papers

Publications (12)

cs.SI2025

Modeling shared micromobility as a label propagation process for detecting the overlapping communities

Peng Luo, Chengyu Song, Hao Li +2

Shared micro-mobility such as e-scooters has gained significant popularity in many cities. However, existing methods for detecting community structures in mobility networks often o…

cs.HC2025

Human vs. AI Safety Perception? Decoding Human Safety Perception with Eye-Tracking Systems, Street View Images, and Explainable AI

Yuhao Kang, Junda Chen, Liu Liu +5

The way residents perceive safety plays an important role in how they use public spaces. Studies have combined large-scale street view images and advanced computer vision technique…

cs.SI2021

Leveraging Artificial Intelligence to Analyze Citizens' Opinions on Urban Green Space

Mohammadhossein Ghahramani, Nadina J. Galle, Fabio Duarte +2

Continued population growth and urbanization is shifting research to consider the quality of urban green space over the quantity of these parks, woods, and wetlands. The quality of…

cs.AI2026

LocationReasoner: Evaluating LLMs on Real-World Site Selection Reasoning

Miho Koda, Yu Zheng, Ruixian Ma +4

Recent advances in large language models (LLMs), particularly those enhanced through reinforced post-training, have demonstrated impressive reasoning capabilities, as exemplified b…

cs.CV2021

Favelas 4D: Scalable methods for morphology analysis of informal settlements using terrestrial laser scanning data

Arianna Salazar Miranda, Guangyu Du, Claire Gorman +3

One billion people live in informal settlements worldwide. The complex and multilayered spaces that characterize this unplanned form of urbanization pose a challenge to traditional…

cs.CV2016

Indoor Space Recognition using Deep Convolutional Neural Network: A Case Study at MIT Campus

Fan Zhang, Fabio Duarte, Ruixian Ma +3

In this paper, we propose a robust and parsimonious approach using Deep Convolutional Neural Network (DCNN) to recognize and interpret interior space. DCNN has achieved incredible…