Hugging Face's logo

Datasets:
YOO-uN-ee
/
ENCQA-test

Languages:
English
Dataset card Files
xet
 Community
1
ENCQA-test / README.md
YOO-uN-ee's picture
YOO-uN-ee
Update README.md
a5f3482 verified
preview code
|
Raw
History Blame Contribute Delete
17.7 kB
metadata 
dataset_info:
  - config_name: Application
    features:
      - name: id
        dtype: large_string
      - name: task
        dtype: large_string
      - name: subtask
        dtype: large_string
      - name: q_type
        dtype: large_string
      - name: question
        dtype: large_string
      - name: answer
        dtype: large_string
      - name: referrable
        dtype: bool
      - name: image
        dtype: image
      - name: image_alt
        dtype: image
      - name: cell
        dtype: large_string
      - name: target_layer
        dtype: large_string
      - name: subdivision
        dtype: large_string
      - name: img_center
        dtype: large_string
      - name: features
        dtype: large_string
    splits:
      - name: train
        num_bytes: 19868297
        num_examples: 561
      - name: test
        num_bytes: 19868297
        num_examples: 561
    download_size: 38753116
    dataset_size: 39736594
  - config_name: ENC_Perception
    features:
      - name: id
        dtype: large_string
      - name: task
        dtype: large_string
      - name: subtask
        dtype: large_string
      - name: q_type
        dtype: large_string
      - name: question
        dtype: large_string
      - name: answer
        dtype: large_string
      - name: referrable
        dtype: bool
      - name: visible
        dtype: bool
      - name: image
        dtype: image
      - name: image_alt
        dtype: image
      - name: cell
        dtype: string
      - name: target_layer
        dtype: string
      - name: img_center
        dtype: large_string
      - name: feature
        dtype: string
    splits:
      - name: train
        num_bytes: 338935136
        num_examples: 11850
      - name: test
        num_bytes: 338935136
        num_examples: 11850
    download_size: 442261896
    dataset_size: 677870272
  - config_name: Joint
    features:
      - name: id
        dtype: large_string
      - name: task
        dtype: large_string
      - name: subtask
        dtype: large_string
      - name: q_type
        dtype: large_string
      - name: question
        dtype: large_string
      - name: answer
        dtype: large_string
      - name: referrable
        dtype: bool
      - name: visible
        dtype: bool
      - name: image
        dtype: image
      - name: image_alt
        dtype: image
      - name: cell
        dtype: large_string
      - name: target_layer
        dtype: large_string
      - name: img_center
        dtype: large_string
      - name: feature
        dtype: large_string
    splits:
      - name: train
        num_bytes: 495980580
        num_examples: 321
      - name: test
        num_bytes: 495980580
        num_examples: 321
    download_size: 988185534
    dataset_size: 991961160
  - config_name: Spatial_Reasoning
    features:
      - name: id
        dtype: large_string
      - name: task
        dtype: large_string
      - name: subtask
        dtype: large_string
      - name: q_type
        dtype: large_string
      - name: question
        dtype: large_string
      - name: answer
        dtype: large_string
      - name: referrable
        dtype: bool
      - name: visible
        dtype: bool
      - name: image
        dtype: image
      - name: image_alt
        dtype: image
      - name: cell
        dtype: large_string
      - name: target_layer
        dtype: large_string
      - name: img_center
        dtype: large_string
      - name: feature
        dtype: large_string
    splits:
      - name: train
        num_bytes: 1764549611
        num_examples: 400
      - name: test
        num_bytes: 1764549611
        num_examples: 400
    download_size: 3518112486
    dataset_size: 3529099222
configs:
  - config_name: Application
    data_files:
      - split: train
        path: Application/train-*
      - split: test
        path: Application/test-*
  - config_name: ENC_Perception
    data_files:
      - split: train
        path: ENC_Perception/train-*
      - split: test
        path: ENC_Perception/test-*
    default: true
  - config_name: Joint
    data_files:
      - split: train
        path: Joint/train-*
      - split: test
        path: Joint/test-*
  - config_name: Spatial_Reasoning
    data_files:
      - split: train
        path: Spatial_Reasoning/train-*
      - split: test
        path: Spatial_Reasoning/test-*
      - split: test-balanced
        path: Spatial_Reasoning/test-balanced-*
language:
  - en

ENC-QA

ENC-QA evaluates vision-language model (VLM) understanding of Electronic Navigational Charts (ENCs). The benchmark measures whether models can interpret standardized nautical-chart symbols and text, reason about spatial relationships among chart features, combine ENC perception with spatial reasoning, and make navigation-relevant judgments from rendered ENC imagery.

ENC-QA is created from National Oceanic and Atmospheric Administration (NOAA) ENCs encoded using the IHO S-57 standard and rendered using OpenCPN. The benchmark contains approximately 56,000 questions derived from 6,835 NOAA ENCs.

The benchmark consists of four tasks in increasing complexity:

  1. ENC Perception: recognize and interpret symbols, objects, text, and symbol-text associations visible in an ENC render.
  2. Spatial Reasoning: reason about topology, distance, and direction between chart features.
  3. Joint Perception and Spatial Reasoning: identify relevant chart features and use them jointly to answer spatial questions.
  4. Application: synthesize perceptual and spatial information into navigation-relevant judgments.

Data organization

Each benchmark example is represented by a row in a questions.csv file. The row contains the question, answer, image path, chart-cell identifier, task labels, and structured metadata used to generate or verify the example.

A task directory follows the general pattern:

ENC-QA/
├── data/
│   ├── ENC_Perception/
│   │   ├── isolated_symbol_recognition/
│   │   │   ├── questions.csv
│   │   │   └── images/
│   │   ├── incontext_symbol_recognition/
│   │   │   ├── questions.csv
│   │   │   └── images/
│   │   ├── symbol_text_association/
│   │   │   ├── questions.csv
│   │   │   └── images/
│   │   └── ...
│   ├── Spatial_Reasoning/
│   │   └── ...
│   ├── Joint/
│   │   └── ...
│   └── Application/
│       └── ...
└── README.md

Within each task directory, image paths in questions.csv are stored relative to the directory containing the CSV. For example:

images/US1EEZ1M_encperception_756619221_lat15.060136_lon145.793954_labeled.png

The CSV files are the authoritative definition of the examples included in each benchmark task.

Task hierarchy

ENC-QA uses three fields to identify where an example belongs in the benchmark:

  • task — broad benchmark level.
  • subtask — specific task within that level.
  • q_type — fine-grained question or portrayal type.

For example, an in-context symbol-recognition example can use:

task     = enc_perception
subtask  = incontext_symbol_recognition
q_type   = ADMARE/ADMARE

A spatial-reasoning example can use:

task     = spatial_reasoning
subtask  = topological
q_type   = within

q_type should be treated as the fine-grained category produced by the question-generation pipeline. Its format depends on the task. For some ENC-perception questions it can contain an object class and portrayal separated by /, while for spatial-reasoning questions it can directly identify the tested relation.

Task definitions

ENC Perception

ENC Perception evaluates whether a model can correctly identify and interpret individual chart elements.

The task includes:

  • Isolated Symbol Recognition: identify an ENC object or symbol from an isolated or localized visual example.
  • In-Context Symbol Recognition: identify an ENC object within a rendered chart scene.
  • Symbol-Text Association: associate a chart symbol or feature with the corresponding visible text or label.

For example:

task     = enc_perception
subtask  = incontext_symbol_recognition

In-context symbol-recognition questions ask the model to identify the object or feature represented by a highlighted point symbol, line, or area.

Spatial Reasoning

Spatial Reasoning evaluates whether a model can reason about relationships between multiple chart features while minimizing dependence on ENC-specific semantic knowledge.

The task includes:

  • Topology
    • within
    • touch
    • overlap
    • cross
    • equal
  • Distance
    • quantitative distance
    • qualitative distance
  • Direction
    • cardinal / 8-direction reasoning
    • exact bearing reasoning

To isolate spatial reasoning capability, the relevant objects or features are referred to using neutral visual identifiers such as red and blue, rather than by their ENC object classes. Questions can require true/false answers, counts, numeric distances, directional labels, or bearings grounded in the rendered chart.

Joint Perception and Spatial Reasoning

Joint tasks require both visual grounding and spatial reasoning. A model must identify the relevant ENC features and then reason over their spatial relationship.

These tasks are designed to test whether a model can successfully combine the ENC-perception and spatial-reasoning capabilities that are evaluated separately in the lower-level tasks.

Application

Application tasks evaluate whether a model can synthesize multiple ENC cues into a navigation-relevant judgment. These questions can require combining navigational aids, restricted areas, depth information, hazards, traffic routes, shoreline features, object attributes, and spatial relationships.

Current Application subdivisions include:

  • Travel Direction: determine the intended or appropriate direction of travel from information visible on the chart.
  • Travel Permission: determine whether travel or entry is permitted in the relevant chart area. This includes questions about whether travel is permitted and whether entry into a particular area is restricted.
  • Change Criticality: identify changes between two versions of a chart and determine whether those changes are critical to navigational safety.

Application examples may rely on both visible chart evidence and ENC feature attributes. The visible metadata field indicates whether the information needed to recover the answer is visually available in the rendered image.

CSV schema

The current schema uses the following 15 columns when both natural-language and S-57-coded answers are included:

Column Description
id Unique string identifier for the example. In ENC-perception data, the identifier can include the ENC cell, task, a generated numeric token, and image-center latitude/longitude.
task Broad benchmark task, e.g. enc_perception.
subtask Specific benchmark subtask, e.g. incontext_symbol_recognition.
q_type Fine-grained question or portrayal category, e.g. ADMARE/ADMARE, POSGEN/POSGEN04, MAGVAR/MAGVAR51, or within.
question Natural-language prompt given to the model.
answer Natural-language ground-truth answer.
answer_alt Alternate ground-truth answer using the S-57 object code, portrayal code, abbreviation, or other task-specific coded representation when applicable.
referrable Boolean metadata flag produced by the question-generation pipeline indicating whether the target is marked as referrable.
visible Boolean indicating whether the information required for the answer is considered visible in the rendered image.
image Relative path to the primary image used for the question.
image_alt Relative path to a paired alternate rendering when available. For in-context recognition, this is the corresponding scene without the target highlight/annotation.
cell NOAA ENC cell identifier, e.g. US1EEZ1M.
target_layer S-57/ENC layer containing the target source feature, e.g. ADMARE, COALNE, or LNDELV. This is not necessarily identical to the final portrayal label or answer.
img_center Image-center coordinate stored as [longitude, latitude].
feature JSON-encoded list containing structured metadata for the source feature or features used to generate the question.

Not every task necessarily requires every optional field. Fields such as answer_alt, image_alt, target_layer, img_center, or feature may be empty when they are not applicable to a particular task.

Example row

A simplified in-context symbol-recognition example is:

id:
US1EEZ1M_encperception_756619221_lat15.060136_lon145.793954

task:
enc_perception

subtask:
incontext_symbol_recognition

q_type:
ADMARE/ADMARE

question:
The image displays a symbol used to represent ENC data on an ECDIS.
What type of object or feature does the highlighted area portray?
Give the final answer in <answer> </answer> tags.

answer:
Administration Area (Named)

answer_alt:
ADMARE

visible:
True

image:
images/US1EEZ1M_encperception_756619221_lat15.060136_lon145.793954_labeled.png

image_alt:
images/US1EEZ1M_encperception_756619221_lat15.060136_lon145.793954.png

cell:
US1EEZ1M

target_layer:
ADMARE

img_center:
[145.79395375541344, 15.06013575]

The prompt's <answer> </answer> instruction specifies the expected model-output format. The CSV ground-truth fields themselves store the answer text without requiring the tags.

For example, either of the following can be formatted for model evaluation:

<answer>Administration Area (Named)</answer>

or, when evaluating against the coded alternative:

<answer>ADMARE</answer>

Feature metadata

The feature column stores a JSON-encoded list containing metadata for the source feature or features associated with an example.

Common fields include:

Field Description
layer ENC layer associated with the feature.
geometry_type Geometry class, such as point, line, or polygon.
geometry Serialized geometry representation for the source feature.
fid Feature identifier used by the source data-processing pipeline.
rcid S-57 record identifier.
object_name Human-readable object-class name when available.
target_label Target label produced for the task.
target_label_source Generator metadata describing how target_label was selected.
portrayal_name Portrayal or symbol identifier associated with the target.
attributes Serialized dictionary containing source S-57 attributes.

For example:

[
  {
    "layer": "ADMARE",
    "geometry_type": "polygon",
    "fid": 141,
    "rcid": 142,
    "object_name": "Administration Area (Named)",
    "target_label": "ADMARE",
    "target_label_source": "object_class_for_polygon_task",
    "portrayal_name": "ADMARE",
    "geometry": "...",
    "attributes": "..."
  }
]

geometry and attributes may themselves be stored as serialized strings rather than recursively expanded JSON objects.

Primary and alternate images

Some ENC-QA tasks contain two image fields:

  • image — the primary image associated with the question.
  • image_alt — a paired alternate rendering of the same scene.

For in-context symbol recognition, filenames follow the convention:

image:
images/{id}_labeled.png

image_alt:
images/{id}.png

The primary image contains the target highlight or annotation used by the question. The alternate image preserves the corresponding chart scene without that target annotation. It may still contain normal chart labels and other OpenCPN-rendered content.

Identifier and coordinate convention

For ENC-perception examples, identifiers can follow the pattern:

{cell}_encperception_{generated_id}_lat{latitude}_lon{longitude}

For example:

US1EEZ1M_encperception_756619221_lat15.060136_lon145.793954

The corresponding img_center is stored in GIS coordinate order:

[longitude, latitude]

For the example above:

[145.79395375541344, 15.06013575]

Do not interpret img_center as [latitude, longitude].

Dataset statistics

Final counts should be filled in after dataset generation, filtering, deduplication, and split assignment are complete.

Configuration Total
ENC Perception TODO
 Isolated Symbol Recognition TODO
 In-Context Symbol Recognition TODO
 Symbol-Text Association TODO
Spatial Reasoning TODO
 Topological TODO
 Distance TODO
 Direction TODO
Joint Perception and Spatial Reasoning TODO
Application TODO
 Travel Direction TODO
 Travel Permission TODO
 Change Criticality TODO
Total ~56k

Data sources

NOAA Electronic Navigational Charts

ENC-QA is derived from NOAA Electronic Navigational Charts. ENCs are vector nautical charts encoded using the IHO S-57 data model and contain navigational objects, attributes, geometry, and chart metadata.

Data checkpoint: June 1, 2026.

Chart rendering

ENC source data are converted into rendered chart imagery for the visual benchmark using OpenCPN. Structured ENC information is used to identify source features, attributes, and geometric relationships, while the rendered chart is provided as the visual model input.

Example usage

Reading the CSV 

from pathlib import Path

import pandas as pd

task_root = Path(
    "/path/to/ENC-QA/data/ENC_Perception/incontext_symbol_recognition"
)

questions = pd.read_csv(task_root / "questions.csv")

row = questions.iloc[0]

image_path = task_root / row["image"]
image_alt_path = (
    task_root / row["image_alt"]
    if pd.notna(row.get("image_alt"))
    else None
)

print(row["id"])
print(row["task"])
print(row["subtask"])
print(row["q_type"])
print(row["question"])
print(row["answer"])
print(row.get("answer_alt"))
print(image_path)
print(image_alt_path)