CartAGen: an Open Source Research Platform for Map Generalization

Touya, Guillaume; Lokhat, Imran; Duchêne, Cécile

doi:10.5194/ica-proc-2-134-2019

Cited by 13 publications

(8 citation statements)

References 41 publications

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“…Our code and image data will be made available in future weeks as part of the DeepMapGen open platform https://github.com/umrlastig/DeepMapGen. The code to generate the images from the vector dataset was part of the CartAGen open source generalisation platform https://github.com/IGNF/CartAGen, developed at IGN [40].…”

Section: Methodsmentioning

confidence: 99%

Exploring the Potential of Deep Learning Segmentation for Mountain Roads Generalisation

Courtial

Ayedi

Touya

et al. 2020

IJGI

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Among cartographic generalisation problems, the generalisation of sinuous bends in mountain roads has always been a popular one due to its difficulty. Recent research showed the potential of deep learning techniques to overcome some remaining research problems regarding the automation of cartographic generalisation. This paper explores this potential on the popular mountain road generalisation problem, which requires smoothing the road, enlarging the bend summits, and schematising the bend series by removing some of the bends. We modelled the mountain road generalisation as a deep learning problem by generating an image from input vector road data, and tried to generate it as an output of the model a new image of the generalised roads. Similarly to previous studies on building generalisation, we used a U-Net architecture to generate the generalised image from the ungeneralised image. The deep learning model was trained and evaluated on a dataset composed of roads in the Alps extracted from IGN (the French national mapping agency) maps at 1:250,000 (output) and 1:25,000 (input) scale. The results are encouraging as the output image looks like a generalised version of the roads and the accuracy of pixel segmentation is around 65%. The model learns how to smooth the output roads, and that it needs to displace and enlarge symbols but does not always correctly achieve these operations. This article shows the ability of deep learning to understand and manage the geographic information for generalisation, but also highlights challenges to come.

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Section: Methodsmentioning

confidence: 99%

Exploring the Potential of Deep Learning Segmentation for Mountain Roads Generalisation

Courtial

Ayedi

Touya

et al. 2020

IJGI

Self Cite

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“…A code to compute these constraint monitors, i.e. the entities that measure the current value and the satisfaction of a constraint on one map object (Touya, 2012), is available in the open CartAGen platform (Touya et al, 2019). However, to make the evaluation easier, we plan to release a QGIS plugin that computes the constraint satisfactions automatically, given the shapefiles of the generalised data.…”

Section: Ign-1-4mentioning

confidence: 99%

L’Alpe d’Huez: A Benchmark for Topographic Map Generalisation

Touya

2022

Abstr. Int. Cartogr. Assoc.

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“…As defined in the GIS literature [33], polygon simplification deals with the graphic symbology, leading to a simplification process of a polygonal feature that results in a multiscale spatial representation [34]. According to documentations of ArcGIS software [35] (ESRI 2020) and open-sourced platforms such as CartAGen [36,37], the geometric unit of a polygonal feature to be simplified is categorized by its points, bends, and other areal units (such as triangles and convex hulls), respectively.…”

Section: Related Algorithms For Polygon Simplificationmentioning

confidence: 99%

PolySimp: A Tool for Polygon Simplification Based on the Underlying Scaling Hierarchy

Zhao

Zheng

et al. 2020

IJGI

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Map generalization is a process of reducing the contents of a map or data to properly show a geographic feature(s) at a smaller extent. Over the past few years, the fractal way of thinking has emerged as a new paradigm for map generalization. A geographic feature can be deemed as a fractal given the perspective of scaling, as its rough, irregular, and unsmooth shape inherently holds a striking scaling hierarchy of far more small elements than large ones. The pattern of far more small things than large ones is a de facto heavy tailed distribution. In this paper, we apply the scaling hierarchy for map generalization to polygonal features. To do this, we firstly revisit the scaling hierarchy of a classic fractal: the Koch Snowflake. We then review previous work that used the Douglas–Peuker algorithm, which identifies characteristic points on a line to derive three types of measures that are long-tailed distributed: the baseline length (d), the perpendicular distance to the baseline (x), and the area formed by x and d (area). More importantly, we extend the usage of the three measures to other most popular cartographical generalization methods; i.e., the bend simplify method, Visvalingam–Whyatt method, and hierarchical decomposition method, each of which decomposes any polygon into a set of bends, triangles, or convex hulls as basic geometric units for simplification. The different levels of details of the polygon can then be derived by recursively selecting the head part of geometric units and omitting the tail part using head/tail breaks, which is a new classification scheme for data with a heavy-tailed distribution. Since there are currently few tools with which to readily conduct the polygon simplification from such a fractal perspective, we have developed PolySimp, a tool that integrates the mentioned four algorithms for polygon simplification based on its underlying scaling hierarchy. The British coastline was selected to demonstrate the tool’s usefulness. The developed tool can be expected to showcase the applicability of fractal way of thinking and contribute to the development of map generalization.

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CartAGen: an Open Source Research Platform for Map Generalization

Cited by 13 publications

References 41 publications

Exploring the Potential of Deep Learning Segmentation for Mountain Roads Generalisation

Exploring the Potential of Deep Learning Segmentation for Mountain Roads Generalisation

L’Alpe d’Huez: A Benchmark for Topographic Map Generalisation

PolySimp: A Tool for Polygon Simplification Based on the Underlying Scaling Hierarchy

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