Datasetpublic

knowledge-computing/FRIEDA

FRIEDA is a multimodal benchmark for open-ended cartographic reasoning over real-world map images.Each example pairs reference maps (and optional contextual maps) with a natural-language question and a reference answer. The benchmark targets common GIS relation types (i.e., topological, metric, directional) and includes questions that require multi-step reasoning and cross-map grounding. Dataset Summary Modality: image + text # Examples: 500 Input: map image(s) +… See the full description on the dataset page: https://huggingface.co/datasets/knowledge-computing/FRIEDA.

sourceHugging Faceupdated 8mo agoView on Hugging Face

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Dataset Card

![arXiv](https://arxiv.org/abs/2512.08016) ![Website](https://knowledge-computing.github.io/FRIEDA/) ![Code](https://github.com/knowledge-computing/FRIEDA)

FRIEDA is a multimodal benchmark for open-ended cartographic reasoning over real-world map images. Each example pairs reference maps (and optional contextual maps) with a natural-language question and a reference answer. The benchmark targets common GIS relation types (i.e., topological, metric, directional) and includes questions that require multi-step reasoning and cross-map grounding.

Dataset Summary

—Modality: image + text
—# Examples: 500
—Input: map image(s) + question text
—Output: expected answer (textual)
—Metadata: map_count, domain, relationship type, map elements

Languages

The dataset questions and answers are in English.

How to use it

python

from datasets import load_dataset

# Full dataset (split name = "data")
ds = load_dataset("knowledge-computing/FRIEDA", split="data")
print(ds[0].keys())
print(ds[0]["question_text"])    # Actual question being asked
print(ds[0]["images"])           # List of string paths to images (e.g., "images/...png")
print(ds[0]["context_images"])   # List of string paths to contextual images