# Level of detail matters: machine learning based river selection across Polish and French data

Izabela Karsznia, Iga Ajdacka, Bérénice Le Mao, Guillaume Touya, Albert Adolf

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Izabela Karsznia, Iga Ajdacka, Bérénice Le Mao, Guillaume Touya, Albert Adolf. Level of detail matters: machine learning based river selection across Polish and French data. ICA Workshop on Multiscale Cartography and Sustainability, ICA, Aug 2025, Vancouver (BC), Canada. hal-05352124

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# Level of detail matters: machine learning based river selection across Polish and French data

Izabela Karsznia a*, Iga Ajdacka a, Bérénice Le Mao b, Guillaume Touya b, Albert Adolf a

**Keywords:** cartographic generalization, river network, machine learning, level of detail (LoD), cross-country analysis

**Abstract:**

Cartographic generalisation is one of the most important and indispensable elements of the map design process. It is defined as the deliberate and logical reduction of map detail, taking into account the purpose of the map, the desired level of detail (LoD), the perceptual capabilities and limitations of the user, as well as the usage environment (Kraak et al., 2020). One of the first steps in this process is selection, whose goal is to remove detail from higher-resolution datasets until their LoD matches that of lower-resolution datasets. The task of selection is to reduce the map or database content appropriately to the target scale or intended LoD (Stanislawski et al., 2014, Touya et al., 2023). In addition to the most important anthropogenic map features, such as settlements and roads, rivers represent key natural elements. Therefore, it is crucial to develop effective river selection algorithms, especially those that account for multiple LoDs and diverse datasets, in order to create scalable and comprehensive methods and tools applicable across different countries (Le Mao et al., 2024).

Rivers also play a crucial role in achieving multiple Sustainable Development Goals (SDGs), including clean water and sanitation (SDG 6), sustainable cities and communities (SDG 11), climate action (SDG 13), and life on land (SDG 15) (https://sdgs.un.org/goals). Therefore, accurately identifying and representing river networks on maps is essential for tracking environmental progress. This study highlights the need for robust, general algorithms that can consistently process geographic data across different levels of detail (LoDs) as well as different countries. By ensuring that methods for river selection and representation are not biased toward specific national datasets or cartographic traditions, we support a scalable approach to global SDG monitoring.

In this context, we propose using machine learning (ML) techniques for river selection in both large- and small-scale maps, aiming to build multiscale models and standardize the process across diverse geographic regions and cross-country datasets. The French data comprise two drainage basins: the Adour River in the southwest and the smaller Blavet River in the northwest. The dataset contains multiple levels of detail generated from the French topographic database BDTopo, using automated selection (Touya, 2007) combined with manual expert corrections (Figure 1). The Polish data include three drainage basins: the Biebrza, Wieprz, and Bzura. This dataset contains two levels of detail (Le Mao et al., 2024). Both datasets share the same set of river attributes, including thematic and topological properties, as well as centrality measures, including but not limited to betweenness centrality, calculated for the strokes (Thomson & Brooks, 2000; Touya, 2010). The geometric attributes were calculated with the use of various Python libraries, including NetworkX<fnref n="1" /> as well as standard GIS tools available in QGIS software. Fluvial and drainage patterns serving as indicators of underlying physiographic conditions, including geological structure and geomorphological processes such as historical erosion, are also often included in the attribute list. These patterns are important for the interpretation of landform development and constitute an integral component of topographic analysis and cartographic representation (Ajdacka & Karsznia, 2024). Various ML models were tested: decision trees (DTs), random forests (RF), and decision trees supported by genetic algorithms (DT-GA). The models were trained on Polish data with the use of Polish atlas reference maps at the scale of 1:500 000 and 1:1 000 000 scales, and subsequently implemented and validated on the French datasets, where the benchmark for the proper selection was an expert decision that covered several large scales. Only rivers with line geometry were generalized, however, in the attribute list the distance to nearest polygon water, including lakes, were taken into account. We tested multiple levels of detail: large-scale maps for the French data and small-scale maps for the Polish data.

<footnote>a Department of Geoinformatics, Cartography and Remote Sensing, Faculty of Geography and Regional Studies, University of Warsaw, Poland - i.karsznia@uw.edu.pl, i.ajdacka@uw.edu.pl, a.adolf@uw.edu.pl</footnote>

<footnote>b Univ Gustave Eiffel, ENSG, IGN, LASTIG, F-77420 Champs-sur-Marne, France, berenice.le-mao@ign.fr, guillaume.touya@ign.fr</footnote>

<footnote>* Corresponding author</footnote>

<footnote n="1">¹ https://networkx.org/</footnote>
<figure><img src="image_3.png" /><figcaption>Figure 1. Examples of the predictions from a Random Forests model trained with Polish and applied on French data at several zoom levels for Adour basin. The initial network is in red and the selected rivers are in blue.</figcaption></figure>

As a result, we observe a noticeable gap in the level of detail (LoD): the model tends to generalize French data far more than expected. We hypothesize that this effect is caused by differences in LoD between the initial and target datasets in each country. Currently, the model struggles to adapt to these disparities and applies a Polish-style generalization pattern to French data. This insight emphasizes the complexity of working with cross-country datasets, where full alignment in terms of LoD is rarely achievable. Understanding and addressing this issue is key for building truly scalable, globally consistent cartographic AI systems. A method to deal with this problem would be to measure and characterise the level of detail, or level of abstraction of the networks, used for training and prediction of these machine learning models, to adjust the model with fine-tuning when level of detail differs from the training data. Another solution would be to use more diverse training datasets, from more countries or from regions with different types of hydrographic networks and different hydrological and relief characteristics.

## Acknowledgements

This research was funded by the National Science Centre, Poland, grant number UMO-2020/37/B/HS4/02605, "Improving Settlement and Road Network Design for Maps of Small Scales Using Artificial Intelligence and Graph Theory." This research has also received funding from the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme (grant agreement N° 101003012).

## References

* Ajdacka I., Karsznia I. (2024). The role of the physical-geographical variables in the automatic selection of rivers. American Association of Geographers Annual Meeting, 16-20.04.2024, Honolulu, USA.