RGB-BASED DEEP SURFACE WATER CONTOUR DETECTION
Keywords: Surface Water Detection, Deep Learning, Waterbody Detection, Water Contour Detection, Data Collection
Abstract. The application of remote monitoring of surface water has focused primarily on the detection of water bodies using expensive multi-spectral IR sensors. However, critical information about surface water bodies, particularly the dynamic behavior, is better derived from water contours. We show that water body detection is inadequate in accurately capturing the contours. Furthermore, we argue that RGB-based detection should be sufficient for accurate water detection. We present a new global dataset of remote sensing images obtained from Sentinel-2 and Landsat-8 missions and contour labeled to assist in this effort. We propose a unique UNet-style contour detection system that utilizes multiscale filters to detect contours accurately. Comparisons between our proposed system, existing water detection, and other segmentation and contour detection systems show the system's effectiveness in detecting water.