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Karsten Schrödter
University of Münster
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Sven Ligensa
University of Münster
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Martin Schwartz
LSCE, France
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References (28)
[3]
Galileo: Learning Global & Local Features of Many Remote Sensing Modalities
Founder's Pitch
"ECHOSAT provides a dynamic global tree height map for enhanced forest monitoring and carbon accounting."
Commercial Viability Breakdown
0-10 scaleHigh Potential
3/4 signals
Quick Build
4/4 signals
Series A Potential
4/4 signals
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Why It Matters
ECHOSAT provides dynamic forest monitoring by estimating canopy height changes over time, which is crucial for carbon accounting and climate change mitigation. It enables tracking of both growth and disturbances, offering a more realistic picture of forest dynamics than static, one-time measurements.
Product Angle
The product could be an API or a user interface providing access to up-to-date tree height maps, helping organizations comply with environmental regulations and manage land sustainably.
Disruption
Replaces static forest biomass estimates with a more dynamic and accurate assessment of forest health, improving decision-making in sectors reliant on ecosystem data.
Product Opportunity
With increasing global focus on environmental sustainability and carbon offset markets, there's a strong demand for accurate forest monitoring tools. Governments, NGOs, and industries like forestry and agriculture could utilize this service for planning and compliance.
Use Case Idea
A commercial application could offer subscription-based access to the dynamic tree height maps for industries such as logging, conservation, and carbon offsetting companies, allowing them to track forest growth and degradation in near real-time.
Science
The research uses a transformer-based model to perform temporal regression on multi-sensor satellite data. By integrating various data sources like Sentinel-2 imagery and GEDI LiDAR measurements, this approach generates a global time series map of tree heights. It applies a novel growth loss function to model natural growth and detect disturbances.
Method & Eval
The model leverages global satellite data and applies a vision transformer to predict changes in canopy height over time. It shows improvement over traditional methods by better capturing natural growth patterns and disturbances, validated against existing datasets.
Caveats
The system's accuracy is limited by the resolution and availability of underlying satellite data. It may not detect fine-scale disturbances quickly due to its reliance on intermittently acquired data sources.
Author Intelligence
Jan Pauls
LEAD University of Münster
jan.pauls@uni-muenster.de
Karsten Schrödter
University of Münster
Sven Ligensa
University of Münster
Martin Schwartz
LSCE, France
Berkant Turan
Zuse Institute Berlin
Max Zimmer
Zuse Institute Berlin
Sassan Saatchi
Jet Propulsion Laboratory, Caltech
Sebastian Pokutta
Technische Universität Berlin
Philippe Ciais
LSCE, France
Fabian Gieseke
University of Münster