ICML 2025

DUNIA: Pixel-Sized Embeddings via Cross-Modal Alignment for Earth
Observation Applications
Ibrahim Fayad 1 2 Max Zimmer 3 Martin Schwartz 1 Fabian Gieseke 4 Philippe Ciais 1 Gabriel Belouze 1
Sarah Brood 5 Aurelien De Truchis 2 Alexandre d’Aspremont 5 2
Abstract
Significant efforts have been directed towards
adapting self-supervised multimodal learning for
Earth observation applications. However, most
current methods produce coarse patch-sized em-
beddings, limiting their effectiveness and inte-
gration with other modalities like LiDAR. To
close this gap, we present DUNIA, an ap-
proach to learn pixel-sized embeddings through
cross-modal alignment between images and full-
waveform LiDAR data. As the model is trained in
a contrastive manner, the embeddings can be di-
rectly leveraged in the context of a variety of envi-
ronmental monitoring tasks in a zero-shot setting.
In our experiments, we demonstrate the effec-
tiveness of the embeddings for seven such tasks:
canopy height mapping, fractional canopy cover,
land cover mapping, tree species identification,
plant area index, crop type classification, and per-
pixel waveform-based vertical structure mapping.
The results show that the embeddings, along with
zero-shot classifiers, often outperform specialized
supervised models, even in low-data regimes. In
the fine-tuning setting, we show strong perfor-
mances near or better than the state-of-the-art on
five out of six tasks.
1. Introduction
With the rapid expansion of Earth Observation (EO) satellite
missions, deep learning has emerged as a powerful solution
1Laboratoire des Sciences du Climat et de l’Environnement,
LSCE/IPSL, France
2Kayrros SAS, Paris 75009,
France
3Department for AI in Society, Science, and Technology, Zuse In-
stitute Berlin, Germany 4Department of Information Systems, Uni-
versity of M¨unster, Germany 5Department of Computer Science,
CNRS, INRIA & ´Ecole Normale Sup´erieure, Paris 75230, France.
Correspondence to: Ibrahim Fayad <ifayad@lsce.ipsl....

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