README.md

---
license: cc-by-nc-4.0
task_categories:
- robotics
tags:
- off-road-navigation
- traversability
- satellite-imagery
- lidar
- geospatial
size_categories:
- 1K<n<10K
---

# Offroad-global-nav Geospatial Dataset

## Overview

This repository contains the dataset introduced in:

> **“Learning Traversability-Aware Global Planners for Long Horizon Off-Road Navigation”**

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  

![Dataset Locations](./geotiff_map.png)

*Red markers indicate sampled regions used for dataset construction.*

![Satellite Images of different terrains](./dataset.png)

*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

```python
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