| dc.description.abstract | The two main challenges of the twenty-first century are the scarcity of energy sources and global warming; trigged by the emission of greenhouse gases. In this context, solar energy became increasingly relevant. Because it makes optimal use of the resources, minimizes environmental impacts, and is sustainable over time.
However, before installing solar panels, it is convenient pre-assessing the amount of energy that a building can harvest. This study proposes a methodology to semi-automatically generate information a building scale; on a large area.
This thesis integrates airborne Light Detection and Ranging (LiDAR) and WoldView-2 satellite data for modelling the solar energy potential of building rooftops in San Francisco, California. The methodology involved building detection solar potential analysis, and estimations at building scale.
First, the outline of building rooftops is extracted using an object-based approach. Next, the solar modelling is carried out using the solar radiation analysis tool in ArcGIS, Spatial Analyst. Then, energy that could potentially be harvested by each building rooftop is estimated. The energy estimation is defined in economic and environmental terms. | en |
