Abstract. This paper presents a novel methodology for the automated creation of 3D city models for Mexican cities using exclusively open data. In Mexico, while national topographic and elevation datasets exist, they lack crucial features like individual building footprints and road polygons, making it difficult to create 3D city models using the most common existing methodologies. The proposed method addresses these limitations by generating building footprints directly from high-resolution DSMs using a region-growing algorithm and deriving road polygons from the empty spaces between city blocks in the topographic data. These generated features, along with existing data for plant cover and water bodies, are then lifted to 3D using customisable rules. The methodology was implemented with Python and C++ scripts and tested in central Mexico City. Results show that the generated building footprints are often more accurate than those in global datasets (Microsoft, Google), particularly for non-rectilinear buildings, leading to recognisable city landmarks. However, the method has limitations, including missing approximately 30% of smaller buildings and occasionally misclassifying tall vegetation as buildings. Despite this, the work demonstrates the feasibility of creating useful 3D city models for the areas in Mexico with high-resolution elevation data.
Ohori et al. (Mon,) studied this question.