Portrait de Marlena Reil

Marlena Reil

marlena.reil@mila.quebec
Maîtrise recherche - McGill
Superviseur⋅e principal⋅e
David Rolnick
Sujets de recherche
Apprentissage profond
Vision par ordinateur
GitHub

Publications

SnowGalileo: A Pre-trained Earth Observation Transformer for Daily, 100 m Fractional Snow Cover Mapping
Marlena Reil
Julia Kaltenborn
Donovan J. M. Allum
Francis Pelletier
Sebastian Roessler
Samip Shrestha
Zhibang Lv
J Truckenbrodt
Gabriele Schwaizer
Thomas Nägler
John W. Pomeroy
Christopher B. Marsh
Benoît Montpetit
Tobias Jonas
Gabriel Tseng
David Rolnick
A.J. Dietz
Celia A. Baumhoer
Abstract. Mountain snow is an important component of the cryosphere that directly affects downstream livelihoods. Accurate monitoring of mou… (voir plus)ntain snow is crucial, yet remains challenging due to complex topography and frequent cloud cover. Although combining data from multiple Earth Observation (EO) satellites can improve spatial and temporal coverage, extracting Fractional Snow Cover (FSC) from sensors with different spatial and temporal resolutions remains difficult. AI-based Earth foundation models can process diverse sensor inputs and have been successfully applied to various remote sensing tasks; however, they often lack snow-specific design considerations. This paper presents SnowGalileo, a pre-trained transformer model designed to integrate EO data to map snow-covered areas. We evaluated SnowGalileo's potential for generating daily, gap-free FSC maps at 100m resolution in mountainous regions. SnowGalileo combines one week of satellite time-series data from multiple sensors, including Sentinel-1, Sentinel-2, Landsat, Sentinel-3, MODIS, and VIIRS, with topographic, land cover, and meteorological information to predict FSC for a given day. The model was pre-trained using masked autoencoding and fine-tuned using labeled data from various mountain ranges across the Northern Hemisphere. In addition to clear-sky conditions, SnowGalileo is evaluated under a wider variety of conditions than was previously possible, including cloud cover and lack of high-resolution (~10–30 m) satellite imagery. For the Canadian Rockies and the Swiss Alps, respectively, SnowGalileo achieves RMSEs of 0.099 and 0.124 on clear days, 0.144 and 0.209 on cloudy days, 0.185 and 0.262 on days without high-resolution imagery, and 0.200 and 0.299 on cloudy days without high-resolution imagery. SnowGalileo also consistently outperforms random forests, support vector regressors, and multi-layer perceptrons. While the current product is a proof of concept that has undergone limited validation across geographic regions, operates on 1km × 1km tiles rather than full maps, and has restricted capabilities in challenging conditions, this approach could ultimately enable the continuous, gap-free operational generation of FSC time series for mountain regions worldwide.
2026-09-07
EGUsphere (accepté)
doi.org
Galileo: Learning Global&Local Features of Many Remote Sensing Modalities
Gabriel Tseng
Anthony Fuller
Marlena Reil
Henry Herzog
Patrick Beukema
Favyen Bastani
James R Green
Evan Shelhamer
Hannah Kerner
David Rolnick
We introduce a highly multimodal transformer to represent many remote sensing modalities - multispectral optical, synthetic aperture radar, … (voir plus)elevation, weather, pseudo-labels, and more - across space and time. These inputs are useful for diverse remote sensing tasks, such as crop mapping and flood detection. However, learning shared representations of remote sensing data is challenging, given the diversity of relevant data modalities, and because objects of interest vary massively in scale, from small boats (1-2 pixels and fast) to glaciers (thousands of pixels and slow). We present a novel self-supervised learning algorithm that extracts multi-scale features across a flexible set of input modalities through masked modeling. Our dual global and local contrastive losses differ in their targets (deep representations vs. shallow input projections) and masking strategies (structured vs. not). Our Galileo is a single generalist model that outperforms SoTA specialist models for satellite images and pixel time series across eleven benchmarks and multiple tasks.
2025-10-05
Proceedings of the 42nd International Conference on Machine Learning (publié)
doi.org
proceedings.mlr.press