  Sub-symbolic artificial intelligence methods dominate the fields of
environment-type classification and Simultaneous Localisation and Mapping.
However, a significant area overlooked within these fields is solution
transparency for the human-machine interaction space, as the sub-symbolic
methods employed for map generation do not account for the explainability of
the solutions generated. This paper proposes a novel approach to
environment-type classification through Symbolic Simultaneous Localisation and
Mapping, SymboSLAM, to bridge the explainability gap. Our method for
environment-type classification observes ontological reasoning used to
synthesise the context of an environment through the features found within. We
achieve explainability within the model by presenting operators with
environment-type classifications overlayed by a semantically labelled occupancy
map of landmarks and features. We evaluate SymboSLAM with ground-truth maps of
the Canberra region, demonstrating method effectiveness. We assessed the system
through both simulations and real-world trials.
