The dataset is designed to support long-range off-road navigation using multi-modal geospatial data, combining large-scale overhead sensing with real-world human driving behavior.

Unlike traditional off-road datasets that focus on local perception, this dataset enables global planning over kilometer-scale environments.

📊 Dataset Summary

Scenes: 299 geographically diverse locations (more will be added)
Coverage: ~1,244 km²
Human driving data: ~1,130 km of GPS trajectories

Modalities

🛰️ Satellite imagery (GeoTIFF)
🌐 Aerial LiDAR point clouds (LAZ)
🗺️ OpenStreetMap vectors (OSM XML)
📍 Human trajectories (KML)

Each scene is fully geo-referenced and co-registered.

🗺️ Geographic Coverage

The dataset spans diverse terrain types across the United States, including:

Deserts
Grasslands
Forests
Mountains
Quarries and mines

Red markers indicate sampled regions used for dataset construction.

*Satellite Images from different locations with diverse terrain.

🧱 Data Structure

🔍 Modalities Explained

Satellite Imagery

High-resolution RGB imagery capturing:

Vegetation
Trails
Water bodies
Terrain appearance

LiDAR Point Clouds

Dense aerial LiDAR (5–27 pts/m²) providing:

Elevation (height)
Surface normals (slope)
Intensity (surface reflectivity)

OpenStreetMap (OSM)

Vector priors including:

Roads and trails
Waterways

Used as weak semantic supervision.

Human GPS Trajectories

Real-world driving paths used as:

Implicit supervision for traversability
Ground truth for path preference

🚀 Getting Started

from datasets import load_dataset

dataset = load_dataset("anony-008/offroad-global-nav")

sample = dataset["data"][0]

🤝 Acknowledgements

This dataset builds upon publicly available geospatial data sources including:

USGS LiDAR

ArcGIS satellite imagery

OpenStreetMap

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