---
license: cc-by-nc-sa-4.0
task_categories:
- object-detection
- image-classification
tags:
- disaster-response
- tornado-damage
- building-damage
- computer-vision
- deep-learning
- yolo
- infrastructure
- resilience
size_categories:
- 1K<n<10K
---

# TornadoNet Dataset: Street-View Building Damage Assessment

## Dataset Description
**TornadoNet** is a comprehensive benchmark dataset for automated post-disaster building damage assessment using street-level imagery. The dataset contains high-resolution geotagged images collected following the December 10-11, 2021 Midwest U.S. tornado outbreak, providing realistic conditions for evaluating modern object detection architectures on multi-level damage classification tasks.

### Dataset Summary
- **Total Images**: 3,333 high-resolution street-view images
- **Annotated Instances**: 8,890 building instances
- **Damage Classes**: 5 levels (DS0-DS4) based on IN-CORE classification
- **Collection Method**: Vehicle-mounted 360° cameras
- **Resolution**: 4K equirectangular images
- **Geographic Coverage**: 4 of the 9 U.S. states affected by 2021 Midwest tornado outbreak
- **Annotation Standard**: IN-CORE (Interdependent Networked Community Resilience Modeling Environment)

### Supported Tasks
- **Object Detection**: Detect and localize damaged buildings in street-view imagery
- **Multi-class Classification**: Classify building damage severity into 5 ordinal levels
- **Damage Assessment**: Support automated post-disaster reconnaissance and response

---

## Quick Start

### Loading the Dataset
```python
from datasets import load_dataset

# Load full dataset
dataset = load_dataset("crumeike/tornadonet-datasets")

# Access specific split
train_data = dataset["train"]
val_data = dataset["validation"]
test_data = dataset["test"]
```

### Using with YOLO
```python
# Dataset is already in YOLO format
# Annotation format: <class> <x_center> <y_center> <width> <height>

# Example YOLO configuration (data.yaml)
"""
path: /path/to/tornadonet
train: images/train
val: images/val
test: images/test

nc: 5  # number of classes
names: ['DS0_Undamaged', 'DS1_Slight', 'DS2_Moderate', 'DS3_Extensive', 'DS4_Complete']
"""
```

### Evaluation Metrics
For ordinal classification tasks, consider using:
- **Standard Metrics**: mAP@0.5, F1-score, Precision, Recall
- **Ordinal Metrics**: 
  - Ordinal Top-k Accuracy
  - Mean Absolute Ordinal Error (MAOE)
  - Confusion matrices emphasizing near-miss errors

### Baseline Models
Benchmark results on TornadoNet:

| Model | mAP@0.5 | F1 Score | Ordinal Top-1 Acc | MAOE |
|-------|---------|----------|-------------------|------|
| YOLOv8n | 40.98% | 45.11% | 84.01% | 0.78 |
| YOLOv8l | 42.09% | 46.41% | 84.19% | 0.78 |
| YOLO11x | 46.05% | 49.40% | 85.20% | 0.76 |
| RT-DETR-L | 39.87% | 44.77% | 88.13% | 0.65 |

###  Ordinal Supervision Impact
| Model | Configuration | mAP@0.5 | Δ vs Baseline | Ordinal Top-1 | MAOE |
|-------|---------------|---------|---------------|---------------|------|
| RT-DETR-L | ψ=0.5, K=1 | **44.70%** | **+4.8 pp** | **91.15%** | **0.56** |

*See [full paper](https://arxiv.org/abs/2603.11557) for detailed experimental results and analysis.*

---

## Dataset Structure

### Data Instances
Each instance consists of:
- **Image**: High-resolution street-view photograph
- **Bounding boxes**: YOLO format annotations (class x_center y_center width height)
- **Damage class**: Integer label (0-4) corresponding to damage severity
- **Metadata**: Geolocation data (when available)

### Data Fields

The annotations follow YOLO format:
```
<class_id> <x_center> <y_center> <width> <height>
```

Where:
- `class_id`: Damage state (0=DS1, 1=DS2, 2=DS3, 3=DS4, 4=DS0,)
- `x_center`, `y_center`: Normalized bounding box center coordinates (0-1)
- `width`, `height`: Normalized bounding box dimensions (0-1)

**Damage State Definitions (IN-CORE Framework):**
| Class ID| Label | Description | Typical Indicators |
|-------|-------|-------------|-------------------|
| 0 | DS1 - Slight | Minor cosmetic damage | 2-15% roof covering damaged, 1 window/door failure |
| 1 | DS2 - Moderate | Noticeable damage, repairable | 15-50% roof damage, 2-3 windows/doors failed |
| 2 | DS3 - Extensive | Severe damage, major repairs needed | >50% roof damage, >3 windows/doors failed, 1-3 roof sheathing sections failed |
| 3 | DS4 - Complete | Structural collapse or near-total destruction | >35% roof sheathing failed, roof-to-wall connection failure |
| 4 | DS0 - Undamaged | No visible structural damage | Intact roof, windows, walls |

### Data Splits

| Split | Images | Instances | Percentage |
|-------|--------|-----------|------------|
| Train | ~2,500 | 6,184 | 75% |
| Validation | ~500 | 1,342 | 15% |
| Test | ~500 | 1,364 | 15% |

**Class Distribution** (across all splits):
- DS0 (Undamaged): ~45%
- DS1 (Slight): ~25%
- DS2 (Moderate): ~15%
- DS3 (Extensive): ~10%
- DS4 (Complete): ~5%

Note: The dataset exhibits natural class imbalance, with fewer instances of severe damage (DS3-DS4).

## Dataset Creation

### Curation Rationale
Traditional manual post-disaster damage assessments are:
- Labor-intensive and time-consuming
- Subject to cognitive biases and inconsistencies
- Unsafe for personnel in hazardous areas
- Unable to provide real-time, building-level information

TornadoNet was created to:
1. Enable development of automated damage assessment systems
2. Benchmark modern object detection architectures for disaster response
3. Support research in ordinal classification for severity grading
4. Provide standardized evaluation protocols for damage detection models

### Source Data

#### Initial Data Collection
- **Event**: December 10-11, 2021 Midwest U.S. tornado outbreak
- **Collection Timing**: ~3 weeks post-event
- **Equipment**: Vehicle-mounted GoPro cameras (360° panoramic video)
- **Coverage**: Prioritized heavily impacted areas based on:
  - Post-event aerial imagery
  - Preliminary damage reports
  - Social vulnerability indices
- **Processing**: Automatic extraction of building-centered frames using geospatial alignment

#### Who are the source data producers?
Data collection was conducted by the Center of Excellence for Risk-Based Community Resilience Planning (CoE), a NIST-funded center, as part of a longitudinal field study to support empirical validation of the IN-CORE modeling platform.

### Annotations

#### Annotation Process
1. **Manual Annotation**: Trained researchers drew bounding boxes around individual buildings
2. **Damage Classification**: Each instance labeled according to IN-CORE five-level damage classification
3. **Quality Control**: Two-stage validation process:
   - Initial annotation by trained annotators
   - Secondary expert review for consistency verification
4. **Tools**: LabelImg and custom annotation interfaces
5. **Guidelines**: Archetype-specific indicators for 19 structural archetypes (T1-T19), with majority being residential wood-frame structures (T1-T5)

#### Who are the annotators?

Annotations were performed by trained researchers familiar with structural engineering and disaster damage assessment, following standardized IN-CORE guidelines. All annotations underwent expert cross-validation.

### Personal and Sensitive Information
The dataset contains street-view imagery of buildings in disaster-affected areas. While efforts were made to focus on structural damage:
- No personally identifiable information (PII) was intentionally collected
- Images may incidentally capture public spaces, vehicles, or street scenes
- Geographic metadata is included for research purposes
- Researchers should use appropriate care when publishing derived visualizations


## Additional Information

### Dataset Curators
TornadoNet was curated by researchers from:
- Johns Hopkins University
- University of Alabama
- University of South Alabama

In collaboration with the Center of Excellence for Risk-Based Community Resilience Planning (NIST-funded).

### Licensing Information
**License**: Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)

This dataset is released for research and educational purposes. Commercial use requires separate permission.

### Citation Information
If you use this dataset, please cite:

```bibtex
@article{umeike2026tornadonet,
  title={TornadoNet: Real-Time Building Damage Detection with Ordinal Supervision},
  author={Umeike, Robinson and Pham, Cuong and Hausen, Ryan and Dao, Thang and Crawford, Shane and Brown-Giammanco, Tanya and Lemson, Gerard and van de Lindt, John and Johnston, Blythe and Mitschang, Arik and Do, Trung},
  journal={arXiv preprint arXiv:2603.11557},
  year={2026}
}
```

### Contributions
Dataset collection and annotation were supported by:
- Center of Excellence for Risk-Based Community Resilience Planning (NIST Cooperative Agreement 70NANB15H044)
- SciServer computational resources (NSF Award ACI-1261715)

### Related Resources

- **Code Repository**: https://github.com/crumeike/TornadoNet
- **IN-CORE Platform**: https://incore.ncsa.illinois.edu/
- **Project Documentation**: https://arxiv.org/abs/2603.11557

### Contact
For questions, issues, or collaboration opportunities:
- **GitHub Issues**: https://github.com/crumeike/TornadoNet/issues
- **Dataset Maintainer**: crumeike@crimson.ua.edu

### Updates and Versions
**Version 1.0** (Initial Release)
- 3,333 images with 8,890 annotated instances
- Train/Val/Test splits (75%/15%/15%)
- IN-CORE 5-level damage classification

---
**Acknowledgments**: We thank the affected communities, first responders, and all those who contributed to disaster response and recovery efforts.