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HVAQ: A High-Resolution Vision-Based Air Quality Dataset
Computer Vision and Pattern Recognition · Submitted 18 Feb 2021
abstract compiled · 2026-09-23 · no language-model review
The authors' main claim 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.
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The abstract, sentence by sentence
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.
background contribution method result interpretation main claim
| # | sentence | role | the authors' own work | states the main conclusion | strength |
|---|---|---|---|---|---|
| s0 | Air pollutants, such as particulate matter, negatively impact human health. | background 1.00 | 0.04 | 0.02 | asserted 1.00 |
| s1 | Most existing pollution monitoring techniques use stationary sensors, which are typically sparsely deployed. | background 0.93 | 0.04 | 0.02 | asserted 0.99 |
| s2 | 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. | background 0.90 | 0.09 | 0.06 | hedged possibility 0.80 |
| s3 | Accurate pollution monitoring requires either densely deployed conventional point sensors, at-a-distance vision-based pollution monitoring, or a combination of both. | background 0.91 | 0.07 | 0.06 | asserted 0.99 |
| s4 | 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. | contribution 1.00 | 0.90 | 0.61 | asserted 0.98 |
| s5 | The dataset enables, for the first time, high spatial resolution evaluation of image-based air pollution estimation algorithms. | contribution 0.91 | 0.50 | 0.15 | asserted 0.98 |
| s6 | It contains PM2.5, PM10, temperature, and humidity data. | contribution 0.57 | 0.82 | 0.04 | asserted 1.00 |
| s7 | 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. | method 0.42 | 0.96 | 0.14 | asserted 1.00 |
| s8 | It is our intent and belief that this dataset can enable advances by other research teams working on air quality estimation. | future work 0.98 | 0.10 | 0.07 | hedged possibility 0.66 |
| s9 | Our dataset is available at https://github.com/implicitDeclaration/HVAQ-dataset/tree/master. | contribution 0.72 | 0.47 | 0.03 | asserted 0.99 |
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The paper
Original 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.
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