Venue: ESPC 2022 Competition

Reviews:

final_score: rating: 4: Accept + Potential Winner. This work can be presented and it is a strong contender to win this competition.
review_content: summary: The authors provide a real time AQI estimation combing image processing, sensor data, and visibility using BRISQUE.
strengths: 1. The authors provide an end to end solution from data collection, data processing, to knowledge discovery.
2. The video presentation and the report are well presented.

weaknesses: 1. The authors mention that the main motivation of carrying this project is because the lifespan of the sensors (10-12 months). However, in this project, the authors use camera and two environmental sensors. Environmental sensors still are sensors suffers from short lifespan. From this point of view, the proposed method conflicts with its' motivation. 
2. The main weaknesses of this approach is that it estimates the pollution levels ( five levels, discrete value) instead of pollutant concentrations (continuous value). This approach cannot be used for real time AQI monitoring for concentration, which limit the usage of this approach to provide rich service to users.
3. The dataset is not balanced. It is not clear how this approach behaves on different pollution levels. The classification matrix could be provided. Based on the current evaluation, it is hard to believe the proposed method works properly.
4. R2 score for the features is presented. However, it would be nice to see the performance in different feature combinations. 
5. Novel is not strong from the technique point of view.
review_title: null

final_score: rating: 4: Accept + Potential Winner. This work can be presented and it is a strong contender to win this competition.
review_content: summary: The author has presented an end-end solution for Air quality measurement 
strengths: 1. Video demonstration is very clear.
2. Interesting idea of measuring AQI using images

weaknesses: 1. Should have presented the demo at normal speed.
2. What is the accuracy of this technique with the sensor-based AQI measurement?
3. Is the algorithm depends on the camera and resolution as well?
review_title: null

final_score: rating: 3: Accept. This work can be presented during the event, but it is not a contender to win the competition.
review_content: summary: The authors have built a system that uses cameras to predict the AQI. The system relies on ground truth information from publicly datasets, and some measurements conducted by the authors. 
strengths: - The authors have built an end-to-end system
- The devices and components used are easilty available making them easy to deploy. 
- The system architecture is detailed and the presentation is good (although difficult to understand given the 2-3x speed of the video)
- The authors have explained the strengths, challenges and avenues to future work. 
weaknesses: - The key shortcoming is the black box ML approach, where the authors have failed to details they steps they took to improve the explainability.  Why is the accuracy so low and what are the key factors that affect the accuracy? It seems the authors are using a black box approach. For instance, how is the model predicting the AQI? Have they explored the sensitivitiy of each of the variables? How sensitive is the system to the camera model? What happens if I change the camera?
- It seems that the dataset / model misses out on bicycles and cycle rickshaws. Also can this model be useful in parks in the city which are adjacent to the roads. For instance, what happens if the picture is taken inside a park which is next to a busy road? What is a bike? How does it distinguish between a cycle, electric bike, and a motorcycle? Are all motorcycles considered to be the same? Simialrly, does a small car pollute the same as a big car or SUV? 
- What are the variables that actually affect the analysis? Will time-of-day be useful in places like Helsinki where there is virtually no light after 15.00 now, and there is light till 23:00 during the summers. In such a situation wouldnt light intensity be the variable that you are actually interested in? 
- Does your work implicitly imply that one has to use different models for different seasons? If yes, how does the system know what is the season?

Some nitpicks and suggestions.
- Please do not speed up the videos. It is very difficult to understand what you are planning to say. 
- What happens when there is some dust or dirt on the cameras? 
- Can this be used for unlawful surveillance? What steps would have to be taken to ensure that the privacy of the people is not violated?
- Can this be part of a mobile app, where we take a picture and the output will the AQI?
review_title: null

Decision: Accept