},
	address = {Yokohama, Japan},
	author = {Miyazaki, Hiroyuki and Shibasaki, Ryosuke and Yan, Wanglin}
}
@proceedings {59,
	title = {Development of Method for Detection of Grazing Activities with Time-Series Satellite images in Plateau Region},
	year = {2007},
	month = {2007},
	pages = {TS25.5},
	address = {Kuala Lumpur, Malaysia},
	author = {Miyazaki, Hiroyuki and Shibasaki, Ryosuke and Yan, Wanglin}
}
@proceedings {60,
	title = {Analysis of the Geographical Process of Land Degrations in Plateau Region with Time-series Satellite Images},
	year = {2006},
	month = {2006},
	pages = {T-1_T2},
	address = {Ulaanbaatar, Mongolia},
	author = {Miyazaki, Hiroyuki and Shibasaki, Ryosuke and Yan, Wanglin}
}
