Cross-view Geo-localisation is typically performed at a coarse granularity, because densely sampled satellite image patches overlap heavily. This heavy overlap would make disambiguating patches very challenging. However, by opting for sparsely sampled patches, prior work has placed an artificial upper bound on the localisation accuracy that is possible. Even a…
Cross-View Geo-Localisation within urban regions is challenging in part due to the lack of geospatial structuring within current datasets and techniques. We propose utilising graph representations to model sequences of local observations and the connectivity of the target location. Modelling as a graph enables generating previously unseen sequences by sampling…
Cross-view image matching for geo-localisation is a challenging problem due to the significant visual difference between aerial and ground-level viewpoints. The method provides localisation capabilities from geo-referenced images, eliminating the need for external devices or costly equipment. This enhances the capacity of agents to autonomously determine their position, navigate, and…
Low Power Wide Area Networks (LPWANs) are a subset of IoT transmission technologies that have gained traction in recent years with the number of such devices exceeding 200 million. This paper considers the scalability of one such LPWAN, LoRaWAN, as the number of devices in a network increases. Various existing…
