Anthropogenic infrastructure such as roads, ditches and culverts have strong impacts on hydrological processes, particularly surface drainage patterns. Despite this, these structures are often not present in the digital elevation models (DEMs) used to provide surface drainage data to hydrological models, owing to the coarse spatial resolution of many available DEMs. Modelling drainage patterns in human-modified landscapes requires very accurate, high-resolution DEM data to capture these features. Light Detection And Ranging (LiDAR) is a remote sensing technique that is used for producing DEMs with fine resolutions that can represent anthropogenic landscapes features such as human modifications on the landscape such as roadside ditches. In these data, roads act as a barrier to flow and are treated as dams, where on the ground culverts and bridges exist. While possible to locate and manually enforce flow across these roads, there is currently no automated technique to identify these locations and perform flow enforcement. This research improves the modelling of surface drainage pathways in rural anthropogenic altered landscapes by utilizing a novel algorithm that identifies ditches and culverts in LiDAR DEMs and enforces flow through these features by way of breaching. This breaching algorithm was tested on LiDAR datasets for two rural test sites in Southern Ontario. These analyses showed that the technique is an effective tool for efficiently incorporating ditches and culverts into the hydrological analysis of a landscape that has both a gradient associated with it, as well as a lack of densely forested areas. The algorithm produced more accurate representations of both overland flow when compared to outputs that excluded these anthropogenic features all together.