There exist different approaches for segmenting Very High Spatial Resolution (VHSR) remote sensing imagery with competitive performance, including object-based (e.g. Multiresolution), gradient-based (e.g. Watershed), and clustering-based (e.g. k-means) segmentation. However, they have a strong dependence on human assistance for tuning the required parameters (e.g. scale value, clusters number or tolerance thresholds), usually following a trial-and-error methodology that becomes tedious, hardly reproducible or transferable to other images, affecting negatively the methods' robustness and efficiency. In this communication, we propose a novel method denominated Line-based segmentation (LBS) that automatically segments VHSR remote sensing imagery through a data-driven approach, bypassing the parameters' definition by experts (i.e. region growing´s seeds and thresholds). The proposed algorithm offers flexibility and accuracy to segment regions with varying sizes and shapes, tested on different VHSR images, including multispectral images (WorldView-3, GeoEywe-1, Ikonos, QuickBird and SkySat), RGB aerial image (NAIP) and panchromatic image (Ikonos). The results revealed the LBS method shows a competitive performance compared against two well-known segmentation approaches, but without user intervention and generating consistent and repeatable segmentation results following an automatic fashion.