Recent advancements in artificial neural networks that focus on reconstructing 3D meshes from input 2D images show great potential and significant practical value in a multitude of GIS applications. This series of posts describes our experiments with one such neural network architecture that we applied to reconstruct 3D building shells from various types of remotely sensed data. This post, the first of the series, describes extracting buildings from elevation rasters, specifically, normalized digital surface model rasters. Modern municipal governments and national mapping agencies are evolving their traditional 2D geographic datasets into 3D interactive and realistically looking digital twins to optimize results from planning an analysis projects. For example, some local governments that are responsible for urban design, public events planning, safety, pollution monitoring, solar radiation potential assessment, etc. rely more and more on this kind of data.