Urban Environment
Urban environment research focuses on understanding and improving various aspects of cities, from infrastructure and transportation to social dynamics and environmental impact. Current research employs diverse methods, including large language models (LLMs) for urban planning and autonomous systems, deep learning for image analysis and prediction of traffic flow and air quality, and advanced sensor fusion techniques like LiDAR and radar for navigation and mapping. These advancements are improving urban planning, resource management, and the development of safer, more efficient, and sustainable urban spaces, with implications for transportation, environmental monitoring, and public safety.
Papers
UrBAN: Urban Beehive Acoustics and PheNotyping Dataset
Mahsa Abdollahi, Yi Zhu, Heitor R. Guimarães, Nico Coallier, Ségolène Maucourt, Pierre Giovenazzo, Tiago H. Falk
Dynamic 3D Gaussian Fields for Urban Areas
Tobias Fischer, Jonas Kulhanek, Samuel Rota Bulò, Lorenzo Porzi, Marc Pollefeys, Peter Kontschieder
OFMPNet: Deep End-to-End Model for Occupancy and Flow Prediction in Urban Environment
Youshaa Murhij, Dmitry Yudin
Precise and Robust Sidewalk Detection: Leveraging Ensemble Learning to Surpass LLM Limitations in Urban Environments
Ibne Farabi Shihab, Benjir Islam Alvee, Sudesh Ramesh Bhagat, Anuj Sharma