Developing a Data Model for an Omnidirectional Image-Based Multi-Scale Representation of Space

One of the major challenges that existing spatial data is facing is the fragmentation of its representation of indoor and outdoor space. As studies in the use of omnidirectional images in representing space and providing Location-based Services (LBS) has been increasing, the representation of the different scales of space, both in indoors and outdoors, has yet to be addressed. This study aims to develop a data model for generating a multi-scale image-based representation of space using omnidirectional images based spatial relationships. This paper identifies the different scales of space that are represented in spatial data and extends previous approaches of using omnidirectional images in providing indoor LBS towards representing the other scales of space, particularly in outdoor space. Using a sample data, we present an experimental implementation to demonstrate the potential of the proposed data model. Results show that apart from the realistic visualization that image data provides, basic spatial functions can be performed on the image data constructed based on the proposed data model.