The concept of assessing surface corrosion of a steel railway bridge was born as a response to the industry need for quick and non-manual assessment of the progress of surface damage of monochromatic bridges. The fourth coordinate of the point cloud, through imaging algorithms, can indicate homogeneous surfaces of the bridge, and therefore subject to corrosion processes or not. The intensity of the object's point cloud, through the use of unsupervised classification tools, ensures the detection of changes in the surface properties of a mono-colored railway bridge. The classification method of unsupervised raster representation of grayscale reflectance intensity (generated from TLS data), as in the case of classical remote sensing, produces classes of pixels with similar reflectance properties. The concept of scientific research on the detection of corrosion progress on a steel railway bridge using an active short-range remote sensing system assumed the development of algorithmic advances allowing for the comparison of periodic classifications of rasters from point clouds. Thanks to the differentiation of images, it is possible to determine changes in the location and extent of corrosion, the rate of its progress in the case of aged steel objects, and the detection of scratches and cracks as critical points of the structure, proving the fulfillment of the operational capabilities of the object, as provided for in the technical documentation. The study indicated the empirical basis for conducting research on automatic corrosion detection.