AUTOMATIC 3D BUILDING MODEL GENERATION FROM LIDAR AND IMAGE DATA USING SEQUENTIAL MINIMUM BOUNDING RECTANGLE

Abstract. Digital Building Model is an important component in many applications such as city modelling, natural disaster planning, and aftermath evaluation. The importance of accurate and up-to-date building models has been discussed by many researchers, and many different approaches for efficient building model generation have been proposed. They can be categorised according to the data source used, the data processing strategy, and the amount of human interaction. In terms of data source, due to the limitations of using single source data, integration of multi-senor data is desired since it preserves the advantages of the involved datasets. Aerial imagery and LiDAR data are among the commonly combined sources to obtain 3D building models with good vertical accuracy from laser scanning and good planimetric accuracy from aerial images. The most used data processing strategies are data-driven and model-driven ones. Theoretically one can model any shape of buildings using data-driven approaches but practically it leaves the question of how to impose constraints and set the rules during the generation process. Due to the complexity of the implementation of the data-driven approaches, model-based approaches draw the attention of the researchers. However, the major drawback of model-based approaches is that the establishment of representative models involves a manual process that requires human intervention. Therefore, the objective of this research work is to automatically generate building models using the Minimum Bounding Rectangle algorithm and sequentially adjusting them to combine the advantages of image and LiDAR datasets.