ai4science
posts tagged with "ai4science"
Neural Discovery in Mathematics: Do Machines Dream of Colored Planes?
May 29, 2025 · Our ICML 2025 Oral Paper Neural Discovery in Mathematics: Do Machines Dream of Colored Planes? introduces a novel neural network approach to tackle the famous Hadwiger-Nelson problem and related geometric coloring challenges. We reformulate the combinatorial task as a continuous optimization problem, enabling neural networks to find probabilistic colorings. This led to discovering two new 6-colorings, marking the first progress in 30 years on a key variant involving different forbidden distances and significantly expanding the known solution range.
Capturing Temporal Dynamics in Tree Canopy Height
May 1, 2025 · Our paper Capturing Temporal Dynamics in Large-Scale Canopy Tree Height Estimation was accepted to ICML 2025! In this work, we present a novel approach to generate large-scale, high-resolution canopy height maps over time. Using Sentinel-2 time series satellite data and GEDI LiDAR data as ground truth, we present the first 10m resolution temporal canopy height map of the European continent for the period 2019-2022. Our pipeline and the resulting temporal height map are publicly available, enabling comprehensive large-scale monitoring of forests.
Global-Scale Forest Height Estimation
Apr 13, 2025 · Our paper Estimating Canopy Height at Scale was accepted to ICML 2024! In this work, we present a novel framework for global-scale forest height estimation. Using a deep learning approach that leverages large amounts of satellite data with only sparsely distributed ground-truth height measurements from NASA's GEDI mission, we achieve state-of-the-art accuracy with MAE/RMSE of 2.43m/4.73m overall, significantly outperforming existing approaches. The resulting height map facilitates ecological analyses at a global scale.