3D building models with prototypical roofs are more valuable in many applications than 2D building footprints. This research proposes a hybrid approach, combining the data-and model-driven approaches for generating LoD2-level building models by using medium resolution (0.91 m) LiDAR nDSM, the 2D building footprint and the high resolution orthophoto for the City of Indianapolis, USA. The main objective is to develop a GIS-based procedure for automatic reconstruction of complex building roof structures in a large area with high accuracy, but without requiring high-density point data clouds and computationally-intensive algorithms. A multi-stage strategy, which combined step-edge detection, roof model selection and ridge detection techniques, was adopted to extract key features and to obtain prior knowledge for 3D building reconstruction. The entire roof can be reconstructed successfully by assembling basic models after their shapes were reconstructed. This research finally created a 3D city model at the Level of Detail 2 (LoD2) according to the CityGML standard for the downtown area of Indianapolis (included 519 buildings).The reconstruction achieved 90.6% completeness and 96% correctness for seven tested buildings whose roofs were mixed by different shapes of structures. Moreover, 86.3% of completeness and 90.9% of correctness were achieved for 38 commercial buildings with complex roof structures in the downtown area, which indicated that the proposed method had the ability for large-area building reconstruction. The major contribution of this paper lies in designing an efficient method to reconstruct complex buildings, such as those with irregular footprints and roof structures with flat, shed and tiled sub-structures mixed together. It overcomes the limitation that building reconstruction using coarse resolution LiDAR nDSM cannot be based on precise horizontal ridge locations, by adopting a novel ridge detection method.

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Green roofs and rooftop solar photovoltaic (PV) systems are two popular mitigation strategies to reduce the net building energy demand and ease urban heat island (UHI) effect. This research tested the potential mitigation effects of green roofs and solar photovoltaic (PV) systems on increased buildings energy demand caused by climate change in Los Angeles County, California, USA. The mitigation effects were assessed based on selected buildings that were predicted to be more vulnerable to climate change. EnergyPlus software was used to simulate hourly building energy consumption with the proper settings of PV-green roofs. All buildings with green roofs showed positive energy savings with regard to total energy and electricity. The savings caused by green roofs were positively correlated with three key parameters: Leaf Area Index (LAI), soil depth, and irrigation saturation percentage. Moreover, the majority of the electricity-saving benefits from green roofs were found in the Heating, Ventilation, and Cooling (HVAC) systems. In addition, this study found that green roofs have different energy-saving abilities on different types of buildings with different technologies, which has received little attention in previous studies.