Binned MSE for Imbalanced Dust Density Estimation

Airborne dust significantly affects numerous factors, making effective dust monitoring essential for understanding and mitigating its impact. Previous studies have demonstrated that dust density can be estimated from standard RGB images. However, pixel-wise estimation of dust concentrations is a complex task with many challenges. A significant challenge is the imbalance in the distribution of dust levels within images: dust can range from transparent to opaque, often resulting in a highly skewed distribution of dust intensities. This imbalance can lead to underestimation or overlooking of high dust concentration events. To address this issue, we propose a new balanced loss function, BinMSE, designed explicitly for pixel-wise visual regression to effectively handle imbalanced dust level distributions. We integrate our loss function into various dust density estimation algorithms and evaluate its effectiveness compared to other options on the Meteodata dust dataset.