--- 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-* 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)](https://nauticalcharts.noaa.gov/charts/noaa-enc.html) ENCs encoded using the [IHO S-57 standard](https://iho.int/en/s-57-standards-in-force) and rendered using [OpenCPN](https://opencpn.org/). 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 The data directory follows the pattern: ```bash 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 ``` The image path for each question is stored relative to the directory containing `questions.csv`. For example: ```text images/US1EEZ1M_encperception_756619221_lat15.060136_lon145.793954_labeled.png ``` ## 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 be labeled as: ```text task = enc_perception subtask = incontext_symbol_recognition q_type = ADMARE/ADMARE ``` A spatial-reasoning example can be labeled as: ```text 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. ## 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. ### Spatial Reasoning Spatial Reasoning evaluates whether a model can reason about relationships between multiple chart features. The task includes: - **Topological** - 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 with neutral visual identifiers such as **red** and **blue**, rather than by their ENC object classes. ### Joint Perception and Spatial Reasoning Joint tasks require both ENC 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, hazards, traffic routes, object attributes, and spatial relationships. Application tasks 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. - **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: | Column | Description | |---|---| | `id` | Unique string identifier for the example | | `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` 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 indicating whether the target can be referred by a name or object type. | | `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. In most cases, 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`. | | `img_center` | Image-center coordinate in WGS84 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`, `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: ```text 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 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] ``` ## 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: ```json [ { "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 distinct images for the image fields: - `image`: the primary image associated with the question. - `image_alt`: a paired alternate rendering of the same scene. In most cases, 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. ## Dataset statistics | 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 ```python 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) ```