The aim of this study is to map and analyze the lineament network in the Edéa, Cameroon, area using remote sensing and gravimetric data to determine their hydrogeological implications. Principal component analysis and directional filters applied to Landsat7 ETM+ and Shuttle Radar Topography Mission imagery, respectively, were used to extract remote sensing lineaments. Rose diagram of these lineaments highlights four families of lineaments along the N–S, E–W, NE–SW, and NW–SE directions. There are three major directions accounting for 74% of lineaments, including N0° to N10°, N20° to N30°, and N40° to N50°; and four minor directions (with 26% of the lineaments), including N60° N70°, N80° to N90°, N130° to N140°, and N150° to N160°. N20° to N90° directions correlate with those of major structures of the Oubanguides Complex, such as the Sanaga Fault and Central Cameroon Shear Zone. N130° to N140° direction corresponds to orientation of Shear Zones and blastomylonitic faults of Nyong Complex. Superposition of these lineaments on hydrographic network shows similarities between their directions, thus highlighting strong impact of tectonics on orientation of hydrographic network. The presence of numerous lineaments highlights strongly fractured subsoil, and their high density favors the circulation and accumulation of groundwater. Upward continuation and horizontal gradient maxima methods applied to Earth Gravitational Model 2008 data allowed the extraction of gravimetric lineaments, with a major N–S orientation, which correlates with general orientation of South Atlantic opening. Superposition of remote sensing lineaments and gravimetric lineaments highlights their parallelism, admitting that gravimetric structures are an extension in depth of surface structures defined by remote sensing.

The rapid, accurate, and intelligent extraction of vegetation areas is of great significance for conducting research on forest resource inventory, climate change, and the greenhouse effect. Currently, existing semantic segmentation models suffer from limitations such as insufficient extraction accuracy (ACC) and unbalanced positive and negative categories in datasets. Therefore, we propose the Attention U-Net model for vegetation extraction from Landsat8 operational land imager remote sensing images. By combining the convolutional block attention module, Visual Geometry Group 16 backbone network, and Dice loss, the model alleviates the phenomenon of omission and misclassification of the fragmented vegetation areas and the imbalance of positive and negative classes. In addition, to test the influence of remote sensing images with different band combinations on the ACC of vegetation extraction, we introduce near-infrared (NIR) and short-wave infrared (SWIR) spectral information to conduct band combination operations, thus forming three datasets, namely, the 432 dataset (R, G, B), 543 dataset (NIR, R, G), and 654 dataset (SWIR, NIR, R). In addition, to validate the effectiveness of the proposed model, it was compared with three classic semantic segmentation models, namely, PSP-Net, DeepLabv3+, and U-Net. Experimental results demonstrate that all models exhibit improved extraction performance on false color datasets compared with the true color dataset, particularly on the 654 dataset where vegetation extraction performance is optimal. Moreover, the proposed Attention U-Net achieves the highest overall ACC with mean intersection over union, mean pixel ACC, and ACC reaching 0.877, 0.940, and 0.946, respectively, providing substantial evidence for the effectiveness of the proposed model. Furthermore, the model demonstrates good generalizability and transferability when tested in other regions, indicating its potential for further application and promotion.