Powered by OpenAIRE graph
Found an issue? Give us feedback
image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ ZENODOarrow_drop_down
image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
ZENODO
Dataset . 2025
License: CC BY
Data sources: ZENODO
image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
ZENODO
Dataset . 2025
License: CC BY
Data sources: ZENODO
ZENODO
Dataset . 2025
License: CC BY
Data sources: Datacite
ZENODO
Dataset . 2025
License: CC BY
Data sources: Datacite
ZENODO
Dataset . 2025
License: CC BY
Data sources: Datacite
versions View all 3 versions
addClaim

Polygon Similarity Benchmark Dataset

Authors: Mallika Kankanamalage, Buddhi Ashan; Puri, Satish; Prasad, Sushil;

Polygon Similarity Benchmark Dataset

Abstract

Dataset Description: Polygon Similarity Benchmark Dataset Overview This dataset provides a curated collection of polygonal shapes derived from SpatialHadoop GIS dataset: Parks, Water Bodies, and Sports.It is intended to support research in polygon representation learning, geometric similarity search, and spatial indexing. The dataset includes raw polygonal geometries, pre-computed similarity ground truth, and supplementary documentation. For each dataset category, 80% of the polygons were used to build the similarity index, while the remaining 20% were reserved exclusively for evaluation. Dataset Contents The distributed ZIP package contains the following files: 1. ShapeToVecResults2.pdf A supplementary document containing additional experimental results referenced in the publication. 2. poly_data.zip A collection of polygonal GIS datasets extracted from SpatialHadoop. These represent the input geometries used for similarity computation. 3. Ground Truth Files These archives contain precomputed shape similarity results for each domain: parks.tar water_bodies.tar sports_all-query.tar.gz Each ground-truth archive consists of multiple text files, where each line represents a similarity query result. How to Extract the Archives You can extract any of the above `.tar` or `.tar.gz` archives using the methods below, depending on your operating system. Linux Use the tar command in the Terminal: tar -xvf archive_name.tar tar -xzvf archive_name.tar.gz This extracts the files into the current directory. macOS Extraction commands are the same as Linux. Open the Terminal and run: tar -xvf archive_name.tar tar -xzvf archive_name.tar.gz You may also double-click the archive in Finder to extract it automatically. Windows Open the PowerShell (Windows 10 and later): tar -xvf archive_name.tar tar -xzvf archive_name.tar.gz You may also use your preferred uncompress software on Windows (e.g., 7-Zip, WinRAR, or PeaZip) to extract both .tar and .tar.gz archives. Ground Truth Format Each line in a ground-truth file encodes: ... The first value is the ID of the input polygon. The subsequent values are the IDs of polygons determined to be most similar based on geometric shape similarity. The list of similar polygons is sorted in decreasing order of similarity, with the most similar polygon appearing first. These ground-truth lists were generated using geometric similarity (Jaccard Similarity) metrics for evaluation and benchmarking of vector-based polygon encodings. Intended Use This dataset is primarily designed for: Research on polygon representation learning, embedding models, and shape encoders. Benchmarking approximate nearest-neighbor (ANN) algorithms on spatial shape data. Studying spatial indexing, vector search strategies, and geometric similarity measures. GIS analytics, spatial data mining, and machine learning applications involving polygonal geometries.

Keywords

jaccard similarity, similarity search, GIS polygons

  • BIP!
    Impact byBIP!
    selected citations
    These citations are derived from selected sources.
    This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    0
    popularity
    This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
    Average
    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    Average
    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
    Average
Powered by OpenAIRE graph
Found an issue? Give us feedback
selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average