Generating and Maintaining Multi-Level 3D City Models Using Advanced Multi-Modal Image Processing

点击翻译按钮获取中文摘要

3D city models that present urban objects and structures, such as buildings, in a three-dimensional geometry are being increasingly used for a wide range of applications, including smart cities, urban planning, disaster management and security. 3D city models are generated in different Levels of Detail (LoD); for example, LoD1 only contains a rough block representation of buildings while LoD3 contains the delicate architectural structures as well as the location of the windows and doors within buildings. The LoD=3 models deal with the exterior of buildings and urban objects, while LoD4 focuses on building interiors. Different applications require different levels of detail. Thus, it is necessary to have multi-level representations of 3D city models to accommodate various requirements. Although 3D city models are vital in different Geomatics and Urban Management applications, producing the models with high LoDs (=2) is still costly and labor-intensive - thus, the development rate of such models for cities is prolonged. Consequently, only a few cities in the world have limited multi-level 3D city models (mainly up to LoD2). Furthermore, maintaining 3D city models is currently very challenging, hence in many applications, instead of updating, 3D city models are regenerated after a certain period. The long term objective of my research program is to develop breakthrough methods to automatically generate and maintain standards-compliant multi-level 3D city models, using a photogrammetric basis combined by learning methods in image processing. The short term objectives of this research program are to develop automatic methods to (1) generate 3D city models, (2) detect changes in the existing models and (3) update the models to maintain the LoD=3 city models (focusing on building exteriors). My grad students and I will combine modern learning techniques, i.e. machine learning and deep learning, and basic photogrammetric concepts to further develop what we have achieved up to date in terms of change detection and sensor modeling to pursue the short goals of this research program. For this purpose, we will use images taken from different angles (e.g. oblique and nadir), different platforms (e.g. terrestrial and airborne) and various sensors (e.g. multispectral, LiDAR) that are referred to as multi-modal images. To pursue the short term objectives, I am going to hire and train 9 HQP in total (2 PhD, 2 MSc, 5 BSc) in the field of Remote Sensing, Photogrammetry and GIS working at the Department of Geodesy and Geomatics Engineering, University of New Brunswick. This initiative will place Canada within the group of leading countries in this field, enabling Canadians to benefit from the opportunities provided by 3D city models in different applications such as disaster management and smart cities.