A new approach to derive buildings footprint from light detection and ranging data using rule-based learning techniques and decision tree

[Display omitted] •Different features were distinguished based on intensity as a criterion.•Automatic correction of the part of the roofs masked by the crown of the trees is possible.•The entire process is performed in a fully automated system. Indeed, the developed program takes the Light Detection and Ranging data address as input and saves, as output, the building footprint map in the form of a shape file at the predefined output address.•The building footprint is extracted using no data layer but the Light Detection and Ranging data.•The implemented methodology needs no learning stage. Buildings are among the most important elements in the urban structure that can affect urban planning. Therefore, it is important to create the footprint of the buildings, especially in developing cities, which is highly time-consuming and costly. Although LiDAR technology has already been used for this purpose, the need to process voluminous amounts of noisy data and make building footprint extraction in accurate. In this study, we propose a step-by-step analysis of LiDAR data using a rule-based algorithm called DB-creator in order to automatically create building footprints. DB-creator was specifically chose as it does not require external data or region information to construct the footprints. The constructed footprints based from the algorithm was compared with manually created ground truth building footprints to assess accuracy. From experimental results, RMSE for urban and rural areas were ± 0.62 m and ± 0.28 m, respectively, which is highly accurate considering LiDAR’s a 0.5 m surveying distance between two points and 0.6 m distance between rows. Moreover, the kappa coefficient were 0.948 and 0.958 for the urban and rural areas, respectively (which are confirmed by T values of 150.204 and 255.553 at p ≤ 0.01 for the urban and rural areas, respectively). The Standard Errors respectively obtained for urban and rural areas were 0.001 and 0.002, reflecting slight internal variations between the built footprint maps. This also highlights the certainty of the kappa coefficient, indicating that extraction of building footprints is highly accurate.