HVAQ: A High-Resolution Vision-Based Air Quality Dataset

TL;DR

This paper introduces HVAQ, a novel high-resolution dataset combining point sensor data and images for improved air quality estimation, enabling more accurate and spatially detailed pollution monitoring.

Contribution

It provides the first publicly available high-resolution dataset with synchronized sensor and image data for air quality analysis.

Findings

Vision-based algorithms improve with higher sensor density.

Higher sensor density increases estimation accuracy.

The dataset facilitates evaluation of high-resolution air pollution estimation methods.

Abstract

Air pollutants, such as particulate matter, negatively impact human health. Most existing pollution monitoring techniques use stationary sensors, which are typically sparsely deployed. However, real-world pollution distributions vary rapidly with position and the visual effects of air pollution can be used to estimate concentration, potentially at high spatial resolution. Accurate pollution monitoring requires either densely deployed conventional point sensors, at-a-distance vision-based pollution monitoring, or a combination of both. The main contribution of this paper is that to the best of our knowledge, it is the first publicly available, high temporal and spatial resolution air quality dataset containing simultaneous point sensor measurements and corresponding images. The dataset enables, for the first time, high spatial resolution evaluation of image-based air pollution estimation algorithms. It contains PM2.5, PM10, temperature, and humidity data. We evaluate several state-of-art vision-based PM concentration estimation algorithms on our dataset and quantify the increase in accuracy resulting from higher point sensor density and the use of images. It is our intent and belief that this dataset can enable advances by other research teams working on air quality estimation. Our dataset is available at https://github.com/implicitDeclaration/HVAQ-dataset/tree/master.