deposit/README.md

StatMapCorpus v2

23,549 English-language statistical maps from 16,374 web domains, identified in MapPool and annotated by thematic map type with vision-language models.

• Dataset (version of record): https://doi.org/10.58132/FSJGSP – Dane Badawcze UW, version 2.0. Version 1 remains available under the same DOI.
• Data descriptor: Solarz, M. A corpus of English-language statistical maps from the web annotated by thematic map type. Scientific Data (submitted).
• Code: https://github.com/MartaSolarz/StatMapCorpus (MIT): the pipeline, the prompts, the export of these files and the analyses of the validation.
• Mirror of the main table: https://huggingface.co/datasets/msolarz/StatMapCorpus

The deposit contains annotations, identifiers, image embeddings and documentation. No images are redistributed: each record points to an image on its original host, and an included script retrieves the images and verifies them against the checksums recorded at annotation time.

Files

File	Format	Contents
statmapcorpus_dataset.parquet	Apache Parquet	The main table: one row per map, 23,549 rows, 30 columns. Identifiers and provenance, image properties and checksums, labels.
embeddings_clip_vitl14.npz	NumPy NPZ	CLIP ViT-L/14 image embeddings, float32, 23,549 × 768, keyed by uid.
candidates.parquet	Apache Parquet	All 203,371 candidates sampled in Stage 3, with the outcome of each step from the URL check to Gate 4.
validation.csv	CSV	Labels of two human coders for the 157-map validation sample, model labels from two repeated runs of Gates 3 and 4 for 300 maps, and a blind audit of 150 images.
positive_keywords.json, negative_keywords.json	JSON	The Stage 1 dictionaries: phrases and their weights.
download_images.py	Python	Retrieves the images from their URLs and verifies them by SHA-256 and perceptual hash.
schema.json	JSON	Field names, data types and SHA-256 checksums of all files.
CODEBOOK.md	Markdown	Every column of every data file: type, permitted and observed values, caveats.
VALIDATION.md	Markdown	Results of the technical validation.
DATASHEET.md	Markdown	The dataset documented following Datasheets for Datasets.
NOTICE.md	Markdown	Provenance, licences and the procedure for removal requests.

What changed from version 1

• New files: candidates.parquet, validation.csv, VALIDATION.md and DATASHEET.md.
• The labels have been validated. They were checked against two human coders, by repeating the model calls and by an audit of rejected images (VALIDATION.md). Version 1 stated that no human validation had been done.
• File names no longer carry a version number. The version is that of the deposit (2.0). statmapcorpus_dataset_v1.parquet of version 1 is now statmapcorpus_dataset.parquet.
• The main table is unchanged. It has the same columns and values as in version 1; only the file name and the dataset entry of its metadata changed. The embeddings file and the dictionaries are unchanged.
• Terminology follows the data descriptor. "Cartographic method" is now "thematic map type". The column names method_* and methods_count are kept, so that code written for version 1 still runs. The codebook gives the definition of each type (Table 3 of the data descriptor) and the numbering of the gates.

How the corpus was built

MapPool (Schnürer 2024) holds 75.9 million map-like images from the CommonPool collection of DataComp, each with its URL, alt-text and CLIP embedding. StatMapCorpus was selected from it in four stages, ordered by increasing cost per record:

1. Keyword filter. Weighted phrase dictionaries in 21 languages are matched against the alt-text; records scoring at least −1 are retained (54.9 million).
2. Image classifier. An SVM with a radial basis function kernel on the CLIP ViT-L/14 embeddings, trained with active learning. Records with a predicted probability of at least 0.5 are passed on (about 1.68 million).
3. Deduplication, sampling and retrieval.
   • URLs are normalised and deduplicated.
   • Records with a probability of at least 0.80 are sampled, at most five per domain: 203,371 candidates.
   • The URLs are checked and the images downloaded: 116,573 images.
4. Vision-language cascade.
   • Gate 1 (rule): the shorter side is at least 400 px.
   • Gate 2 (Claude Haiku 4.5): the image shows a map that covers at least about half of it.
   • Gate 3 (Claude Haiku 4.5): a statistical map with quantitative data referenced to administrative or statistical units, with English lettering. Phenomena measured by sensors or physical models — weather, climate, hydrology, pollution, geology, ecology — are excluded.
   • Gate 4 (Claude Sonnet 4.6): at least one of the seven thematic map types; it also records the legend type and its confidence.

The images were downloaded on 1 August 2026 and annotated on 1–2 August 2026. candidates.parquet records the outcome of every step of Stages 3 and 4.

Each map carries seven non-exclusive thematic map type labels: choropleth, diagram, isoline, dot density, heat map, cartogram and flow map. It also has a legend type (classed, continuous or not applicable), a one-sentence description, the model's self-reported confidence, image properties, SHA-256 and perceptual hashes, and a CLIP image embedding. The alt-text is not redistributed; it belongs to MapPool and can be joined on uid.

Getting the images

python download_images.py --data statmapcorpus_dataset.parquet --out images/ --verify-phash

Every downloaded file is checked against the sha256 recorded at annotation time:

• The hash matches: the file is the one that was annotated.
• The hash differs but the 64-bit phash matches: the host has only re-encoded the file, and the labels still apply.
• Neither matches: the image has changed, and its labels should not be used.

All 23,549 images were retrievable on 1 August 2026, and the share of reachable URLs will decline over time. The embeddings remain available for every record, but they were computed on images downscaled to at most 512 px.

Before you use it

• The labels are a silver standard. Against two human coders, precision was 0.90–0.97 for choropleths, diagrams and cartograms, 0.63–0.71 for heat maps, dot density and flow maps, and 0.52 for isolines (VALIDATION.md). The errors are systematic, not random noise. Labels for these four types should be verified before they are used as ground truth; validation.csv lets you estimate the error rate for a chosen subset.
• Splits. Group records by phash, not by duplicate_group_id: 3,294 rows share a perceptual hash with another row without being byte-identical. Grouping also by domain further reduces leakage between maps from the same publisher.
• Class balance. The type labels are multi-label and highly imbalanced. Choropleth covers 88.7% of records, and each of the five rarest types covers less than 1.5%. Report macro-averaged metrics.
• Legend type. A null value in legend_is_classed means not applicable (no legend, a qualitative legend or an illegible one), not a missing value. Do not treat it as a continuous legend.
• Maps without lettering. The 17 maps without lettering are in the corpus only incidentally, because Gate 3 required English lettering. They are not a sample of textless maps.
• Composition. The corpus is shaped by the selection design: a web crawl, a classifier threshold of 0.80 and at most five images per domain. It is not a representative sample of statistical maps on the web, and it is not exhaustive even among the candidates examined: an audit suggests that about 2,600 qualifying images were rejected, almost all at Gate 3.

Licence, citation and contact

Annotations, metadata and documentation are licensed CC BY 4.0. The CLIP embeddings are derived from MapPool and are also licensed CC BY 4.0. download_images.py is MIT. Rights in the images remain with their owners. NOTICE.md gives the full attribution, the derivation chain and the procedure for removal requests.

Please cite the dataset and the data descriptor:

Solarz, Marta, 2026, "StatMapCorpus: 23,549 English-language statistical maps from MapPool, annotated by thematic map type using vision-language models", https://doi.org/10.58132/FSJGSP, Dane Badawcze UW, V2.

Marta Solarz, Faculty of Geography and Regional Studies, University of Warsaw – m.solarz2@uw.edu.pl · ORCID 0009-0001-4134-8500

Funded by the University of Warsaw under the Excellence Initiative – Research University (IDUB) programme, through a microgrant for doctoral students (Action IV.4.1), BOB-IDUB-622-272-2026.