FathomDEM, a new global 30 m DTM
Alessandro Samuel-Rosa
Dear colleagues,
I think that many of you may find the following of your interest. Please share with your colleagues.
FathomDEM, a new global 30 m DTM
Location: Zoom link: https://uqac.zoom.us/j/89439078911
When: Tuesday, January 20, 2026, 11:00 – 12:00
Description: Zoom link: https://uqac.zoom.us/j/89439078911
Dr. Chris Lucas
Fathom company
Chris Lucas is the Principal Machine Learning Engineer at Fathom,
a global water intelligence company. Holding a PhD in High Energy
Physics, his 11 years in industry span from software engineering
to ML research, contributing to projects such as scaling medical
diagnosis models and developing new computer vision techniques for
vehicle analysis. At Fathom, he leverages his combined ML,
software, and scientific expertise to advance multiple levels of
the company's modeling stack, from terrain modeling (DEMs) to
generative AI for climate downscaling and hydrological modeling
with graph neural networks.
Abstract: Accurate digital elevation models (DEMs) are
foundational inputs for a vast array of geomorphometry
applications, including natural hazard modeling, glaciology, and
infrastructure planning. However, existing global DEMs, such as
Copernicus DEM (COPDEM), often contain surface features like trees
and buildings, limiting their effectiveness as Digital Terrain
Models (DTMs). This talk introduces FathomDEM, a new global 30 m
DTM created using a novel machine-learning methodology. We
utilized a hybrid vision transformer model within a U- Net
architecture to perform pixel-wise regression, analyzing and
correcting the height biases in COPDEM. This approach differs
significantly from previous methods (like the pixel-by-pixel
correction used for FABDEM) by leveraging 2D spatial information
(context) as an inductive basis, essentially employing 'computer
vision' to achieve more spatially coherent and robust corrections.
FathomDEM was trained on extensive, diverse LiDAR reference data
and has been rigorously validated, demonstrating: Improved
Accuracy, surpassing the accuracy of existing best-ranked global
DEMs; Excellent Performance in Specific Landscapes, showing
reduced error even when compared to specialized coastal DEMs
(e.g., DeltaDTM); High Utility in Downstream Tasks, when utilised
in flood inundation modeling, FathomDEM achieves increased
accuracy, approaching the performance levels of models derived
from high-resolution LiDAR data. Join this session for an informal
discussion on the methodology behind FathomDEM, its novel use of
ML for artifact removal, and its potential to improve applied
geomorphometry tasks globally.
Ciências do Solo para o
Desenvolvimento Sustentável
(2025-2034)
Alessandro Samuel-Rosa
Professor & Pesquisador
Ciência do Solo – Pedometria
www.pedometria.org
Universidade Tecnológica Federal do
Paraná
Prolongamento da Rua Cerejeira, s/n, Sala E8
Bairro São Luiz
CEP 85892-000
Santa Helena, Paraná, Brasil
Telefone: +55 (45) 3268-8800
Website: www.utfpr.edu.br/santahelena
Alessandro Samuel-Rosa
Dear all,
I forgot to mention the correct time and time zone of the event:
January 20th (Tuesday), 2026
14:00 UTC
Good to know that the topic if of interest for many of you.