Relevant Space Partitioning for Collaborative Generalisation

workshopInternational audienc

What Is the Level of Detail of OpenStreetMap?

workshopInternational audienc

Social Welfare to Assess the Global Legibility of a Generalized Map

International audienceCartographic generalization seeks to summarize geographical information from a geo-database to produce a less detailed and readable map. The specifications of a legible map are translated into a set of constraints to guide the generalization process and evaluate it. The global evaluation of the map, or of a part of it, consisting in aggregating all the single constraints satisfactions, is still to tackle for the generalization community. This paper deals with the use of the social welfare theory to handle the aggregation of the single satisfactions on the map level. The social welfare theory deals with the evaluation of the economical global welfare of a society, based on the individual welfare. Different social welfare orderings are adapted to generalization, compared and some are chosen for several generalization use cases. Experiments with topographic maps are carried out to validate the choices

Finding the Oasis in the Desert Fog? Understanding Multi- Scale Map Reading

workshopInternational audienc

A Road Network Selection Process Based on Data Enrichment and Structure Detection

International audienceIn the context of geographical database generalisation, this paper deals with a generic process for road network selection. It is based on the geographical context that is made explicit and on the characteristic structure preservation. It relies on literature work that is adapted and gathered. The first step is to detect significant structures and patterns of the road network such as roundabouts or highway interchanges. It allows to enrich the initial dataset with explicit geographic structures that were implicit in initial data. It helps both to explicit the geographical context and to preserve characteristic structures. Then, this enrichment is used as knowledge input for the following step that is the selection of roads in rural areas using graph theory techniques. After that, urban roads are selected by means of a block aggregation complex algorithm. Continuity between urban and rural areas is guaranteed by modelling continuity using strokes. Finally, the previously detected characteristic structures are typified to maintain their properties in the selected network. This automated process has been fully implemented on Clarity™ and tested on large datasets

Lessons Learned From Research on Multimedia Summarization

workshopInternational audienc

Deep Learning for Enrichment of Vector Spatial Databases: Application to Highway Interchange

Spatial analysis and pattern recognition with vector spatial data is particularly useful to enrich raw data. In road networks for instance, there are many patterns and structures that are implicit with only road line features, among which highway interchange appeared very complex to recognise with vector-based techniques. The goal is to find the roads that belong to an interchange, i.e. the slip roads and the highway roads connected to the slip roads. In order to go further than state-of-the-art vector-based techniques, this paper proposes to use raster-based deep learning techniques to recognise highway interchanges. The contribution of this work is to study how to optimally convert vector data into small images suitable for state-of-the-art deep learning models. Image classification with a convolutional neural network (i.e. is there an interchange in this image or not?) and image segmentation with a u-net (i.e. find the pixels that cover the interchange) are experimented and give results way better than existing vector-based techniques in this specific use case

Continuously Generalizing Buildings to Built-up Areas by Aggregating and Growing

International audienceTo enable smooth zooming, we propose a method to continuously generalize buildings from a given start map to a smaller-scale goal map, where there are only built-up area polygons instead of individual building polygons. We name the buildings on the start map original buildings. For an intermediate scale, we aggregate the original buildings that will become too close by adding bridges. We grow (bridged) original buildings based on buffering, and simplify the grown buildings. We take into account the shapes of the buildings both at the previous map and goal map to make sure that the buildings are always growing. The running time of our method is in O(n 3), where n is the number of edges of all the original buildings. The advantages of our method are as follows. First, the buildings grow continuously and, at the same time, are simplified. Second, right angles of buildings are preserved during growing: the merged buildings still look like buildings. Third, the distances between buildings are always larger than a specified threshold. We do a case study to show the performances of our method

Enhancing building footprints with squaring operations

Whatever the data source, or the capture process, the creation of a building footprint in a geographical dataset is error prone. Building footprints are designed with square angles, but once in a geographical dataset, the angles may not be exactly square. The almost-square angles blur the legibility of the footprints when displayed on maps, but might also be propagated in further applications based on the footprints, e.g., 3D city model construction. This paper proposes two new methods to square such buildings: a simple one, and a more complex one based on nonlinear least squares. The latter squares right and flat angles by iteratively moving vertices, while preserving the initial shape and position of the buildings. The methods are tested on real datasets and assessed against existing methods, proving the usefulness of the contribution. Direct applications of the squaring transformation, such as OpenStreetMap enhancement, or map generalization are presented

Automatic Structure Detection and Generalization of Railway Networks

International audienceUnlike road or river networks, railway networks automatic generalization are missing to properly handle the detailed networks provided in current geo-datasets like OpenStreetMap. This paper proposes automatic methods to automatically identify key structures of railway networks, such as parallel main tracks, or fan and pack patterns inside large train stations. Then, algorithms based on the detected structures are proposed to generalize the railway networks. The algorithms are tested on real datasets, including OpenStreetMap data