Death of Dr Francisco Javier Ayala-Carcedo, Spanish INHIGEO Member, at Burgos, Spain, 28 November 2004

Death of Dr Francisco Javier Ayala-Carcedo, Spanish lNHIGEO Member, at Burgos, Spain, 28 November 2004

Methodology for the metric restoration of the historical cartography applied to Francisco Coello's cartografic series of the Royal Site of Aranjuez

Hojas Kilométricas (Kilometric Sheets). Specifically, the study focuses on those sheets referring to the city centre and surrounding area of the Royal Site of Aranjuez, a town in the south of the Province of Madrid. The aim of this study is to restore the actual size and measurements of scanned images of the Hojas Kilométricas. This would allow us, among other things, to reestablish both the format and scale of the original plans. To achieve this goal it is necessary to rectify and then georeference these images, i.e. assign them a geographic reference system. This procedure is essential in the overlaying and comparison of the Hojas Kilométricas of the Royal Site with other historical cartography as well as other sources related to the same area from different time periods. Subsequent research would allow us, for example, to reconstruct the time-evolution of the urban area, to spot new construction and to pinpoint the locations of any altered or missing buildings or architectural features. In addition, this would allow us to develop and integrate databases for GIS models applicable to the management of our cultural heritage.

Floating car data augmentation based on infrastructure sensors and neural networks

The development of new-generation intelligent vehicle technologies will lead to a better level of road safety and CO2 emission reductions. However, the weak point of all these systems is their need for comprehensive and reliable data. For traffic data acquisition, two sources are currently available: 1) infrastructure sensors and 2) floating vehicles. The former consists of a set of fixed point detectors installed in the roads, and the latter consists of the use of mobile probe vehicles as mobile sensors. However, both systems still have some deficiencies. The infrastructure sensors retrieve information fromstatic points of the road, which are spaced, in some cases, kilometers apart. This means that the picture of the actual traffic situation is not a real one. This deficiency is corrected by floating cars, which retrieve dynamic information on the traffic situation. Unfortunately, the number of floating data vehicles currently available is too small and insufficient to give a complete picture of the road traffic. In this paper, we present a floating car data (FCD) augmentation system that combines information fromfloating data vehicles and infrastructure sensors, and that, by using neural networks, is capable of incrementing the amount of FCD with virtual information. This system has been implemented and tested on actual roads, and the results show little difference between the data supplied by the floating vehicles and the virtual vehicles.