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キャンパス公開2025 – 研究ポスター

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関本研究室研究紹介 / Sekimoto Lab Research Introduction

1: Virtual 3D City Model Generation in CityGML

2: An appearance-contingent generation solution with an automated creation of 3D building digital cousins

3: Evaluating the Influence of Gender and Age Factors on Subjective Perceptions of Streetscapes

4: Economic Impact of Inbound Tourism in Tokyo and Its Regional Spillovers: A Multi-Regional Input-Output Analysis

5: Designing for Perception: Weather-Aware Streetscapes via Generative Modeling and Global Datasets

1: Construction of National Pseudo Human Flow Data: Evolution to Version 2.0

2: Advancing the Sensitivity Frontier in digital contact tracing: Comparative analysis of proposed methods toward maximized utility

3: Detecting Human Flow Surges to Identify Event-Driven Activation in a Small City

4: Destination Choice Model Utilizing Urban Knowledge Graph to Improve Accuracy of Behavior Prediction

5: Improvement of High Accuracy People Flow Simulation by Comparing Real Data and Synthetic Data

6: Spatio Heterogeneity Mixture-of-Experts Embedding for Traffic Forecasting

1: Construction of a digital twin for social mobility experiments incorporating diverse geospatial information and nationwide pseudo-human flow data

2: Shotengai Digital Twin: promoting stakeholder collaboration through decision support systems

3: Unveiling and Simulating the Activity Space-based Socioeconomic Segregation under Various Activity Contexts in Tokyo Metropolitan Area

4: Heterogeneous responses to built environment improvements in nonroutine activity participation among family types

5: Identification and Analysis of Consumer Behaviors by Age and Gender within Commercial Facilities in the 23 wards of Tokyo from GPS Data

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1: Developing a method for simultaneous extraction of building and road data from satellite imagery using deep learning