no code implementations • 26 Feb 2024 • Temitope Akinboyewa, Huan Ning, M. Naser Lessani, Zhenlong Li
Results show that the proposed approach can rapidly provide a consistent and reliable estimation of floodwater depth from flood photos.
1 code implementation • 10 May 2023 • Zhenlong Li, Huan Ning
Large Language Models (LLMs), such as ChatGPT, demonstrate a strong understanding of human natural language and have been explored and applied in various fields, including reasoning, creative writing, code generation, translation, and information retrieval.
no code implementations • 16 Aug 2022 • Yuqin Jiang, Andrey A. Popov, Zhenlong Li, Michael E. Hodgson
In this paper, we propose an optimal sensors-based simulation method for spatiotemporal event detection using human activity signals derived from taxi trip data.
1 code implementation • 8 Feb 2021 • Zhenlong Li, Xiao Huang, Xinyue Ye, Yuqin Jiang, Martin Yago, Huan Ning, Michael E. Hodgson, Xiaoming Li
In this study, we introduced a global multi-scale place connectivity index (PCI) based on spatial interactions among places revealed by geotagged tweets as a spatiotemporal-continuous and easy-to-implement measurement.
Social and Information Networks
no code implementations • 26 Nov 2020 • Yago Martin, Zhenlong Li, Yue Ge
The study of migrations and mobility has historically been severely limited by the absence of reliable data or the temporal sparsity of the available data.
no code implementations • 11 Jul 2020 • Dong Xu, Xiao Huang, Joseph Mango, Xiang Li, Zhenlong Li
We propose a multi-exit evacuation simulation based on Deep Reinforcement Learning (DRL), referred to as the MultiExit-DRL, which involves in a Deep Neural Network (DNN) framework to facilitate state-to-action mapping.
no code implementations • 28 Dec 2019 • Xiao Huang, Dong Xu, Zhenlong Li, Cuizhen Wang
The results of this study prove the possibility of multispectral-to-nighttime translation and further indicate that, with the additional social media data, the generated nighttime imagery can be very similar to the ground-truth imagery.