A Novel GIS-based Polygon Shape Similarity Measure Applied to OSM Building Footprints
Bibliographic record
Abstract
Abstract. Assessing the similarity between polygonal shapes is a fundamental problem in geographic information science (GIS) with applications in spatial data quality assessment, feature matching, and cartographic generalization. This paper introduces a novel and computationally efficient shape similarity measure tailored for comparing building footprints in OpenStreetMap (OSM). Unlike traditional methods that rely on complex transformations such as Fourier descriptors or graph-based techniques, our approach is based on the average boundary distance between two polygons after applying translation and rotation corrections. This method is both easy to implement and computationally light, making it suitable for large-scale applications. The proposed measure demonstrates strong alignment with human perception of shape similarity. However, a notable limitation is that it tends to produce similarity values predominantly within the range of 70% to 100%. This behaviour arises because the measure emphasizes overall shape alignment while overlooking finer local discrepancies. As a result, subtle deviations, such as missing details or minor geometric distortions, may not significantly impact the computed similarity score. Despite this drawback, the method remains a practical and efficient alternative for evaluating shape similarity in large spatial datasets, particularly where computational simplicity and scalability are prioritized. Future works can explore potential refinements to enhance sensitivity to local shape variations while maintaining computational efficiency.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".