51 citations · 64 across the 11 of their papers we have counts for
14 papers
MIRANDA: MId-feature RANk-adversarial Domain Adaptation toward climate change-robust ecological forecasting with deep learning
Yuchang Jiang, Jan Dirk Wegner, Vivien Sainte Fare Garnot
Plant phenology modelling aims to predict the timing of seasonal phases, such as leaf-out or flowering, from meteorological time series. Reliable predictions are crucial for antici…
Tree crop mapping of South America reveals links to deforestation and conservation
Yuchang Jiang, Anton Raichuk, Xiaoye Tong +6
Monitoring tree crop expansion is vital for zero-deforestation policies like the European Union's Regulation on Deforestation-free Products (EUDR). However, these efforts are hinde…
GSR4B: Biomass Map Super-Resolution with Sentinel-1/2 Guidance
Kaan Karaman, Yuchang Jiang, Damien Robert +3
Accurate Above-Ground Biomass (AGB) mapping at both large scale and high spatio-temporal resolution is essential for applications ranging from climate modeling to biodiversity asse…
Deep learning meets tree phenology modeling: PhenoFormer vs. process-based models
Vivien Sainte Fare Garnot, Lynsay Spafford, Jelle Lever +5
Phenology, the timing of cyclical plant life events such as leaf emergence and coloration, is crucial in the bio-climatic system. Climate change drives shifts in these phenological…
Accuracy and Consistency of Space-based Vegetation Height Maps for Forest Dynamics in Alpine Terrain
Yuchang Jiang, Marius Rüetschi, Vivien Sainte Fare Garnot +4
Monitoring and understanding forest dynamics is essential for environmental conservation and management. This is why the Swiss National Forest Inventory (NFI) provides countrywide…
Uncertainty Voting Ensemble for Imbalanced Deep Regression
Yuchang Jiang, Vivien Sainte Fare Garnot, Konrad Schindler +1
Data imbalance is ubiquitous when applying machine learning to real-world problems, particularly regression problems. If training data are imbalanced, the learning is dominated by…