Selected Publications
■ HigHigh-Impact Papers (Top 1%)
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
https://doi.org/10.1016/j.ufug.2021.126995
Tag: Urban AI / Greenery Assessment
ストリートビュー画像と深層学習を用いて街路緑の質を定量評価する手法を提案。| Proposes a deep learning-based method to quantify street-level greenery using street view imagery.
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
https://doi.org/10.1016/j.scs.2021.103656
Tag: Urban Vitality / Deep Learning
都市空間の構成要素とストリートの活力の関係を深層学習により分析。| Investigates the relationship between built environment and street vitality using deep learning.
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
https://doi.org/10.1016/j.cities.2025.106122
Tag: Urban Safety / Multimodal AI
マルチモーダルLLMと画像を統合し都市の安全認知を評価する新手法を提示。| Introduces a multimodal LLM-based framework for assessing urban safety perception.
■ Methodological & Applied Advances
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
https://doi.org/10.1016/j.scs.2022.104140
Tag: Walkability / VR / Deep Learning
VRとストリートビュー画像を用いて歩行快適性の知覚を定量化。| Quantifies perceived walkability using VR and street view imagery.
Kikuchi, N., Fukuda, T., Yabuki, N. (2022)
Future landscape visualization using a city digital twin
Journal of Computational Design and Engineering, 9(2)
https://doi.org/10.1093/jcde/qwac032
Tag: Digital Twin / Visualization
都市デジタルツインを活用した将来景観の可視化手法を提案。| Proposes a digital twin-based framework for future landscape visualization.
Kido, D., Fukuda, T., Yabuki, N. (2021)
Assessing future landscapes using enhanced mixed reality
Advanced Engineering Informatics, 48
https://doi.org/10.1016/j.aei.2021.101281
Tag: Mixed Reality / Landscape Assessment
MR技術を用いた将来景観評価手法を開発。| Develops a mixed reality-based method for future landscape assessment.
■ Featured Work
Tsujimoto, R., Fukuda, T., Yabuki, N. (2024)
Server-enabled mixed reality for flood risk communication: On-site visualization with digital twins
Environmental Modelling and Software, 177
https://doi.org/10.1016/j.envsoft.2024.106054
Tag: Disaster Risk / Digital Twin / MR
デジタルツインとMRを用いた洪水リスクの現地可視化・共有手法を提案。| Presents a digital twin and MR-based system for on-site flood risk communication.
Liang, X., Yabuki, N., Fukuda, T. (2026)
Fully Automated Synthetic BIM Dataset Generation Using a Deep Learning-Based Framework
Automation in Construction, 181
https://doi.org/10.1016/j.autcon.2025.106584
Tag: BIM / Deep Learning / Dataset Generation
深層学習を用いて合成BIMデータセットを自動生成するフレームワークを提案。| Proposes a deep learning-based framework for fully automated synthetic BIM dataset generation.
本研究室では、既存のソフトウェアを用いて建造環境を分析することよりもむしろ、その基盤となるソフトウェア自体の開発に取り組んでいます。点群処理や3次元再構成、SLAMなどに代表される「知覚」側の研究テーマも近年増加しています。研究室を志望する学生の皆さんには、こうした“使う側”にとどまらず、工学者として“創る側”としての姿勢も重視している点にご留意ください。
Images generated by AI based on “Selected Publications” to visualize our laboratory’s recent research (top: ChatGPT (DALL·E 3), bottom: Google Gemini (Nano Banana v2)).
Rather than analyzing the built environment using existing software, our laboratory focuses on developing the underlying software itself. In recent years, there has also been a growing number of research topics on the “perception” side, such as point cloud processing, 3D reconstruction, and SLAM. Prospective students should note that we place great importance not only on the “user” perspective but also on the “creator” perspective as engineers.