README.md

---
license: cc-by-4.0
task_categories:
  - visual-question-answering
language:
  - en
tags:
  - spatial-reasoning
  - map-understanding
  - multi-choice-qa
configs:
  - config_name: t1_no_visual_supervision
    data_files:
      - split: test
        path: data/t1_no_visual_supervision.jsonl
  - config_name: t1_query_supervision
    data_files:
      - split: test
        path: data/t1_query_supervision.jsonl
  - config_name: t2_no_visual_supervision
    data_files:
      - split: test
        path: data/t2_no_visual_supervision.jsonl
  - config_name: t2_query_supervision
    data_files:
      - split: test
        path: data/t2_query_supervision.jsonl
  - config_name: t3_no_visual_supervision
    data_files:
      - split: test
        path: data/t3_no_visual_supervision.jsonl
  - config_name: t3_oracle_supervision
    data_files:
      - split: test
        path: data/t3_oracle_supervision.jsonl
  - config_name: t3_query_supervision
    data_files:
      - split: test
        path: data/t3_query_supervision.jsonl
  - config_name: t4_no_visual_supervision
    data_files:
      - split: test
        path: data/t4_no_visual_supervision.jsonl
  - config_name: t4_oracle_supervision
    data_files:
      - split: test
        path: data/t4_oracle_supervision.jsonl
  - config_name: t4_query_supervision
    data_files:
      - split: test
        path: data/t4_query_supervision.jsonl
---

# Map-based Spatial Reasoning Benchmark

A multi-view map-based spatial reasoning benchmark. Each row is one multiple-choice
question instance over a registered map image; models must answer with a single option
letter. Four tasks (T1–T4), four base-map views, and controlled evidence conditions
(direct / query / oracle) and world perturbations (transform / world layers) allow
fine-grained analysis of spatial reasoning robustness.

## Task overview

| Task | Question type | Description |
|---|---|---|
| T1 | direction / angular_order / egocentric_side / dual_anchor_direction | Cardinal direction, clockwise angular ordering, and egocentric side between colored point markers |
| T2 | composite_euclidean_distance / composite_network_distance / nearest_point / directional_nearest_point | Comparing metric or network distances between marked points, and identifying the nearest point |
| T3 | segment_building_count (subtasks: `route`, `seg`) | Counting first-row buildings along a directed route segment |
| T4 | waypoint_ordering / route_validity / detour_waypoint_shortest / detour_waypoint_traversability | Waypoint ordering, route validity, and detour planning under road closures |

## Supervision conditions

Each task is released under one or more *supervision conditions*, which differ only in
the visual evidence overlaid on the registered map images — the questions, options and
answers are identical across conditions of the same task.

| File | `condition` | Available for | Visual evidence |
|---|---|---|---|
| `*_no_visual_supervision.jsonl` | `direct` | T1–T4 | None — the bare base map, no task annotations |
| `*_query_supervision.jsonl` | `query` | T1–T4 | Query-dependent scaffolding that supports answering the question (e.g., route segments, measured rays, partial building-footprint scaffolds) |
| `*_oracle_supervision.jsonl` | `oracle` | T3–T4 | Complete oracle annotations of the ground-truth solution |

Note: for T4 `detour_waypoint_traversability`, the `query` condition uses the
fully annotated road-status / waypoint-map pair, since the weak scaffold does not
cover this question type.

## Sample counts (per condition)

T1/T2 provide 2 conditions each; T3/T4 provide 3. All conditions of a task share the
same instance set. Total: **77,800 rows** (40,000 for T1/T2 + 13,800 for T3 + 24,000
for T4).

### T1 — 10,000 per condition

Base question set = 400 instances per view. 7 geometric transform variants (rot90,
rot180, rot270, mirror_h, mirror_h_rot90/180/270) replicate the base set.

| view | composition | rows |
|---|---|---|
| sat | base 400 + 7 transforms x 400 | 3,200 |
| wprd01 | base 400 + 7 transforms x 400 | 3,200 |
| blank | base 400 + 7 transforms x 400 | 3,200 |
| webrd04 | base 400 only | 400 |
| **total** | | **10,000** |

### T2 — 10,000 per condition

Same structure as T1 (400 base instances, same view/transform coverage): 3,200 x 3 + 400 = **10,000**.

### T3 — 4,600 per condition

Base question set = 200 instances per view; world layer has 2 variants
(`intervention_001`, `sham_001`) with 200 each. The `blank` view is not used for T3:
the task is building-footprint recognition, which is only meaningful on imagery that
actually shows buildings (satellite / map tiles).

| view | composition | rows |
|---|---|---|
| sat | base 200 + 7 transforms x 200 + 2 world x 200 | 2,000 |
| wprd01 | base 200 + 7 transforms x 200 + 2 world x 200 | 2,000 |
| webrd04 | base 200 + 2 world x 200 | 600 |
| **total** | | **4,600** |

### T4 — 8,000 per condition

Base question set = 400 instances; world layer = 2 variants x 200. The `blank` view is
not used for T4: the task is road/waypoint recognition on the route network, which
requires imagery that actually depicts roads (satellite / map tiles).

| view | composition | rows |
|---|---|---|
| sat | base 400 + 7 transforms x 400 + 2 world x 200 | 3,600 |
| wprd01 | base 400 + 7 transforms x 400 + 2 world x 200 | 3,600 |
| webrd04 | base 400 + 2 world x 200 | 800 |
| **total** | | **8,000** |

## Dimensions

| Field | Values | Meaning |
|---|---|---|
| `view` | `sat` / `webrd04` / `wprd01` / `blank` | Base map style: satellite imagery / road map / place-label map / blank background. **Note:** the `webrd04` (road-map) view is only provided for the `base` layer — its tiles contain textual labels (e.g., Chinese characters), which would become unreadable or mirrored after rotation/flipping, so no `transform` variants are generated for this view |
| `layer` | `base` / `transform` / `world` | Original scene, geometric transform stress test, or world intervention |
| `variant` | `rot90`, `rot180`, `rot270`, `mirror_h`, `mirror_h_rot*` (transform); `intervention_001`, `sham_001` (world); `null` (base) | Sub-variant of the layer. Note: transform variants have rotated/flipped map frames — "north is up" applies to the transformed image |
| `condition` | `direct` / `query` / `oracle` | Visual evidence condition (see [Supervision conditions](#supervision-conditions)) |

## Row schema

```json
{
  "id": "c0019932db15_blank_query",
  "task_id": "T1",
  "layer": "base",
  "variant": null,
  "view": "blank",
  "condition": "query",
  "question_type": "angular_order",
  "question": "From the green point, sweeping clockwise from north, ...",
  "options": ["A. yellow, red", "B. red, yellow"],
  "answer": "B",
  "answer_letter": "B",
  "system_prompt": "You are evaluating spatial reasoning ...",
  "images": ["images/t1/c_06d46000d7/s_429d01__q_e5ebc72207__base__query__blank_query_q4.png"],
  "meta": {
    "case_id": "c_06d46000d7",
    "scheme": "s_429d01",
    "instance_id": "q_e5ebc72207",
    "tile_type": "blank",
    "subtask": null
  }
}
```

- `options` are pre-shuffled; the correct letter is `answer_letter`.
- Evaluation is exact match on the option letter for all rows.
- Multiple-choice prompt format: append `Options:\n<options>` and instruct the model to
  output only the letter (see `system_prompt`).

## Image naming

Images live under `images/{task}/{case_code}/`. Directory segments `case_id`, `scheme`
and `question_dir` are anonymized with deterministic short codes (`c_*`, `s_*`, `q_*`);
the remaining segments (`layer`, `condition`, original file name) are kept verbatim, e.g.

```
images/t1/c_06d46000d7/s_429d01__q_e5ebc72207__base__query__blank_query_q4.png
```

The same source image may be referenced by several rows (e.g., shared across transform
variants or views); 77,800 rows reference 27,394 unique image files.

## Loading

```python
from datasets import load_dataset
from huggingface_hub import snapshot_download
from PIL import Image

# Download the image files once (the jsonl only stores relative image paths)
root = snapshot_download("mapspatial/map-spatial-benchmark", repo_type="dataset")

ds = load_dataset("mapspatial/map-spatial-benchmark", "t1_query_supervision", split="test")
row = ds[0]
img = Image.open(f"{root}/{row['images'][0]}")
```

## Anonymization note

Identifiers of the source map data (case / scheme / question directory) are replaced by
deterministic hash codes and are not reversible from this repository.