README.md

---
license: cc-by-4.0
language:
  - en
pretty_name: StatMapCorpus
size_categories:
  - 10K<n<100K
annotations_creators:
  - machine-generated
task_categories:
  - image-classification
tags:
  - cartography
  - thematic-maps
  - statistical-maps
  - choropleth
  - vlm-annotations
  - silver-standard
  - web-crawl
configs:
  - config_name: default
    data_files:
      - split: full
        path: deposit/statmapcorpus_dataset.parquet
---

# 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.

> **This repository is a mirror.** The citable source of record is the deposit in Dane
> Badawcze UW (University of Warsaw): **https://doi.org/10.58132/FSJGSP**, version 2.0.
> The thirteen files in `deposit/` are byte-identical to that release; `deposit/schema.json`
> carries their SHA-256 sums so you can verify this yourself. Cite the DOI, not this URL.

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.

- **Code:** https://github.com/MartaSolarz/StatMapCorpus (MIT), archived at
  https://doi.org/10.5281/zenodo.23215421.

## 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 (`deposit/VALIDATION.md`). The errors are systematic, not
  random noise. Labels for these four types should be verified before they are used as
  ground truth; `deposit/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.
- **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.

## Files

Everything under `deposit/` is the published release, unmodified.

| File | Contents |
|---|---|
| `statmapcorpus_dataset.parquet` | The main table: one row per map, 23,549 rows, 30 columns. |
| `embeddings_clip_vitl14.npz` | CLIP ViT-L/14 image embeddings, float32, 23,549 × 768, keyed by `uid`. |
| `candidates.parquet` | All 203,371 candidates sampled in Stage 3, with the outcome of each step from the URL check to Gate 4. |
| `validation.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` | The Stage 1 dictionaries: phrases and their weights. |
| `download_images.py` | Retrieves the images and verifies them by SHA-256 and perceptual hash. |
| `schema.json` | Field names, data types and SHA-256 checksums of all files. |
| `CODEBOOK.md` | Every column of every data file: type, permitted and observed values, caveats. |
| `VALIDATION.md` | Results of the technical validation. |
| `DATASHEET.md` | The dataset documented following *Datasheets for Datasets*. |
| `NOTICE.md` | Provenance, licences and the procedure for removal requests. |
| `README.md` | The deposit's own README, as published. |

The dataset viewer shows the main table only. The other files have to be loaded directly.

```python
from huggingface_hub import hf_hub_download
import numpy as np, pandas as pd

repo = "msolarz/StatMapCorpus"
df = pd.read_parquet(hf_hub_download(repo, "deposit/statmapcorpus_dataset.parquet",
                                     repo_type="dataset"),
                     dtype_backend="numpy_nullable")
npz = np.load(hf_hub_download(repo, "deposit/embeddings_clip_vitl14.npz",
                              repo_type="dataset"))
uids, emb = npz["uids"], npz["emb"]          # emb.shape == (23549, 768)
```

## 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): a map covering at least about half of the image. Gate 3 (Claude Haiku
   4.5): a statistical map with quantitative data referenced to administrative or statistical
   units, with English lettering. Gate 4 (Claude Sonnet 4.6): at least one of the seven
   thematic map types, the legend type and the model's confidence.

The images were downloaded on 1 August 2026 and annotated on 1–2 August 2026. Each map carries
seven non-exclusive thematic map type labels (choropleth, diagram, isoline, dot density, heat
map, cartogram, flow map), a legend type, 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

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

Every downloaded file is checked against the `sha256` recorded at annotation time. Where the
hash differs but the 64-bit `phash` still matches, the host has only re-encoded the file and
the labels still apply. Where 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.

## Licence

Annotations, metadata and documentation are CC BY 4.0. The CLIP embeddings are derived from
MapPool and are also CC BY 4.0. `download_images.py` is MIT; the Hub records a single licence
per repository, so the MIT grant is stated here and in the file's own header. Rights in the
images remain with their owners; they are neither redistributed nor licensed here.

`deposit/NOTICE.md` gives the full attribution, the derivation chain from MapPool and
CommonPool/DataComp, and the procedure for removal requests. Removal requests are handled
against the deposit, and this mirror is updated to match.

## Citation

```bibtex
@dataset{solarz2026statmapcorpus,
  author    = {Solarz, Marta},
  title     = {{StatMapCorpus}: 23,549 English-language statistical maps from {MapPool},
               annotated by thematic map type using vision-language models},
  year      = {2026},
  publisher = {Dane Badawcze UW},
  version   = {V2},
  doi       = {10.58132/FSJGSP},
  url       = {https://doi.org/10.58132/FSJGSP}
}
```

Please also cite MapPool, from which the identifiers and embeddings are inherited:
Schnürer, R. (2024). *MapPool – Bubbling up an extremely large corpus of maps for AI.*
Abstracts of the ICA 8, 22. https://doi.org/10.5194/ica-abs-8-22-2024

## Versions

| Tag | Deposit version | Notes |
|---|---|---|
| `v1.0` | V1 (2026-08-24) | Initial release. Model-generated annotations, no human validation. |
| `v2.0` | V2 | Validation files, candidate list, datasheet; "cartographic method" renamed to "thematic map type"; file names without a version number. The main table has the same columns and values as in V1. |

## Contact

Marta Solarz, Faculty of Geography and Regional Studies, University of Warsaw –
m.solarz2@uw.edu.pl

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.