Coastal erosion is a major problem that has been worsened by climate change, along with other natural occurrences like droughts and marine flooding. Countries situated along coastlines are facing significant challenges when it comes to preserving their land and protecting their people and assets. To mitigate the damage caused by the encroachment of the sea on land, effective monitoring tools and methods are required. Several remote sensing techniques and methods have been developed to address these issues, including machine learning and deep learning methods. In this study, objectoriented analysis (OBIA), pixel-oriented analysis (PBIA), and convolutional neural network (CNN) methods are used to automatically detect and extract the Greater Libreville coastline, based on Pléiades very high-resolution satellite image dating from 2022. Three test areas were chosen there and then extracted. The first zone is located in the north of the municipality of Akanda (marked by the presence of small coastal cliffs). The second zone is located in the commune of Libreville (sandy beach). The third zone is located in the municipality of Owendo (artificialized beach, mostly muddy). The images of the three zones were the subject of a classification based on the methods mentioned above. Results of proposed methodologies showed competitive Overall accuracy (OA) values obtained with OBIA method and the CNN model. However, the OBIA method using Random Forest algorithm (RF) achieved the highest accuracy rates, which reached 95%, 90%, and 80% for the 3 test areas respectively.