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Sekimoto Lab
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A lab in human-urban informatics that uses information technology to interpret the flow of people and urban data.
AboutAbout the lab
Human-centered urban information technology.
The Sekimoto-Nakai Lab studies urban information technology that is centered on people.
The lab estimates the flow of people from data such as smartphone location data, and detects road deterioration using images and machine learning.
It also designs mechanisms for how information flows across countries and regions, and works with citizens to run cities together.
The lab places importance on an international perspective, and makes it a rule that students present at an international conference every year.
Key terms
- Human mobility
- The flow of people. Data showing when and from where to where people moved, used in urban planning and disaster prevention.
- Data assimilation
- A method of incorporating observed data into a simulation to bring the calculated results closer to reality.
- Machine learning
- A technology that lets computers learn rules from large amounts of data so they can classify images and make predictions.
NewsLab news
Published a paper on a method for extracting buildings nationwide from satellite imagery
Published a paper using human mobility data to analyze inequality in exposure to extreme heat in India
Students won the top prize and other awards at Urban Data Challenge 2025
ResearchResearch themes
The lab aims to solve the challenges of dynamically changing cities with the power of information. Its research ranges from technologies for measuring and analyzing data on the flow of people and urban infrastructure to building mechanisms for information distribution at the national and regional levels.
*Illustrative imageMeasuring and estimating the flow of people
The lab combines fragmentary travel survey data and mobile phone location data with behavior models and data assimilation to reproduce the flow of people across an entire city. It also works on human mobility simulation of large metropolitan areas using reinforcement learning.
*Illustrative imageUnderstanding people's movement in disasters and other emergencies
The lab analyzes evacuation and changes in behavior after earthquakes and heavy rain, using data such as smartphone GPS data. It also studies mechanisms for prioritizing road restoration that take people's movement into account.
*Illustrative imageInspecting urban infrastructure cheaply and quickly
The lab detects road damage such as cracks using images taken with smartphones and machine learning. It also studies how to measure traffic volume from surveillance camera and in-vehicle camera footage.
*Illustrative imageDriving cities through the flow of information
The lab builds citizen-oriented urban planning simulators and urban dashboards, and designs mechanisms that promote the circulation of geospatial information held by governments and companies. It is also involved in participatory mapping with citizens.
MembersFaculty
The members of the Sekimoto-Nakai Lab include faculty, researchers, secretaries, doctoral and master's students, visiting scholars and exchange students, academic specialists, research fellows, visiting researchers, and collaborating researchers.As of 2026
- ProfessorYoshihide SekimotoPublications ↗
- LecturerFuko NakaiPublications ↗
- Project Assistant ProfessorDeeksha AryaPublications ↗
- Project Assistant ProfessorMayumi MizobuchiPublications ↗
- Project Assistant ProfessorJue MaPublications ↗
- Project Assistant ProfessorChenbo ZhaoPublications ↗
VoicesStudent voices
Compiled from answers by students who were in the lab. Ratings and reviews reflect individual students' experiences, not the lab's official views.
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Report an errorAbout the images
The top and research theme images are illustrative and were not taken at the lab. They are cropped from Wikimedia Commons images. Cropped CC BY-SA images are provided under the same CC BY-SA license as the original (version shown on each line).
- Top image: “ISS-62 Tokyo, Japan at night” NASA / Public domain (cropped)
- Measuring and estimating the flow of people: “Durban Tracks” CoolestGuyJake / CC BY-SA 4.0 (cropped)
- Understanding people's movement in disasters and other emergencies: “Damage in the Mabi District from the Heavy Rain of July 2018” Original Images: Geospatial Information Authority of Japan, composed by 軽快 / CC BY 4.0 (cropped)
- Inspecting urban infrastructure cheaply and quickly: “Pothole in Villeray, Montréal” Miguel Tremblay / Public domain (cropped)
- Driving cities through the flow of information: “2019 City of London 3D model” AccuCities / CC BY-SA 4.0 (cropped)
- Related lab (交通・都市・国土学研究室
（BinN / 都市生活学・ネットワーク 行動学研究グループ） ): “X-shaped pedestrian Sakura bridge over Sumida river, linking Taitō and Sumida wards, view from Tokyo Skytree, Japan” Basile Morin / CC BY-SA 4.0 (cropped) - Related lab (地域/情報研究室): “Landsat View, Tokyo, Japan - Flickr - NASA Goddard Photo and Video” NASA Goddard Space Flight Center from Greenbelt, MD, USA / Public domain (cropped)
- Related lab (大口研究室): “Hakozaki Junction - 14” Kabacchi / CC BY 2.0 (cropped)
- Related lab (河川/流域環境研究室 澤田グループ): “Typhoon In-Fa (MODIS )” MODIS Land Rapid Response Team, NASA GSFC / Public domain (cropped)
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