Using User Generated Online Photos to Estimate and Monitor Air Pollution in Major Cities

Abstract

With the rapid development of economy in China over the past decade, air pollution has become an increasingly serious problem in major cities and caused grave public health concerns in China. Recently, a number of studies have dealt with air quality and air pollution. Among them, some attempt to predict and monitor the air quality from different sources of information, ranging from deployed physical sensors to social media. These methods are either too expensive or unreliable, prompting us to search for a novel and effective way to sense the air quality. In this study, we propose to employ the state of the art in computer vision techniques to analyze photos that can be easily acquired from online social media. Next, we establish the correlation between the haze level computed directly from photos with the official PM 2.5 record of the taken city at the taken time. Our experiments based on both synthetic and real photos have shown the promise of this image-based approach to estimating and monitoring air pollution.

ICIMCS '15

References in corpus (1)

• Learning Depth from Single Monocular Images Using Deep Convolutional Neural Fields

Cited by in corpus (11)

• Federated Learning in the Sky: Aerial-Ground Air Quality Sensing Framework with UAV Swarms
• Using Social Media to Predict the Future: A Systematic Literature Review
• Benchmarking Single Image Dehazing and Beyond
• MapLUR: Exploring a new Paradigm for Estimating Air Pollution using Deep Learning on Map Images
• Uncovering local aggregated air quality index with smartphone captured images leveraging efficient deep convolutional neural network
• Image Dehazing using Bilinear Composition Loss Function
• HVAQ: A High-Resolution Vision-Based Air Quality Dataset
• What Is It Like Down There? Generating Dense Ground-Level Views and Image Features From Overhead Imagery Using Conditional Generative Adversarial Networks
• Using Conditional Generative Adversarial Networks to Generate Ground-Level Views From Overhead Imagery
• Spatial Morphing Kernel Regression For Feature Interpolation
• Jurassic World Remake: Bringing Ancient Fossils Back to Life via Zero-Shot Long Image-to-Image Translation