Application of Artificial Intelligence for Monitoring Shoreline Changes in the Central Coast of Vietnam

Abstract

The identification and monitoring of coastline and shoreline plays an important role in coastal erosion assessment. The deep learning models can be a potential tool to detect the coastlines and shorelines in Vietnam using ultra-high resolution satellite images. The aims of the study are: i) To propose a set of indicators to determine the coastlines and shoreline; ii) To build deep machine learning models that automatically interpret the coastlines and shorelines on ultra-high resolution remote sensing images; and iii) To apply developed deep learning (DL) models to monitor coastal erosion in central Vietnam. Eight DL models were implemented based on four artificial intelligence network structures, including U-Net, U2-Net, U-Net3+, and DexiNed. Satellite images collected through Google Earth Pro software were used as input for all models. As a result, the U-Net model has been effectively applied to coasts in Cu De, Lai Giang, and Bien Lo estuaries,. The output results were used to calculate the rate of erosion/accretion in these areas. Additionally, the study indicated that coastline is a suitable criterion in assessing coastal erosion under the impact of sea level rise during storms. On the other hand, shoreline is a suitable criterion in assessing tidal fluctuations or instantaneous movements of wave currents during the year.