Application of Convolutional Neural Networks With Object-Based Image Analysis for Land Cover and Land Use Mapping in Coastal Areas: A Case Study in Ain Témouchent, Algeria

Abstract

Land use and land cover (LULC) information is a fundamental component of environmental research relating to urban planning, agricultural sustainability, and natural hazards assessment. In particular, remote sensing technology has demonstrated a powerful capacity for LULC modeling with a corresponding increase in sensor number and type. Here, an advanced convolutional neural network (CNN) deep learning model was developed in combination with object-based image analysis (OBIA) to map LULC in Ain Témouchent coastal area, western Algeria, using Sentinel-2 and Pléiades imagery data. First, the CNN model was constructed based on convolution, hidden, and max pooling layers. The parameters of CNN architecture were optimized to improve the model for further processing. Then, based on high levels of CNN feature extraction, the OBIA was applied to classify the segmented objects, and detect the LULC features. Furthermore, machine learning methods, including random forest (RF) and support vector machines (SVM) were tested for comparison. The proposed method achieved a high overall accuracy (93.5%) using Pléiades imagery, revealing significant improvements compared to other machine learning techniques. Accordingly, it was concluded that the method proposed here is useful for LULC detection, and can be applied at larger scales in coastal areas. The derived maps can also inform regional and national-level decision making.