Gatorsense/Muuflgulfport: Hyperspectral and Lidar Data with Ground Truth

Gatorsense/Muuflgulfport Release 01 is an initial dataset release from the University of Florida. It includes hyperspectral and lidar data, ground spectra, lab spectra, and ground truth for emplaced targets and ground cover classes. The dataset was authored by Alina Zare.

Use Cases

• Developing target detection algorithms based on emplaced target ground truth.
• Training land cover classification models based on ground cover class labels.
• Fusing hyperspectral and lidar data for multimodal scene analysis.
• Validating remote sensing algorithms using ground and lab spectra.

Strengths

• Includes multiple complementary data modalities: hyperspectral imagery, lidar, and ground spectra.
• Provides ground truth for both emplaced targets and ground cover classes, enabling supervised learning.

Limitations

• Row count and dataset size are unknown, which may limit suitability assessment.
• Column-level documentation is absent; field semantics must be inferred after download.
• Last update date is unknown; freshness unverified.

Provenance

Source

University of Florida, authored by Alina Zare.

Collection Method

Data collection likely involves aerial or ground-based sensors.

Geography

Likely covers the Gulfport area.