TY - CHAP A1 - Fayad, Ibrahim A1 - Zimmer, Max A1 - Schwartz, Martin A1 - Ciais, Philippe A1 - Gieseke, Fabian A1 - Belouze, Gabriel A1 - Brood, Sarah A1 - De Truchis, Aurelien A1 - d'Aspremont, Alexandre T1 - DUNIA: Pixel-Sized Embeddings via Cross-Modal Alignment for Earth Observation Applications T2 - Proceedings of the 42nd International Conference on Machine Learning Y1 - 2025 UR - https://raw.githubusercontent.com/mlresearch/v267/main/assets/fayad25a/fayad25a.pdf VL - 267 SP - 16375 EP - 16406 ER - TY - CHAP A1 - Pauls, Jan A1 - Zimmer, Max A1 - Kelly, Una M A1 - Schwartz, Martin A1 - Saatchi, Sassan A1 - Ciais, Philippe A1 - Pokutta, Sebastian A1 - Brandt, Martin A1 - Gieseke, Fabian T1 - Estimating canopy height at scale T2 - Proceedings of the 41st International Conference on Machine Learning N2 - We propose a framework for global-scale canopy height estimation based on satellite data. Our model leverages advanced data preprocessing techniques, resorts to a novel loss function designed to counter geolocation inaccuracies inherent in the ground-truth height measurements, and employs data from the Shuttle Radar Topography Mission to effectively filter out erroneous labels in mountainous regions, enhancing the reliability of our predictions in those areas. A comparison between predictions and ground-truth labels yields an MAE/RMSE of 2.43 / 4.73 (meters) overall and 4.45 / 6.72 (meters) for trees taller than five meters, which depicts a substantial improvement compared to existing global-scale products. The resulting height map as well as the underlying framework will facilitate and enhance ecological analyses at a global scale, including, but not limited to, large-scale forest and biomass monitoring. Y1 - 2024 UR - https://raw.githubusercontent.com/mlresearch/v235/main/assets/pauls24a/pauls24a.pdf VL - 235 SP - 39972 EP - 39988 ER -