Datasets:
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---
task_categories:
- visual-question-answering
language:
- en
size_categories:
- n<1K
configs:
- config_name: default
data_files:
- split: data
path: data/data-*
dataset_info:
features:
- name: question_ref
dtype: string
- name: images
list: string
- name: question_text
dtype: string
- name: expected_answer
dtype: string
- name: map_count
dtype: string
- name: spatial_relationship
dtype: string
- name: answer_type
dtype: string
- name: domain
dtype: string
- name: map_elements
list: string
- name: context_images
list: string
splits:
- name: data
num_bytes: 576010
num_examples: 500
download_size: 120231
dataset_size: 576010
pretty_name: FRIEDA
---
[](https://arxiv.org/abs/2512.08016)
[](https://knowledge-computing.github.io/FRIEDA/)
[](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 |