Significant efforts have been directed towards adapting self-supervised multimodal learning for Earth observation applications. However, most current methods produce coarse patch-sized embeddings, limiting their effectiveness and integration with other modalities like LiDAR. To close this gap, we present DUNIA, an approach 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 directly leveraged in the context of a variety of environmental monitoring tasks in a zero-shot setting. In our experiments, we demonstrate the effectiveness 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 performances near or better than the state-of-the-art on five out of six tasks.

Methane emissions monitoring is essential to control methane pollution. In this paper, we propose an automatic practical methodology using time series to estimate the quantity of methane in a given plume using a multispectral satellite like Sentinel-2. Sentinel-2 proposes a low revisit time, a good spatial resolution and a low acquisition cost. Contrary to previous methods, the proposed approach does not require a manual selection of an optimal reference image. We compared its performance on an oil-and-gas site in Kazakhstan. This is the first step toward an automatic global monitoring system for methane plume detection and quantification with these satellites.