51 citations · 78 across the 14 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…
U-TILISE: A Sequence-to-sequence Model for Cloud Removal in Optical Satellite Time Series
Corinne Stucker, Vivien Sainte Fare Garnot, Konrad Schindler
Satellite image time series in the optical and infrared spectrum suffer from frequent data gaps due to cloud cover, cloud shadows, and temporary sensor outages. It has been a long-…
UnCRtainTS: Uncertainty Quantification for Cloud Removal in Optical Satellite Time Series
Patrick Ebel, Vivien Sainte Fare Garnot, Michael Schmitt +2
Clouds and haze often occlude optical satellite images, hindering continuous, dense monitoring of the Earth's surface. Although modern deep learning methods can implicitly learn to…
Multi-Modal Temporal Attention Models for Crop Mapping from Satellite Time Series
Vivien Sainte Fare Garnot, Loic Landrieu, Nesrine Chehata
Optical and radar satellite time series are synergetic: optical images contain rich spectral information, while C-band radar captures useful geometrical information and is immune t…
Panoptic Segmentation of Satellite Image Time Series with Convolutional Temporal Attention Networks
Vivien Sainte Fare Garnot, Loic Landrieu
Unprecedented access to multi-temporal satellite imagery has opened new perspectives for a variety of Earth observation tasks. Among them, pixel-precise panoptic segmentation of ag…
Lightweight Temporal Self-Attention for Classifying Satellite Image Time Series
Vivien Sainte Fare Garnot, Loic Landrieu
The increasing accessibility and precision of Earth observation satellite data offers considerable opportunities for industrial and state actors alike. This calls however for effic…
Leveraging Class Hierarchies with Metric-Guided Prototype Learning
Vivien Sainte Fare Garnot, Loic Landrieu
In many classification tasks, the set of target classes can be organized into a hierarchy. This structure induces a semantic distance between classes, and can be summarised under t…
Satellite Image Time Series Classification with Pixel-Set Encoders and Temporal Self-Attention
Vivien Sainte Fare Garnot, Loic Landrieu, Sebastien Giordano +1
Satellite image time series, bolstered by their growing availability, are at the forefront of an extensive effort towards automated Earth monitoring by international institutions.…
Time-Space tradeoff in deep learning models for crop classification on satellite multi-spectral image time series
Vivien Sainte Fare Garnot, Loic Landrieu, Sebastien Giordano +1
In this article, we investigate several structured deep learning models for crop type classification on multi-spectral time series. In particular, our aim is to assess the respecti…