StreamCollab: A Streaming Crowd-AI Collaborative System to Smart Urban Infrastructure Monitoring in Social Sensing
Keywords:Social Sensing, Streaming Crowd-AI Collaboration, Smart Urban Infrastructure Monitoring
AbstractSocial sensing has emerged as a pervasive and scalable sensing paradigm to collect observations of the physical world from human sensors. A key advantage of social sensing is its infrastructure-free nature. In this paper, we focus on a streaming urban infrastructure monitoring (Streaming UIM) problem in social sensing. The goal is to automatically detect the urban infrastructure damages from the streaming imagery data posted on social media by exploring the collective power of both AI and human intelligence from crowdsourcing systems. Our work is motivated by the limitation of current AI and crowdsourcing solutions that either fail in many critical time-sensitive UIM application scenarios or are not easily generalizable to monitor the damage of different types of urban infrastructures. We identify two critical challenges in solving our problem: i) it is difficult to dynamically integrate AI and crowd intelligence to effectively identify and fix the failure cases of AI solutions; ii) it is non-trivial to obtain accurate human intelligence from unreliable crowd workers in streaming UIM applications. In this paper, we propose StreamCollab, a streaming crowd-AI collaborative system that explores the collaborative intelligence from AI and crowd to solve the streaming UIM problem. The evaluation results on a real-world urban infrastructure imagery dataset collected from social media demonstrate that StreamCollab consistently outperforms both state-of-the-art AI and crowd-AI baselines in UIM accuracy while maintaining the lowest computational cost.
How to Cite
Zhang, Y., Shang, L., Zong, R., Wang, Z., Kou, Z., & Wang, D. (2021). StreamCollab: A Streaming Crowd-AI Collaborative System to Smart Urban Infrastructure Monitoring in Social Sensing. Proceedings of the AAAI Conference on Human Computation and Crowdsourcing, 9(1), 179-190. https://doi.org/10.1609/hcomp.v9i1.18950
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