TinyNina: A Resource-Efficient Edge-AI Framework for Sustainable Air Quality Monitoring via Intra-Image Satellite Super-Resolution

Abstract

Nitrogen dioxide (NO$_2$) is a primary atmospheric pollutant and a significant contributor to respiratory morbidity and urban climate-related challenges. While satellite platforms like Sentinel-2 provide global coverage, their native spatial resolution often limits the precision required, fine-grained NO$_2$ assessment. To address this, we propose TinyNina, a resource-efficient Edge-AI framework specifically engineered for sustainable environmental monitoring. TinyNina implements a novel intra-image learning paradigm that leverages the multi-spectral hierarchy of Sentinel-2 as internal training labels, effectively eliminating the dependency on costly and often unavailable external high-resolution reference datasets. The framework incorporates wavelength-specific attention gates and depthwise separable convolutions to preserve pollutant-sensitive spectral features while maintaining an ultra-lightweight footprint of only 51K parameters. Experimental results, validated against 3,276 matched satellite-ground station pairs, demonstrate that TinyNina achieves a state-of-the-art Mean Absolute Error (MAE) of 7.4 $μ$g/m$^3$. This performance represents a 95% reduction in computational overhead and 47$\times$ faster inference compared to high-capacity models such as EDSR and RCAN. By prioritizing task-specific utility and architectural efficiency, TinyNina provides a scalable, low-latency solution for real-time air quality monitoring in smart city infrastructures.

Figures (10)

• Figure 1: Map illustrating the locations of air pollution monitoring stations that provide ground-truth data for NO2 pollutant levels.
• Figure 2: Detailed information on the twelve Sentinel-2 spectral bands for a specific location. The rows are color-coded to differentiate spatial resolutions: yellow highlights the 10m bands, red represents the 20m bands, and blue indicates the 60m bands, providing a clear overview of the wavelength and resolution for each band.
• Figure 3: End-to-end architecture for NO2 prediction integrating (1) Sentinel-2 data preprocessing, (2) TinyNina spectral super-resolution, and (3) ResNet50-based concentration estimation. The system maintains temporal synchronization between satellite acquisitions and ground station measurements while preserving spectral-spatial features critical for accurate pollution mapping.
• Figure 4: TinyNina's super-resolution architecture: (a) Spectral attention gates weight bands by NO2 sensitivity, (b) Depthwise separable convolutions reduce parameters while extracting spatial-spectral features, and (c) Residual upsampling with PixelShuffle generates high-resolution outputs.
• Figure 5: Training convergence comparison of super-resolution models across Naive SR and Channel SR tasks. The proposed TinyNina model demonstrates faster and more stable convergence compared to baseline architectures (EDSR, RCAN, and NinaB1). In the Channel SR setting, TinyNina achieves optimal performance within 50 epochs, while requiring substantially fewer training iterations than EDSR, which requires approximately 200 epochs to converge despite having 800$\times$ more parameters.