README.md

---
license: cc-by-4.0
task_categories:
- image-classification
- object-detection
tags:
- biology
- ecology
- invasive-species
- spotted-lanternfly
- geolocation
- citizen-science
- conservation
- carnegie-mellon-university
- pittsburgh
- academic-research
size_categories:
- n<1K
language:
- en
pretty_name: CMU Campus Spotted Lanternfly Geolocated Dataset
---

# CMU Campus Spotted Lanternfly Geolocated Dataset

## Dataset Description

This dataset contains geolocated photographs of spotted lanternflies (Lycorma delicatula) collected around Carnegie Mellon University campus in Pittsburgh, Pennsylvania. The dataset was created as part of a class project (Project 1) focused on invasive species monitoring and geospatial data collection. Each entry includes high-resolution photographs with precise GPS coordinates, timestamps, and metadata for spatial analysis of lanternfly distribution patterns.

### Dataset Summary

The CMU Campus Spotted Lanternfly Geolocated Dataset consists of field-collected data from systematic surveys conducted around Carnegie Mellon University campus. The dataset includes:

- **Geolocated Images**: High-quality photographs with precise GPS coordinates
- **Temporal Data**: Timestamps for temporal analysis of sighting patterns
- **Spatial Metadata**: Location descriptions and environmental context
- **Quality Metrics**: Image quality and classification confidence scores

This dataset supports research on invasive species distribution patterns, urban ecology studies, and the development of geospatial analysis tools for environmental monitoring.

### Supported Tasks and Leaderboards

- **Image Classification**: Binary classification (Lantern Fly vs Non-Lantern Fly)
- **Object Detection**: Identification of spotted lanternflies in images
- **Geospatial Analysis**: Spatial distribution and clustering analysis
- **Temporal Analysis**: Time-series analysis of sighting patterns
- **Urban Ecology**: Study of invasive species in urban environments

## Dataset Structure

### Data Instances

Each data instance contains:
- **image**: High-resolution photograph (typically 512x512 or higher)
- **filename**: Unique identifier with timestamp and location information
- **latitude**: GPS latitude coordinate (decimal degrees)
- **longitude**: GPS longitude coordinate (decimal degrees)
- **timestamp**: Date and time of sighting (ISO 8601 format)
- **location_description**: Human-readable location description
- **confidence_score**: Classification confidence (0-1 scale)

### Data Fields

| Field | Type | Description |
|-------|------|-------------|
| image | Image | High-resolution photograph of spotted lanternfly |
| filename | String | Unique filename with timestamp and location |
| latitude | Float | GPS latitude coordinate (decimal degrees) |
| longitude | Float | GPS longitude coordinate (decimal degrees) |
| timestamp | String | ISO 8601 formatted timestamp |
| location_description | String | Human-readable location description |
| confidence_score | Float | Classification confidence (0-1) |

### Data Splits

| Split | Examples | Description |
|-------|----------|-------------|
| train | ~80% | Training data for model development |
| validation | ~10% | Validation data for model evaluation |
| test | ~10% | Test data for final model assessment |

## Dataset Creation

### Curation Rationale

This dataset was created as part of an academic project to:

1. **Study Invasive Species Distribution**: Understand spatial patterns of spotted lanternfly infestations in urban environments
2. **Develop Geospatial Tools**: Create tools for mapping and analyzing species distribution data
3. **Support Academic Research**: Provide high-quality data for ecological and entomological research
4. **Demonstrate Data Collection Methods**: Showcase systematic field data collection techniques

### Source Data

- **Collection Method**: Systematic field surveys around CMU campus
- **Geographic Scope**: Carnegie Mellon University campus and surrounding areas
- **Time Period**: Collected during academic semester (specific dates available in metadata)
- **Equipment**: Mobile devices with GPS capabilities for geolocation data

### Data Collection Process

1. **Field Surveys**: Systematic walking surveys of campus areas
2. **Photo Documentation**: High-quality photographs with GPS coordinates
3. **Metadata Recording**: Timestamp, location description, and environmental context
4. **Quality Control**: Verification of species identification and coordinate accuracy
5. **Data Validation**: Cross-checking of GPS coordinates and image quality

### Geographic Coverage

- **Primary Area**: Carnegie Mellon University campus, Pittsburgh, PA
- **Coordinate Range**: 
  - Latitude: ~40.44°N (approximate CMU campus center)
  - Longitude: ~-80.00°W (approximate CMU campus center)
- **Coverage Type**: Urban campus environment with diverse microhabitats

### Personal and Sensitive Information

No personal or sensitive information is included in this dataset. All images contain only biological specimens and environmental contexts. GPS coordinates are limited to public campus areas.

## Considerations for Using the Data

### Social Impact of Dataset

This dataset supports:
- **Academic Research**: Ecological studies of invasive species in urban environments
- **Educational Purposes**: Teaching materials for geospatial analysis and ecology
- **Conservation Efforts**: Understanding distribution patterns for management strategies
- **Methodological Development**: Tools for citizen science and environmental monitoring

### Discussion of Biases

Potential biases in the dataset include:
- **Temporal Bias**: Collection limited to specific time periods (academic calendar)
- **Spatial Bias**: Concentrated on CMU campus area
- **Weather Bias**: Collection may favor certain weather conditions
- **Accessibility Bias**: Limited to publicly accessible campus areas
- **Seasonal Bias**: Collection timing may not represent full seasonal patterns

### Other Known Limitations

- Limited geographic scope (single campus)
- Potential overrepresentation of certain microhabitats
- Variable lighting conditions across collection sessions
- Limited temporal coverage (academic semester timeframe)

## Additional Information

### Dataset Curators

- **Primary Curators**: Student Researchers
- **Institution**: Carnegie Mellon University
- **Course**: Project 1
- **Academic Year**: 2024

### Licensing Information

This dataset is released under the **Creative Commons Attribution 4.0 International License** (CC BY 4.0).

### Citation Information

```bibtex
@dataset{cmu_lanternfly_geolocated_2024,
  title={CMU Campus Spotted Lanternfly Geolocated Dataset: Field-collected geospatial data for invasive species research},
  author={Student Researchers},
  year={2024},
  institution={Carnegie Mellon University},
  course={Project 1},
  publisher={Hugging Face},
  url={https://huggingface.co/datasets/rlogh/lanternfly-data},
  license={CC BY 4.0}
}
```

### Academic Context

This dataset was created as part of an academic project focused on:
- **Geospatial Data Collection**: Methods for systematic field data collection
- **Invasive Species Monitoring**: Techniques for tracking invasive species
- **Urban Ecology**: Study of species distribution in urban environments
- **Data Science Applications**: Integration of geospatial and image data

### Usage Examples

```python
from datasets import load_dataset
import folium
import pandas as pd

# Load the dataset
dataset = load_dataset("username/cmu-lanternfly-geolocated")

# Create a map of sightings
m = folium.Map(location=[40.4406, -80.00], zoom_start=15)

# Add markers for each sighting
for item in dataset['train']:
    folium.Marker(
        [item['latitude'], item['longitude']],
        popup=f"Confidence: {item['confidence_score']:.2f}",
        icon=folium.Icon(color='red', icon='bug')
    ).add_to(m)

# Display the map
m
```

### Acknowledgments

- Carnegie Mellon University for providing the academic framework
- Course instructor for project guidance
- CMU campus community for access to collection sites
- The open-source community for geospatial analysis tools

### Future Work

Potential extensions of this dataset include:
- **Expanded Geographic Coverage**: Collection from additional Pittsburgh areas
- **Temporal Extension**: Multi-season data collection
- **Multi-species Data**: Inclusion of other invasive species
- **Environmental Variables**: Weather and habitat data integration

---

*This dataset represents academic work in geospatial data collection and invasive species monitoring at Carnegie Mellon University.*