Identification of undocumented buildings in cadastral data using remote sensing: Construction period, morphology, and landscape

Abstract

Buildings are the predominant objects that characterize the urban structure. For many cities, local governments establish building databases for administration as well as urban planning and monitoring. However, newly constructed buildings are often only included with a considerable time delay in the official digital cadastral maps due to processes in the acquisition of data, so-called undocumented buildings. In this regard, detecting undocumented buildings using remote sensing techniques would support the construction of update-to-date building databases with complementary information. In-depth studies on undocumented buildings and their number and location, however, are scarce. Therefore, we exploit a deep learning-based framework to detect undocumented buildings in remote sensing data and propose to derive 2D and 3D morphological parameters as well as landscape metrics., which are capable of depicting the physical forms and spatial structures of undocumented buildings. Furthermore, we exemplify the variabilities of undocumented buildings across space by the differences in morphology and landscape metrics between high and low building density regions. Upon analysis of undocumented buildings in 15 cities in the state of Bavaria, Germany, both state- and cityscale results reveal that most undocumented buildings are located in lower dense regions. This reveals that fragmentation of the landscape by building structures in the state of Bavaria is probably greater than official geospatial data currently documented.