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Features of the World

Applying the field-boundary approach to a broader set of physical features visible from space — training models capable of detecting roads, buildings, solar panels, trees, and more at global scale.

Aerial view of a densely packed neighborhood with red-roofed houses, green trees, and narrow roads winding through the area.

A global, regularly updated map of built and natural features gives governments, development organizations, and businesses a new window into how the physical landscape is changing. Applications range from tracking renewable energy deployment and disaster damage assessment to monitoring deforestation, land-use change, and urban growth in rapidly developing regions — all from a single, open platform.

Research Partners
Kerner Lab, Arizona State University
Multimodal Vision Research Lab, WashU
Microsoft AI for Good Lab

Research Focus

Dataset Compilation

Labeled data drawn from publicly available map sources, paired with satellite imagery from multiple providers to cover an initial set of built and natural features.

Feature Detection Models

AI models trained to detect and map roads, buildings, solar panels, trees, and additional features.

Open-World Generalization

Models flexible enough to identify new feature types without full retraining — enabling the dataset to expand beyond features anticipated at launch.

World Model Integration

Methods for estimating feature coverage efficiently by drawing on neural world models developed in Fields of The World.

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Fields of the World

Expanding the world’s first open ecosystem for global agricultural field boundary detection from satellite imagery — combining a global benchmark dataset, baseline ML models, inference tools, and web applications.

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