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J

Jan Pauls

University of Münster

K

Karsten Schrödter

University of Münster

S

Sven Ligensa

University of Münster

M

Martin Schwartz

LSCE, France

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References (28)

[1]
Canopy height and biomass distribution across the forests of Iberian Peninsula
2025Yang Su, Martin Schwartz et al.
[2]
DUNIA: Pixel-Sized Embeddings via Cross-Modal Alignment for Earth Observation Applications
2025Ibrahim Fayad, Max Zimmer et al.
[3]
Galileo: Learning Global & Local Features of Many Remote Sensing Modalities
2025Gabriel Tseng, A. Fuller et al.
[4]
Monitoring changes of forest height in California
2025Samuel Favrichon, Jake H. Lee et al.
[5]
Retrieving yearly forest growth from satellite data: A deep learning based approach
2025Martin Schwartz, P. Ciais et al.
[6]
AnySat: One Earth Observation Model for Many Resolutions, Scales, and Modalities
2024Guillaume Astruc, Nicolas Gonthier et al.
[7]
The enduring world forest carbon sink
2024Yude Pan, R. Birdsey et al.
[8]
An Image is Worth More Than 16x16 Patches: Exploring Transformers on Individual Pixels
2024Duy-Kien Nguyen, Mahmoud Assran et al.
[9]
Tree canopy extent and height change in Europe, 2001–2021, quantified using Landsat data archive
2023S. Turubanova, P. Potapov et al.
[10]
CROMA: Remote Sensing Representations with Contrastive Radar-Optical Masked Autoencoders
2023A. Fuller, K. Millard et al.
[11]
The overlooked contribution of trees outside forests to tree cover and woody biomass across Europe
2023Siyu Liu, M. Brandt et al.
[12]
Lightweight, Pre-trained Transformers for Remote Sensing Timeseries
2023Gabriel Tseng, Ivan Zvonkov et al.
[13]
Very high resolution canopy height maps from RGB imagery using self-supervised vision transformer and convolutional decoder trained on aerial lidar
2023James M. Tolan, Hung-I Yang et al.
[14]
Forest Structure Characterization in Germany: Novel Products and Analysis Based on GEDI, Sentinel-1 and Sentinel-2 Data
2023Patrick Kacic, Frank Thonfeld et al.
[15]
Self-Supervised Learning from Images with a Joint-Embedding Predictive Architecture
2023Mahmoud Assran, Quentin Duval et al.
[16]
GEDI launches a new era of biomass inference from space
2022R. Dubayah, J. Armston et al.
[17]
A high-resolution canopy height model of the Earth
2022Nico Lang, W. Jetz et al.
[18]
Masked Autoencoders Are Scalable Vision Learners
2021Kaiming He, Xinlei Chen et al.
[19]
Video Swin Transformer
2021Ze Liu, Jia Ning et al.
[20]
Swin-Unet: Unet-like Pure Transformer for Medical Image Segmentation
2021Hu Cao, Yueyue Wang et al.

Showing 20 of 28 references

Founder's Pitch

"ECHOSAT provides a dynamic global tree height map for enhanced forest monitoring and carbon accounting."

Remote Sensing and Environmental MonitoringScore: 9View PDF ↗

Commercial Viability Breakdown

0-10 scale

High Potential

3/4 signals

7.5

Quick Build

4/4 signals

10

Series A Potential

4/4 signals

10

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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