# Uncovering local aggregated air quality index with smartphone captured images leveraging efficient deep convolutional neural network

arXiv: https://arxiv.org/abs/2308.03200 · HTML: https://mlsanity.com/paper/2308.03200/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 62 references that are in the map.
- First posted on arXiv 6 Aug 2023.

## Builds on (most cited first, 7 of 7)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Learning Depth from Single Monocular Images Using Deep Convolutional Neural Fields](https://mlsanity.com/paper/1502.07411/) (2015)
- [Involution: Inverting the Inherence of Convolution for Visual Recognition](https://mlsanity.com/paper/2103.06255/) (2021)
- [Learning a No-Reference Quality Assessment Model of Enhanced Images With Big Data](https://mlsanity.com/paper/1904.08632/) (2019)
- [Full Transformer Framework for Robust Point Cloud Registration with Deep Information Interaction](https://mlsanity.com/paper/2112.09385/) (2021)
- [Using User Generated Online Photos to Estimate and Monitor Air Pollution in Major Cities](https://mlsanity.com/paper/1508.05028/) (2015)

## Built on by (most cited first, 4 of 4)

- [Tailoring Adversarial Attacks on Deep Neural Networks for Targeted Class Manipulation Using DeepFool Algorithm](https://mlsanity.com/paper/2310.13019/) (2023)
- [PM25Vision: A Large-Scale Benchmark Dataset for Visual Estimation of Air Quality](https://mlsanity.com/paper/2509.16519/) (2025)
- [M2FedAQI: Multimodal Federated Learning for Air Quality Prediction on Heterogeneous Edge Devices](https://mlsanity.com/paper/2605.16375/) (2026)
- [AQIFormer: A Transformer-Based Multi-View Architecture for Cross-City Air Quality Classification](https://mlsanity.com/paper/2606.07648/) (2026)

## Abstract (arXiv)

The prevalence and mobility of smartphones make these a widely used tool for environmental health research. However, their potential for determining aggregated air quality index (AQI) based on PM2.5 concentration in specific locations remains largely unexplored in the existing literature. In this paper, we thoroughly examine the challenges associated with predicting location-specific PM2.5 concentration using images taken with smartphone cameras. The focus of our study is on Dhaka, the capital of Bangladesh, due to its significant air pollution levels and the large population exposed to it. Our research involves the development of a Deep Convolutional Neural Network (DCNN), which we train using over a thousand outdoor images taken and annotated. These photos are captured at various locations in Dhaka, and their labels are based on PM2.5 concentration data obtained from the local US consulate, calculated using the NowCast algorithm. Through supervised learning, our model establishes a correlation index during training, enhancing its ability to function as a Picture-based Predictor of PM2.5 Concentration (PPPC). This enables the algorithm to calculate an equivalent daily averaged AQI index from a smartphone image. Unlike, popular overly parameterized models, our model shows resource efficiency since it uses fewer parameters. Furthermore, test results indicate that our model outperforms popular models like ViT and INN, as well as popular CNN-based models such as VGG19, ResNet50, and MobileNetV2, in predicting location-specific PM2.5 concentration. Our dataset is the first publicly available collection that includes atmospheric images and corresponding PM2.5 measurements from Dhaka. Our codes and dataset are available at https://github.com/lepotatoguy/aqi.