A computer-implemented method for automatic geometry generation for stripe-shaped objects for localization and/or map updates, the method comprising: identifying (S101) an image and a corresponding three-dimensional point cloud including a roadway having one or more stripe-shaped objects; determining (S103) at least one targeted region within the image, wherein the at least one targeted region designates a plurality of pixels in the image, and wherein the at least one targeted region is shaped to intersect only part of the one or more stripe-shaped objects and overlaps a plurality of pixels being a fraction of the total image; performing (S105), by a deep learning model, an image analysis on the image to determine when the at least one target region having the plurality of pixels includes pixels in common with the one or more stripe-shaped objects; constructing (S107) a lane-line geometry of the one or more stripe-shaped objects by connecting the pixels in common using a line fitting technique; modifying the lane-line geometry using the corresponding three-dimensional point cloud, the modifying comprising: determining at least one point in the corresponding three-dimensional point cloud that correlates with one of the pixels in common of the one or more stripe-shaped objects in the image, determining a shape of the one or more stripe-shaped objects in the three-dimensional point cloud based on the determined correlation, and modifying the lane-line geometry based on the determined shape; and using the modified lane-line geometry of the one or more stripe-shaped objects for localization and/or map updates. identifying (S101) an image and a corresponding three-dimensional point cloud including a roadway having one or more stripe-shaped objects; determining (S103) at least one targeted region within the image, wherein the at least one targeted region designates a plurality of pixels in the image, and wherein the at least one targeted region is shaped to intersect only part of the one or more stripe-shaped objects and overlaps a plurality of pixels being a fraction of the total image; performing (S105), by a deep learning model, an image analysis on the image to determine when the at least one target region having the plurality of pixels includes pixels in common with the one or more stripe-shaped objects; constructing (S107) a lane-line geometry of the one or more stripe-shaped objects by connecting the pixels in common using a line fitting technique; modifying the lane-line geometry using the corresponding three-dimensional point cloud, the modifying comprising: determining at least one point in the corresponding three-dimensional point cloud that correlates with one of the pixels in common of the one or more stripe-shaped objects in the image, determining a shape of the one or more stripe-shaped objects in the three-dimensional point cloud based on the determined correlation, and modifying the lane-line geometry based on the determined shape; and determining at least one point in the corresponding three-dimensional point cloud that correlates with one of the pixels in common of the one or more stripe-shaped objects in the image, determining a shape of the one or more stripe-shaped objects in the three-dimensional point cloud based on the determined correlation, and modifying the lane-line geometry based on the determined shape; and using the modified lane-line geometry of the one or more stripe-shaped objects for localization and/or map updates. <EOS>