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September 5, 2025Environmental Data ScienceOpen Access

Air quality prediction from images in Indonesia: enhancing model explainability through visual explanation with AQI-net and grad-CAM

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Research employs deep learning to classify air quality using images, highlighting insights from grad-CAM and CNNs.

The study achieved an impressive accuracy of 99.81% for air quality classification.
Images were categorized into four air quality classes: good, moderate, unhealthy for sensitive groups, and unhealthy.
A dataset of 11,000 images from three Indonesian regions was collected for analysis.
Grad-CAM was used to enhance model explainability in classifying air quality conditions.

Alauddin et al. (2025) studied this question.

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September 5, 2025Environmental Data ScienceOpen Access

View Full Paper

Ask AI

Bookmark

Authors

Research employs deep learning to classify air quality using images, highlighting insights from grad-CAM and CNNs.

The study achieved an impressive accuracy of 99.81% for air quality classification.
Images were categorized into four air quality classes: good, moderate, unhealthy for sensitive groups, and unhealthy.
A dataset of 11,000 images from three Indonesian regions was collected for analysis.
Grad-CAM was used to enhance model explainability in classifying air quality conditions.

Alauddin et al. (2025) studied this question.

View Full Paper

Ask AI

Bookmark