Automated processing for map generalization using web services

In map generalization various operators are applied to the features of a map in order to maintain and improve the legibility of the map after the scale has been changed. These operators must be applied in the proper sequence and the quality of the results must be continuously evaluated. Cartographic constraints can be used to define the conditions that have to be met in order to make a map legible and compliant to the user needs. The combinatorial optimization approaches shown in this paper use cartographic constraints to control and restrict the selection and application of a variety of different independent generalization operators into an optimal sequence. Different optimization techniques including hill climbing, simulated annealing and genetic deep search are presented and evaluated experimentally by the example of the generalization of buildings in blocks. All algorithms used in this paper have been implemented in a web services framework. This allows the use of distributed and parallel processing in order to speed up the search for optimized generalization operator sequence

Climate-based site selection for a very large telescope using GIS techniques

Astronomical research at present requires that a telescope with an aperture diameter of between 50 and 100 metres be constructed within the next 10 years or so. This new generation of telescopes will be called OWL (Overwhelmingly Large), and it represents one order of magnitude increase in size over today's telescopes. Selection of an ideal site for this giant telescope is dependent on many climatological, meteorological and geomorphological parameters (Grenon 1990). Among these are cloud cover, atmospheric humidity, aerosol content, airflow direction and strength, air temperature, topography, and seismicity. Even relatively minor changes in weather patterns can have a significant effect on seeing conditions (Beniston et al. 2002)

Metropolitan Segment Traffic Speeds from Massive Floating Car Data in 10 Cities

Traffic analysis is crucial for urban operations and planning, while the availability of dense urban traffic data beyond loop detectors is still scarce. We present a large-scale floating vehicle dataset of per-street segment traffic information, Metropolitan Segment Traffic Speeds from Massive Floating Car Data in 10 Cities (MeTS-10), available for 10 global cities with a 15-minute resolution for collection periods ranging between 108 and 361 days in 2019-2021 and covering more than 1500 square kilometers per metropolitan area. MeTS-10 features traffic speed information at all street levels from main arterials to local streets for Antwerp, Bangkok, Barcelona, Berlin, Chicago, Istanbul, London, Madrid, Melbourne and Moscow. The dataset leverages the industrial-scale floating vehicle Traffic4cast data with speeds and vehicle counts provided in a privacy-preserving spatio-temporal aggregation. We detail the efficient matching approach mapping the data to the OpenStreetMap road graph. We evaluate the dataset by comparing it with publicly available stationary vehicle detector data (for Berlin, London, and Madrid) and the Uber traffic speed dataset (for Barcelona, Berlin, and London). The comparison highlights the differences across datasets in spatio-temporal coverage and variations in the reported traffic caused by the binning method. MeTS-10 enables novel, city-wide analysis of mobility and traffic patterns for ten major world cities, overcoming current limitations of spatially sparse vehicle detector data. The large spatial and temporal coverage offers an opportunity for joining the MeTS-10 with other datasets, such as traffic surveys in traffic planning studies or vehicle detector data in traffic control settings.Comment: Accepted by IEEE Transactions on Intelligent Transportation Systems (T-ITS), DOI: https://doi.org/10.1109/TITS.2023.329173

Traffic4cast at NeurIPS 2022 -- Predict Dynamics along Graph Edges from Sparse Node Data: Whole City Traffic and ETA from Stationary Vehicle Detectors

The global trends of urbanization and increased personal mobility force us to rethink the way we live and use urban space. The Traffic4cast competition series tackles this problem in a data-driven way, advancing the latest methods in machine learning for modeling complex spatial systems over time. In this edition, our dynamic road graph data combine information from road maps, $10^{12}$ probe data points, and stationary vehicle detectors in three cities over the span of two years. While stationary vehicle detectors are the most accurate way to capture traffic volume, they are only available in few locations. Traffic4cast 2022 explores models that have the ability to generalize loosely related temporal vertex data on just a few nodes to predict dynamic future traffic states on the edges of the entire road graph. In the core challenge, participants are invited to predict the likelihoods of three congestion classes derived from the speed levels in the GPS data for the entire road graph in three cities 15 min into the future. We only provide vehicle count data from spatially sparse stationary vehicle detectors in these three cities as model input for this task. The data are aggregated in 15 min time bins for one hour prior to the prediction time. For the extended challenge, participants are tasked to predict the average travel times on super-segments 15 min into the future - super-segments are longer sequences of road segments in the graph. The competition results provide an important advance in the prediction of complex city-wide traffic states just from publicly available sparse vehicle data and without the need for large amounts of real-time floating vehicle data.Comment: Pre-print under review, submitted to Proceedings of Machine Learning Researc

Modelling Cartographic Relations for Categorical Maps

Abstract: Map generalisation seeks to maintain important map objects, patterns, and relationships, while suppressing unimportant ones. Hence, the spatial and semantic characteristics of map objects as well as the relations existing between them have to be detected, preserved and exploited for generalisation. Two main groups of relations can be differentiated: Horizontal relations exist on the same level of detail (LOD), or scale, and represent common structural properties. Vertical relations appear between homologous objects and object groups in a collection of the same map type but across different map scales. Focusing on thematic categorical maps, the paper emphasises the importance of horizontal and vertical relations in automated generalisation. Hence, a typology of horizontal and vertical relations for categorical maps is presented and links to existing generalisation operators are identified. A selection of relations is discussed and illustrated in more detail. 1

Experiments in building an open generalisation system

There is a growing consensus within the map generalization research community that open research platforms will allow closer integration in terms of collaborative research, data abstractions, interoperability of functional components, and the augmentation of geo-spatial applications with generalization capabilities. This chapter describes the authors' experiences in developing systems to support collaborative research in map generalization. The increasing complexity of methods used in generalization research together with growing demands for generalization processing to support a myriad of new geospatial services and technologies has led researchers to investigate how open architectures might better support such changing needs. The chapter explains two such requirements: to share generalization techniques among researchers within the field and to present generalization functionality externally to geographic services. This chapter discusses the issue of openness first in relation to the requirements of the generalization community and then in the broader context of open standards, open architectures, and open sources for geographic information