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A shape-based heuristic for the detection of urban block artifacts in street networks

Martin Fleischmann, Anastassia Vybornova

TL;DR

This paper tackles the problem of preprocessing street networks for morphological analyses by identifying 'face artifacts'—polygons formed by transportation-focused mappings that do not correspond to real urban blocks. It introduces a cheap heuristic that combines polygon area with five compactness metrics into a face artifact index, then uses KDE to locate a per-city threshold that splits artifacts from true blocks. Across 131 OpenStreetMap-derived FUAs, the method detects artifacts in 89% of cases, with validation against building footprints showing low false positives for practical thresholds; the approach also reveals regional differences in urban form and data representation. This work provides a concrete, scalable step toward automated street network simplification and offers a foundation for future automated artifact removal and cross-regional urban morphology studies.

Abstract

Street networks are ubiquitous components of cities, guiding their development and enabling movement from place to place; street networks are also the critical components of many urban analytical methods. However, their graph representation is often designed primarily for transportation purposes. This representation is less suitable for other use cases where transportation networks need to be simplified as a mandatory pre-processing step, e.g., in the case of morphological analysis, visual navigation, or drone flight routing. While the urgent demand for automated pre-processing methods comes from various fields, it is still an unsolved challenge. In this article, we tackle this challenge by proposing a cheap computational heuristic for the identification of "face artifacts", i.e., geometries that are enclosed by transportation edges but do not represent urban blocks. The heuristic is based on combining the frequency distributions of shape compactness metrics and area measurements of street network face polygons. We test our method on 131 globally sampled large cities and show that it successfully identifies face artifacts in 89\% of analyzed cities. Our heuristic of detecting artifacts caused by data being collected for another purpose is the first step towards an automated street network simplification workflow. Moreover, the proposed face artifact index uncovers differences in structural rules guiding the development of cities in different world regions.

A shape-based heuristic for the detection of urban block artifacts in street networks

TL;DR

This paper tackles the problem of preprocessing street networks for morphological analyses by identifying 'face artifacts'—polygons formed by transportation-focused mappings that do not correspond to real urban blocks. It introduces a cheap heuristic that combines polygon area with five compactness metrics into a face artifact index, then uses KDE to locate a per-city threshold that splits artifacts from true blocks. Across 131 OpenStreetMap-derived FUAs, the method detects artifacts in 89% of cases, with validation against building footprints showing low false positives for practical thresholds; the approach also reveals regional differences in urban form and data representation. This work provides a concrete, scalable step toward automated street network simplification and offers a foundation for future automated artifact removal and cross-regional urban morphology studies.

Abstract

Street networks are ubiquitous components of cities, guiding their development and enabling movement from place to place; street networks are also the critical components of many urban analytical methods. However, their graph representation is often designed primarily for transportation purposes. This representation is less suitable for other use cases where transportation networks need to be simplified as a mandatory pre-processing step, e.g., in the case of morphological analysis, visual navigation, or drone flight routing. While the urgent demand for automated pre-processing methods comes from various fields, it is still an unsolved challenge. In this article, we tackle this challenge by proposing a cheap computational heuristic for the identification of "face artifacts", i.e., geometries that are enclosed by transportation edges but do not represent urban blocks. The heuristic is based on combining the frequency distributions of shape compactness metrics and area measurements of street network face polygons. We test our method on 131 globally sampled large cities and show that it successfully identifies face artifacts in 89\% of analyzed cities. Our heuristic of detecting artifacts caused by data being collected for another purpose is the first step towards an automated street network simplification workflow. Moreover, the proposed face artifact index uncovers differences in structural rules guiding the development of cities in different world regions.
Paper Structure (14 sections, 2 equations, 10 figures, 3 tables)

This paper contains 14 sections, 2 equations, 10 figures, 3 tables.

Table of Contents

  1. Introduction
  2. Data and Method
  3. Data: 131 cities' street networks from OpenStreetMap
  4. Street network retrieval and generation of face polygons
  5. Compactness metrics
  6. Proposed heuristic: Face artifact index
  7. Validation: Visual assessment and overlay with OSM building data
  8. Evaluation of compactness metrics
  9. Results
  10. Compactness metrics
  11. Validation
  12. Evaluation
  13. Discussion
  14. List of analyzed FUAs

Figures (10)

  • Figure 1: a) Bridge, Amsterdam; b) Roundabout, Abidjan; c) Intersection, Kabul; d) Motorway, Vienna. Polygons classified as face artifacts are shown in red, and the OSM street network (without service roads) is shown in black. Face artifacts are polygons enclosed by street network geometries (in the case of OSM, lane centerlines) that do not represent morphological urban blocks, but instead are a result of detailed transportation-focused mapping of the streetscape. Map data © OpenStreetMap contributors © CARTO
  • Figure 2: Spatial distribution of FUAs selected to be used within this study color-coded according to the (sub-)continent they lie on.
  • Figure 3: Pairwise scatter plots and histograms of five shape metrics for a combined sample of all 131 FUAs. The diagonal plots show the distribution of each metric, while the off-diagonal plots show the correlation between the two metrics. Each scatter plot contains a value of Pearson correlation coefficient ($\rho$) and of Spearman's rank correlation coefficient ($r_{s}$) for a specific pair.
  • Figure 4: Face artifact index distributions for different compactness metrics and cities. In the columns, from left to right: circular compactness; isoperimetric quotient; isoareal quotient; radii ratio; diameter ratio. In the rows, from top to bottom: a) Cochabamba (Bolivia); b) Douala (Cameroon); c) Sydney (Australia); d) Tbilisi (Georgia); e) Montreal (Canada). Peaks are highlighted with black dots; valleys with red dots. The dashed red vertical line shows the position of the identified face artifact index threshold $T_{i}$ for the given compactness metric and city.
  • Figure 5: Birdview plots of detected face artifacts (red polygons) within each FUA border (black lines) for one city per each continent, clockwise from top left: Wuhan (China, Asia), Khartoum (Sudan, Africa), São Paulo (Brazil, South America), Moscow (Russia, Europe), Perth (Australia, Oceania), and Montreal (Canada, North America). doi.org/10.5281/zenodo.8300730.
  • ...and 5 more figures