---
license: mit
task_categories:
  - visual-question-answering
  - image-classification
language:
  - en
tags:
  - extreme-weather
  - satellite-imagery
  - multimodal
  - vqa
  - benchmark
  - disaster-analysis
size_categories:
  - 1K<n<10K
---

# ObsCrisis-Bench

A multimodal benchmark for evaluating large vision-language models on extreme weather event analysis tasks.

## Dataset Description

ObsCrisis-Bench contains **4,202 VQA samples** across **127 extreme weather events** in **8 disaster categories**, covering **61 countries**. Each sample combines satellite multispectral imagery (AMSU-A, HIRS, MHS sensors) with optional weather station data, and requires models to perform risk assessment, type classification, timing prediction, and impact analysis.

## Disaster Categories

| Category | Events | VQA Samples | Subtypes |
|----------|--------|-------------|----------|
| cold-wave | 5 | 140 | cold-wave |
| earthquake | 10 | 417 | ground movement, tsunami |
| flood | 17 | 523 | coastal flood, flash flood, general flood, riverine flood |
| heat-wave | 5 | 165 | heat-wave |
| mass movement (wet) | 6 | 189 | landslide (wet), mudslide |
| storm | 55 | 1,852 | blizzard/winter storm, derecho, extra-tropical storm, general storm, hail, lightning/thunderstorm, sand/dust storm, severe weather, storm surge, tornado, tropical cyclone |
| volcanic activity | 15 | 450 | ash fall, general activity, lava flow, pyroclastic flow |
| wildfire | 14 | 466 | forest fire, general wildfire, land fire |
| **Total** | **127** | **4,202** | |

## Task Structure

Each VQA sample belongs to one of three task categories:

| Task Category | Samples | Percentage |
|---------------|---------|------------|
| Early Warning | 1,524 | 36.3% |
| Impact Assessment | 2,465 | 58.7% |
| Recovery Assessment | 213 | 5.1% |

### Task Types

**Early Warning** (t1, t8, t12):
- Risk Detection (381 samples): Assess the risk of an impending disaster
- Type Classification (381 samples): Identify the specific type of potential disaster
- Arrival Time Prediction (381 samples): Predict when the disaster will arrive
- Duration Prediction (381 samples): Predict how long the disaster will last

**Impact Assessment** (t1, t8, t12, t15, tmax):
- CPI / Crisis Prevention Index (610 samples): Estimate the crisis prevention index
- Total Affected (485 samples): Predict total affected population
- Total Deaths (455 samples): Predict total deaths
- No. Affected (400 samples): Predict number of affected people
- No. Injured (260 samples): Predict number of injured people
- Magnitude (185 samples): Predict earthquake magnitude
- No. Homeless (70 samples): Predict number of homeless people

**Recovery Assessment** (t15, tmax-1):
- Recovery Time Prediction (213 samples): Predict recovery time after the event

### Timestep Convention

- `t1` = 14 days before event start
- `t8` = 7 days before event start
- `t12` = 3 days before event start
- `t15` = event start day
- `tmax` = maximum impact time (event-dependent)
- `tmax-1` = one timestep before maximum impact
- `t_number = (image_date - event_start_date).days + 15`

## Dataset Structure

```
ObsCrisis-Bench/
├── cold-wave/                          # Satellite imagery
│   └── cold-wave1/<event_id>/<sensor>/<band>/t1.png ... tN.png
├── cold-wave_json/
│   └── All.json                        # VQA records
├── heat-wave/
├── heat-wave_json/All.json
├── earthquake/
├── earthquake_json/All.json
├── flood/
├── flood_json/All.json
├── mass movement (wet)/
├── mass movement (wet)_json/All.json
├── storm/
├── storm_json/All.json
├── volcanic activity/
├── volcanic activity_json/All.json
├── wildfire/
└── wildfire_json/All.json
```

### Satellite Sensors

The dataset includes multispectral imagery from three satellite sensors:

| Sensor | Bands | Description |
|--------|-------|-------------|
| AMSU-A | 15 bands (0-14) | Advanced Microwave Sounding Unit-A |
| HIRS | 20 bands (0-19) | High-resolution Infrared Radiation Sounder |
| MHS | 5 bands (0-4) | Microwave Humidity Sounder |

### Weather Station Data

Selected samples include supplementary weather station measurements (temperature, pressure, humidity, wind speed) when available for the event location.

## VQA Sample Fields

| Field | Type | Description |
|-------|------|-------------|
| `Question_id` | string | Unique identifier (format: `Task/Type/timestep/subtype/event_id`) |
| `Task` | string | Task category (Early Warning / Impact Assessment / Recovery Assessment) |
| `Subtask` | string | Subtask with timestep, e.g. "Risk Detection (t1)" |
| `Text` | string | Question text |
| `Image` | string | Comma-separated relative image paths |
| `Stations` | object | Weather station sensor data (optional) |
| `Ground truth` | string | Standard answer |

### QID Format

```
Task/Type/timestep/subtype/event_id
```
Examples:
- `Early Warning/Risk Detection/t1/ground movement/2014-0049-GRC`
- `Impact Assessment/Magnitude/tmax/tsunami/2011-0282-JPN`
- `Recovery Assessment/Recovery Time Prediction/t15/cold-wave1/2011-0105-MEX`

## Evaluation

The evaluation framework supports multiple scoring rules per subtask type:
- **Boolean**: Exact match (Risk Detection)
- **Numeric**: Error-based scoring with tolerance (CPI, Magnitude, population counts)
- **Classification**: Exact or partial match (Type Classification)
- **Text**: Jaccard word overlap (Arrival Time, Duration, Recovery Time)

Evaluation code is available at: [https://github.com/YYQ898/ObsCrisis-Bench](https://github.com/YYQ898/ObsCrisis-Bench)

## Citation

If you use this dataset, please cite:

```bibtex
@misc{obscrisis-bench,
  title={ObsCrisis-Bench: A Multimodal Benchmark for Extreme Weather Event Analysis},
  author={ObsCrisis Team},
  year={2025}
}
```

## License

MIT License