# Back to the roots - Using map generalization for green cartography

Guillaume Touya

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Guillaume Touya. Back to the roots - Using map generalization for green cartography. Workshop on Multiscale Cartography and Sustainability, ICA, Aug 2025, Vancouver, BC, Canada, Canada. hal-05352099

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Submitted on 6 Nov 2025

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# Back to the roots - Using map generalization for green cartography

Guillaume Touya<sup>a*</sup>

**Keywords:** map generalization, green cartography, compression, tiling

**Abstract:**

With the current massive use of web maps through mapping and navigation applications (Wu et al., 2024), the energy consumed by these applications becomes significant, and we should try to reduce it to reduce the impact on climate change. This is the goal of green cartography (Wu et al., 2024), a new topic of research for cartographers, to design energy-efficient web maps. We can list four types of sources of energy consumption for web maps (Touya et al., 2025): storing data and map tiles in data centers, computing map tiles from geographic data, transferring vector or raster tiles through the web, and displaying the map on digital screens. Cartographic generalisation is a map design technique used to abstract and simplify geographic data in order to make medium or small-scale maps more legible with less detail. It does not seem related to green cartography, but if we go back to the roots of automated cartographic generalisation, the first algorithms, e.g. the Douglas & Peucker line simplification algorithm (Douglas and Peucker, 1973), were meant to compress the digitized lines to reduce the size of the digital files used to store those lines, in a time when hard drives and RAM were extremely limited. This abstract discusses how map generalization can be used to better compress the web maps to reduce their energy consumption in the storage and web transfer phases.

Current web multi-scale maps are tiled to allow a smooth visualisation, and these tiles are either raster (following the Open Geospatial Consortium Web Map Tiling Service (WMTS)) or vector (for more recent maps). The impact of generalization as a data compression process is not equivalent for raster and vector tiles (Figure 1), but in this work, we focus on vector tiles as they seem to be the most promising technology for maps in the future.

<figure><img src="image_3.png" /><figcaption>Figure 1. Three simple examples of map tiles before and after generalization with their respective vector and image size. The number of vertices is the same in case (c).</figcaption></figure>

<footnote><sup>a</sup> LASTIG, Univ Gustave Eiffel, IGN, ENSG, Champs-sur-Marne, F-77420, firstname.lastname@ign.fr</footnote>

<footnote>* Corresponding author</footnote>
Even if data compression was one of the first goals of digital and automated generalization, the current methods used to generalize topographic maps focus more on map legibility than data compression. As an illustration, we counted the size of the vector files necessary to build a topographic map at the 1:50k scale before and after generalization. The initial vector files weigh 6,818,153 bytes, while the generalized files (using automated generalization (Touya and Duchêne, 2011)) weigh 6,376,289 bytes, which corresponds to a 6.5% reduction of the size of the files. However, if we study the different generalization operators, two of them seem to have the potential to reduce the size of vector tiles significantly: selection and simplification.

Let us use an example to illustrate the potential importance of generalization, and in particular selection, to compress vector map tiles in a multi-scale map. Figure 2 shows zoom levels 16 to 13 of OpenStreetMap default map. The default building footprints are rendered without generalization at zoom levels 16 and 15. At zoom level 14, the only change is that the outline of the footprint is not rendered anymore, but this change has no impact on the size of vector tiles. At zoom level 13, the buildings are simply completely removed. If S is the total storage size of the vector tiles including the buildings at zoom level 16 for a given fixed region, the total size for the scale range [16, 13] covering this region is 3S. If we use a building generalization process combining simplification, aggregation and displacement, the total size should be $αS + βS + γS < 3S$ with $α, β, γ ∈ [0, 1]$.

<figure><img src="image_4.png" /><figcaption>Figure 2. Default OpenStreetMap scales from zoom level 16 to 13. The buildings remain unchanged until level 13 where they are completely removed.</figcaption></figure>

Although map generalization research has lately been less focused on its original data compression goals, we need to reassess existing algorithms for the purposes of green cartography and data compression. For instance, there are many line simplification algorithms, and we could assess them in relation to green cartography. This is what we plan to do in the future.

Finally, it should be noted that using more generalization to reduce the size of the map tiles involves more computational resources during the generation of those tiles. However, the map tiles are generated once per update, and their use through web transfer is repeated thousands of times. As a consequence, we consider that increasing the computational resources for tile generation is worth the global energy consumption of the map.

## Acknowledgements

This project received funding from the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme (grant agreement No. 101003012, LostInZoom).

## References

Douglas, D. H. and Peucker, T. K., 1973. Algorithms for the Reduction of the Number of Points Required to Represent a Digitized Line or its Caricature. *Cartographica: The International Journal for Geographic Information and Geovisualization* 10(2), pp. 112–122.

Touya, G. and Duchêne, C., 2011. CollaGen: Collaboration between automatic cartographic Generalisation Processes. In: A. Ruas (ed.), *Advances in Cartography and GIScience*, Lecture Notes in Geoinformation and Cartography, Vol. 1, Springer Berlin Heidelberg, Berlin, Heidelberg, pp. 541–558.

Touya, G., Courtial, A., Kalsron, J., Berli, J., Le Mao, B. and Wenclik, L., 2025. Understanding the Carbon Footprint of Tile Transfer for Web Maps. *ISPRS International Journal of Geo-Information* 14(3), pp. 107.

Wu, M., Lv, G., Qiao, L., Roth, R. E. and Zhu, A.-X., 2024. Green Cartography: A research agenda towards sustainable development. *Annals of GIS* 30(1), pp. 15–34.