| Popis: |
In urban areas, building rooftops are used for PV system installation for commercial or individual household purposes. An accurate solar map is essential to determine the amount of solar radiation falling on the rooftop of a building for PV installation. This study focuses on developing a methodology on how to configure a Drone to capture imageries for extracting features that are useful in assessing sun-exposed building rooftops. The focus is also to develop a geospatial model using GeoAI for processing Drone images and DSM to delineate actual solar exposed rooftops. Then, the model output was evaluated and compared with the existing models. The study was designed using DSR principles to develop software artifacts, a workflow model, and evaluation methods. This study provides a novel approach by combining image classifications using GeoAI for information extraction from Drone images for practical utility purposes. The study fulfilled the three research objectives. First, a flying configuration of a Drone to capture imagery for DSM creation. Second, the study fulfilled the development of a geospatial model using the DSM and GeoAI algorithm for delineating sun-exposed building rooftop. Third, this study evaluated the output of the model efficiency and reliability to determine whether this model can be used for enhancing the existing solar maps available from the government or other agencies. The utility of this model is a relevant topic in the shared economy research community, and no studies so far are focused designing a process that can create or augment inexpensive solar maps from Drone images. |
