Automatic mapping and innovative on-demand mapping services at IGN France.

Author:Lafay, S.
 
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  1. Introduction

    Modern cartography has been evolving quickly thanks to new tools and devices. Mapmaking has become widespread through the Internet. The growing possibility of creating maps on the Internet led to the development of a wide range of products with different cartographic quality (Cartwright 2007; Harrie and Stigmar 2009). Intuitive on-demand mapping tools and online services are needed to improve and standardize their quality (Christophe 2011; Harrie, Mustiere, and Stigmar 2011).

    National mapping agencies are now faced with the need of publishing maps through geoportals, ideally up-to-date, at low cost, and with high cartographic quality (Foerster, Stoter, and Kraak 2010; Duchene et al. 2014). This is in line with the INSPIRE Directive in Europe (INSPIRE 2007) and the global tendency of disseminating public data.

    In this article, we present work carried out at IGN France, through the 'on-demand mapping' project, to provide data and services for high-quality, customizable maps. The project addresses three topics:

    (1) Automatic production of high-quality and up-to-date cartographic vector data from reference geodata;

    (2) Generation of raster maps from the vector data for distribution through Web services or downloadable tiles;

    (3) Offering high-level and innovative customization applications on these products, available through Web services.

    After presenting a global overview of the process in the next section, we explain how the digital cartographic model (DCM) was produced. Then, we focus on the map legend and how it is customizable. Finally, before concluding, we present a prototype for offering a customization service.

  2. Overview

    A DCM is a database ready for displaying and plotting data at specified map scales (Griinreich 1985; Brassel and Weibel 1988). A DCM is usually derived from a digital landscape model (DLM), which is a database accurately describing topographic features without considering map scale or graphic constraints. DCM is produced to be readable at specified map scales. Figure 1 illustrates the process for building a multiscale DCM from existing data at IGN France. Multiscale means here that DCMs at different scales are stored in a common system and that data are harmonized as much as possible, in terms of schemas and graphic choices. It should be noted, however, that we did not consider explicitly linking objects at different scales, even if this could be useful for managing updates or detect inconsistencies, for example.

    Various data sources were derived to build the multiscale DCM, as detailed in the following section. Data were stored in a PostgreSQL server with a PostGIS spatial extension. Note that a (nearly) complete automation is required to build the multiscale DCM. Automation reduces the cost of producing maps. Automation is also necessary to reduce time to produce maps and then ensure up-to-date maps (at least maps as up-to-date as the data used to produce them). In order to obtain such automation, it is necessary to take advantage of existing data and processes.

    We follow a pragmatic approach in our work. First, we used different original data sources to build the multiscale DCM, either existing DLMs or DCMs. Then, we applied all together 'star' and 'ladder' approaches as defined by Eurogeographics (2005), that is, respectively deriving directly different levels of the DCM from the same original data-set by means of independent processes, and recursively and progressively deriving levels of the DCM from scale to scale. This mixed strategy is the one currently under development or in use in most national mapping agencies, as assessed by surveys of Stoter (2005) and Duchene et al. (2014).

    Once the multiscale vector DCM has been built, it can be symbolized, rasterized, and tiled for distribution through Web services or as downloadable files (for more detailed description, see below). At this step, a good organization of legend parameters facilitates the customization of color choices while keeping continuity of colors through scales. Some default legends have thus been defined. More, this paves the way to on-demand customization of legends, in terms of the data to be represented and color choices. A prototype is presented later in the paper to help users to define their own original legends.

  3. Producing the multiscale vector DCM

    3.1. Small scales, from 1:100,000 to 1:1,000,000

    Cartographic data from IGN's mapping department are used as part of our DCM. The data are published at scales of 1:100,000, 1:250,000, and 1:1,000,000 and are updated every year or every 2 years to produce both paper and digital maps (Figure 2).

    The maps have been originally produced and are updated using production flow lines (Jahard, Lemarie, and Lecordix 2003) partly based on automatic generalization methods developed by the COGIT Lab and partners (Barrault et al. 2001; Duchene 2010). Those maps have initially been designed as paper maps. The purpose of the 'on-demand mapping' project presented here is to use those existing maps in a consistent multiscale DCM.

    The data we use were thus only converted from proprietary format like 1 Spatial (http://www.lspatial.com/) Lamps2 or GeoConcept (http://www.geoconcept.com) to a central PostgreSQL database using conversion tools already widely used on other production flow lines.

    3.2. Scale 1:25,000

    Currently, IGN does not have a full vector cartographic database at 1:25,000 scale. Maps of part of the country are still stored as raster files. A countrywide vector database at the scale of 1:25,000 should be available at the end of 2017.

    Furthermore, there is urgent need for a quality map at this scale, covering all of France, showing fewer features than the existing topographic maps but with more frequent updates (an uncluttered map allows for overlaying other data with a more readable result).

    We implemented a process to build this needed cartographic database at the 1:25,000 scale and make available a countrywide vector map.

    The data source for the map at this scale is the production version of the IGN BDTOPO[R] database. An updated version of this database is released every 6 months. The production of 1:25,000-scale cartographic data from BDTOPO[R] was automated to obtain a ready-to-display map with good cartographic quality. We used existing flow lines from a previous R&D project implemented to automate the production of 1:25,000 topographic maps at IGN (Braun et al. 2007; Maugeais et al. 2011).

    The 1:25,000 cartographic database was produced in five phases:

    (1) Preprocessing

    In the source database, vegetation is too detailed and buildings are split between cadastral units. The data were cleaned during preprocessing which involved a simple but CPU-intensive task performed by a dedicated java process, using the open-source Java library JTS (Davis 2013). The process loads data the after tile, processes it in-memory, and uploads the results in new PostgreSQL tables. This process takes about 24 hours to handle the whole French territory.

    (2) Main process

    Several tasks were performed automatically, as listed below and illustrated in the Figures 3-5. These included: Symbolization:

    (1) Defining symbolization relevant to the 1:25,000 scale, by combining different...

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