2021～2025年に研究室の関係者が発表した論文を対象に、Clarivate 社の InCites
（https://access.clarivate.com/login?app=incites）を用いた分析を行いました。その結果、以下の論文が Top 1% 論文 および Top 10% 論文 に選定されていることが分かりました。
Top 1%論文とは、研究分野ごとに被引用数で順位付けした際、上位 1%に入る論文を指し、当該分野において特に影響力の高い研究成果です。
We conducted an analysis using Clarivate’s InCites (https://access.clarivate.com/login?app=incites) targeting papers published by laboratory members between 2021 and 2025. As a result, we found that the following papers were selected as Top 1% papers and Top 10% papers.
Top 1% papers refer to those ranking within the top 1% of papers in their respective research fields based on citation counts, representing research outcomes with particularly high impact within that field.
◆ Top 1% 論文（※括弧内は研究分野）｜Top 1% Papers (※Fields in parentheses)
- Xia, Y., Yabuki, N., Fukuda, T. (2021).
Development of a system for assessing the quality of urban street-level greenery using street view images and deep learning,
Urban Forestry & Urban Greening, 59, 126995
https://doi.org/10.1016/j.ufug.2021.126995
（URBAN STUDIES; FORESTRY; PLANT SCIENCES） - Zhang, J., Li, Y., Fukuda, T., Wang, B. (2025).
Urban Safety Perception Assessments via Integrating Multimodal Large Language Models with Street View Images,
Cities, 165, 106122
https://doi.org/10.1016/j.cities.2025.106122
（URBAN STUDIES） - Li, Y., Yabuki, N., Fukuda, T. (2022).
Exploring the association between street built environment and street vitality using deep learning methods,
Sustainable Cities and Society, 79, 103656
https://doi.org/10.1016/j.scs.2021.103656
（CONSTRUCTION & BUILDING TECHNOLOGY）
◆ Top 10% 論文（※括弧内は研究分野）｜Top 10% Papers (※Fields in parentheses)
- Xia, Y., Yabuki, N., Fukuda, T. (2021).
Development of a system for assessing the quality of urban street-level greenery using street view images and deep learning,
Urban Forestry & Urban Greening, 59, 126995
https://doi.org/10.1016/j.ufug.2021.126995
（ENVIRONMENTAL STUDIES） - Li, Y., Yabuki, N., Fukuda, T. (2022).
Exploring the association between street built environment and street vitality using deep learning methods,
Sustainable Cities and Society, 79, 103656
https://doi.org/10.1016/j.scs.2021.103656
（ENERGY & FUELS） - Li, Y., Yabuki, N., Fukuda, T. (2022).
Measuring visual walkability perception using panoramic street view images, virtual reality, and deep learning,
Sustainable Cities and Society, 86, 104140, https://doi.org/10.1016/j.scs.2022.104140
（CONSTRUCTION & BUILDING TECHNOLOGY; ENERGY & FUELS） - Kikuchi, N., Fukuda, T., Yabuki, N. (2022).
Future landscape visualization using a city digital twin,
Journal of Computational Design and Engineering, 9(2), 837–856,
https://doi.org/10.1093/jcde/qwac032
（ENGINEERING, MULTIDISCIPLINARY; COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS） - Kido, D., Fukuda, T., Yabuki, N. (2021).
Assessing future landscapes using enhanced mixed reality,
Advanced Engineering Informatics, 48, 101281, https://doi.org/10.1016/j.aei.2021.101281
（ENGINEERING, MULTIDISCIPLINARY） - Zhang, J., Fukuda, T., Yabuki, N. (2021).
City-scale facade color measurement using deep learning and street view images,
ISPRS International Journal of Geo-Information, 10(8), 551,
https://doi.org/10.3390/ijgi10080551
（GEOGRAPHY, PHYSICAL; COMPUTER SCIENCE, INFORMATION SYSTEMS） - Xia, Y., Yabuki, N., Fukuda, T. (2021).
Sky view factor estimation from street view images,
Urban Climate, 40, 100999,
https://doi.org/10.1016/j.uclim.2021.100999
（METEOROLOGY & ATMOSPHERIC SCIENCES; ENVIRONMENTAL SCIENCES）
共著者の皆様、学生の皆様、そして日頃より議論・支援をいただいている多くの方々に、心より感謝申し上げます。今後も、環境・都市・建築・土木分野における AI・CV・デジタルツイン研究 を着実に進めてまいります。
To our co-authors, students, and the many individuals who consistently engage in discussions and provide support, we extend our heartfelt gratitude. Moving forward, we will steadily advance our research in AI, computer vision, and digital twins within the fields of environment, urban planning, architecture, and civil engineering.