Tratamiento de multitrazas GNSS 3D para la obtención de ejes medios

El uso de los sistemas de navegación se ha difundido mucho y la explotación de sus datos es compleja debido tanto al volumen como a su variabilidad. Este trabajo de revisión presenta una definición de los elementos (trazas) y un análisis de las fuentes de variabilidad, así como los criterios usuales de filtrado de los datos originales. Otro aspecto presentado son los atípicos, éstos se definen y clasifican para el caso de trazas GNSS. La obtención del eje medio se aborda en dos pasos, primero se presentan algoritmos que, a partir de dos polilíneas 3D permiten obtener una solución media y, posteriormente, se presentan otras opciones que permiten obtener una solución de eje medio a partir de un conjunto de trazas.Este trabajo se ha realizado dentro del proyecto de investigación “Evaluación 3D de elementos lineales de información geográfica” del Ministerio de Ciencia e Innovación (BIA2011-23271) cofinanciado por el Fondo Europeo de Desarrollo Regional

Incremental data acquisition from gps-traces

GPS traces can track actual time and coordinates of regular vehicles going their own business, and it is easy to scale to the entire area with an accuracy of 6 to 10 meters in normal condition. As a result, extracting road map from GPS traces could be an alternative way to traditional way of road map generation. The basic idea of this paper is to describe a process which incrementally improves existing road data with incoming new information in terms of GPS traces. In this way we consider the GPS traces as measurements which represent a "digitization" of the true road. Although the accuracy of the traces is not too high, due to the high number of measurements an improvement of the quality of the road information can be achieved. Thus, this paper presents a method for integrating GPS traces and an existing road map towards a more accurate, up-to-data and detailed road map. First we profile the existing road by a sequence of perpendicular profiles and get the road's candidate sampling traces which intersect with the profile. Then we match the potential traces with the road and finally estimate the new road centerline from its corresponding traces. In addition to the geometry of roads we also mine attribute information from GPS traces, such as number of lanes. Furthermore, we explore the benefit of an incremental acquisition by a temporal analysis of the data

RoadTagger: Robust Road Attribute Inference with Graph Neural Networks

Inferring road attributes such as lane count and road type from satellite imagery is challenging. Often, due to the occlusion in satellite imagery and the spatial correlation of road attributes, a road attribute at one position on a road may only be apparent when considering far-away segments of the road. Thus, to robustly infer road attributes, the model must integrate scattered information and capture the spatial correlation of features along roads. Existing solutions that rely on image classifiers fail to capture this correlation, resulting in poor accuracy. We find this failure is caused by a fundamental limitation -- the limited effective receptive field of image classifiers. To overcome this limitation, we propose RoadTagger, an end-to-end architecture which combines both Convolutional Neural Networks (CNNs) and Graph Neural Networks (GNNs) to infer road attributes. The usage of graph neural networks allows information propagation on the road network graph and eliminates the receptive field limitation of image classifiers. We evaluate RoadTagger on both a large real-world dataset covering 688 km^2 area in 20 U.S. cities and a synthesized micro-dataset. In the evaluation, RoadTagger improves inference accuracy over the CNN image classifier based approaches. RoadTagger also demonstrates strong robustness against different disruptions in the satellite imagery and the ability to learn complicated inductive rules for aggregating scattered information along the road network

Inferring directed road networks from GPS traces by track alignment

This paper proposes a method to infer road networks from GPS traces. These networks include intersections between roads, the connectivity between the intersections and the possible traffic directions between directly-connected intersections. These intersections are localized by detecting and clustering turning points, which are locations where the moving direction changes on GPS traces. We infer the structure of road networks by segmenting all of the GPS traces to identify these intersections. We can then form both a connectivity matrix of the intersections and a small representative GPS track for each road segment. The road segment between each pair of directly-connected intersections is represented using a series of geographical locations, which are averaged from all of the tracks on this road segment by aligning them using the dynamic time warping (DTW) algorithm. Our contribution is two-fold. First, we detect potential intersections by clustering the turning points on the GPS traces. Second, we infer the geometry of the road segments between intersections by aligning GPS tracks point by point using a stretch and then compress strategy based on the DTW algorithm. This approach not only allows road estimation by averaging the aligned tracks, but also a deeper statistical analysis based on the individual track's time alignment, for example the variance of speed along a road segment