Road map extraction from satellite imagery using connected component analysis and landscape metrics

Road map extraction is considered an essential task in GIS as its results are the basis of location-based applications in various domains. Examples include GPS navigation on cell phone, delivery route optimization and planning, tourist attraction locator, and location-based marketing. Satellite imagery, one of the big spatial data sources, is used in this research — though other types of remotely-sensed images can also be applied, such as aerial photographs from aircrafts, UAVs or drones. Despite several methods and techniques proposed and accompanied with different performance criteria, the focus was mainly on the accuracy aspect rather than the completeness of the result sets. That is, the results were said to be satisfactory if it met a certain accuracy criterion associated with some benchmark data sets, regardless of whether all results were retrieved. In many cases; however, both accuracy and completeness are equally important. In this paper, we enhance the result accuracy by incorporating connected component analysis into the method as well as the completeness performance by utilizing an ecology concept, called Landscape Metrics, which describes spatial characteristics, patterns, and correlations of areas/patches through different indices. Two types of metrics are used: shape metrics and isolation metrics. The performance is evaluated based on four criteria: precision, recall, quality, and F1 scores. The results show that more than 90% of performance is achieved in all four criteria.