Environment-Specific Fog Detection over the Korean Peninsula Using GEO-KOMPSAT-2A and DeepLabV3+

Fog detection using geostationary satellite data has the advantage of monitoring large areas in a short period of time. However, because fog exhibits highly diverse optical characteristics in both space and time, it is difficult to achieve reliable detection with a single satellite-based detection strategy that does not consider environmental conditions. Therefore, this study utilized data from GEO-KOMPSAT-2A (GK2A) to pre-define fog occurrence environments, construct appropriate input data and labels for each environmental condition, and then applied a categorized deep learning-based fog detection system.

First, fog was identified when ground-station visibility was under 1 km. To create reliable training data, the ground-station visibility data was spatially aligned with fog labels from the Korea Meteorological Administration (KMA) for GK2A observations. Only areas consistently identified as fog by both ground-station observations and KMA fog labels were selected and cropped. In this process, a spatial grouping method was used to eliminate noise and ensure the fog regions had continuous spatial coverage.        

In constructing the input data, variables representing surface characteristics were chosen to optimize detection accuracy for each environmental condition. Using this high-quality dataset, data were organized into different groups based on four seasons, three time periods (daytime, nighttime, dawn/dusk), and two surface types (land, ocean). Separate DeepLabV3+ models were trained for each category, with 2022 data used for training and 2023 data for validation.

To evaluate the model's ability to generalize, the entire 2024 dataset not included in training was used as an independent test set. For accurate assessment, post-processing filtering with a cloud mask was applied to measure detection performance in cloud-free regions. The results revealed notable seasonal fluctuations in performance, indicating that detection efficiency depends on environmental conditions. Even with the same deep learning architecture, this suggests that careful data preprocessing and environment-specific strategies can help advance satellite-based fog detection technology.

 

This work was supported by the National Research Foundation of Korea(NRF) grant funded by the Korea government(MSIT)(RS-2025-00515357).