twin*

The TWIN* team investigates the impact of climate change on sensitive high-altitude wetlands--known as fens--through the innovative use of artificial intelligence (AI), drone technology (UAS), and high-speed computer clusters (supercomputers). 

The team gratefully acknowledges the support of Arizona State University's HIRBI (Herberger Institute Research Building Investment) Seed Grant Program, ASU's Research Office, and the Telluride Institute.

*While the satellite and ground-based data utilized in this project are not "real time," our goal is to produce a data processing pipeline that rapidly processes "near real-time" data--especially from regularly scheduled data uploads from satellite based sensors.  In  this sense, the idea of a "digital twin" for planet earth is fast approaching reality.  The acronymn"TWIN" is an allusion to this idea.  Here is a working definition of the concept of a "digital twin":

A digital twin is a dynamic, virtual copy of a physical object, system, or process, using real-time data from sensors (IoT) to mirror its real-world counterpart's behavior, performance, and conditions throughout its lifecycle, enabling simulation, analysis, and better decision-making without touching the physical asset. It's a "living" model that evolves with live data, unlike static simulations, allowing for "what-if" scenarios, predictive maintenance, and optimized operations.  --AI generated definition