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Soils as a Living System

scroll down Water Flow Nutrient Cycling Healthy Soil, Resilient Futures Soil Properties Learn More 01. 02 03
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Ask Better Questions Across Layers

Explore the app scroll down Cross-layer queries Spatiotemporal Patterns Linked layers, Sharper questions Contextual Reasoning 01 02. 03
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GeoAI and Geospatial Intelligence

scroll down Physics-guided AI Process Linkages Research Place-aware AI, better decisions Landscape Signals 01 02 03.
Digital Twin

The Soil Twin : Exploring the Processes That Shape Soil

Soils, despite their apparent simplicity, are a world unto itself. It nourishes plants, anchoring them in place while ferrying essential nutrients upward. It holds and drains water, modulating moisture levels in ways that dictate the success of crops. And it is home to an entire ecosystem of microorganisms, an unseen but essential network that ensures fertility and resilience. To model this complexity, our work weaves together meteorological and environmental data, capturing the intricate interplay of moisture, salinity, and nutrients across time and space.

This digital twin is intended to not just be a static map but a living, breathing representation; an evolving portrait of the soil’s unseen processes. Through an advanced visualization engine, users can peel back layers, watch animations unfold, and observe phenomena rendered in ways that reveal the deep logic of the land. In doing so, we move beyond mere data and into understanding, transforming numbers into narratives and analysis into insight, making the hidden world of soil visible at last.

Physics-informed GeoAI

Big Data for the Sciences

Ecology

Making soil systems visible and usable

Capabilities

How We Get There

Our Services

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The SoilTwin App

A browser-based digital twin for exploring, querying, and visualizing soil systems across space and time.

Video Tutorials

Short videos that walk you through various capabilities and controls within the SoilTwin app.

Research: Science Informed GeoAI

Peruse our peer-reviewed papers on science informed deep neural networks.

Research Papers

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