The Cheonji Lake GeoAI Dataset based in Optical Satellite Imagery: Landsat-5/-7/-8 and Sentinel-2

Abstract

The variations in the water area and water level of Cheonji, the caldera lake of Baekdu Mountain, serve as reliable indicators of volcanic precursors. However, the geographical and spatial features of Baekdusan make it impossible to directly observe the water area and water level. Therefore, it is crucial to rely on remote sensing data for monitoring purposes. Optical satellite imagery employs different spectral bands to accurately delineate the boundaries between water bodies and non-water bodies. Conventional methods for classifying water bodies using optical satellite images are significantly influenced by the surrounding environment, including factors like terrain and shadows. As a result, these methods often misclassify the boundaries. To address these limitations, deep learning techniques have been employed in recent times. Hence, this study aimed to create an AI dataset using Landsat-5/-7/-8 and Sentinel-2 optical satellite images to accurately detect the water body area and water level of Cheonji lake. By utilizing deep learning methods on the dataset, it is reasonable to consistently observe the area and level of water in Cheonji lake. Furthermore, by integrating additional volcanic precursor monitoring factors, a more accurate volcano monitoring system can be established.

Keywords

• waterbody
• optical satellite images
• ai
• segmentation