A map-matching algorithm dealing with sparse cellular fingerprint observations

The widespread availability of mobile communication makes mobile devices a resource for the collection of data about mobile infrastructures and user mobility. In these contexts, the problem of reconstructing the most likely trajectory of a device on the road network on the basis of the sequence of observed locations (map-matching problem) turns out to be particularly relevant. Different contributions have demonstrated that the reconstruction of the trajectory of a device with good accuracy is technically feasible even when only a sparse set of GNSS positions is available. In this paper, we face the problem of coping with sparse sequences of cellular fingerprints. Compared to GNSS positions, cellular fingerprints provide coarser spatial information, but they work even when a device is missing GNSS positions or is operating in an energy saving mode. We devise a new map-matching algorithm, that exploits the well-known Hidden Markov Model and Random Forests to successfully deal with noisy and sparse cellular observations. The performance of the proposed solution has been tested over a medium-sized Italian city urban environment by varying both the sampling of the observations and the density of the fingerprint map as well as by including some GPS positions into the sequence of fingerprint observations

Bringing together Brazilian soil scientists to share soil data.

Brazilian soil scientists have recently created a soil data repository using community-built standards and following open data policies in an attempt to address the issues mentioned above. The Free Brazilian Repository for Open Soil Data - febr -, accessible through www.ufsm.br/febr, is a centralized repository targeted at storing open soil data and serving it in a standardized and harmonized format, for various applications. This paper describes the features of febr and the opportunities that it creates for soil science.Na publicação: Wenceslau Teixeira