6 papers
From Machine Learning to Large-Scale EO Products: Best Practices for Making Maps
Ghjulia Sialelli, Robin Young, Yuchang Jiang +9
Recent years have seen a rapid expansion in the production of large-scale geospatial maps derived from Earth observation (EO) data, driven largely by advances in machine learning (…
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…
Climplicit: Climatic Implicit Embeddings for Global Ecological Tasks
Johannes Dollinger, Damien Robert, Elena Plekhanova +2
Deep learning on climatic data holds potential for macroecological applications. However, its adoption remains limited among scientists outside the deep learning community due to s…
SSL4Eco: A Global Seasonal Dataset for Geospatial Foundation Models in Ecology
Elena Plekhanova, Damien Robert, Johannes Dollinger +4
With the exacerbation of the biodiversity and climate crises, macroecological pursuits such as global biodiversity mapping become more urgent. Remote sensing offers a wealth of Ear…
Lossy Neural Compression for Geospatial Analytics: A Review
Carlos Gomes, Isabelle Wittmann, Damien Robert +24
Over the past decades, there has been an explosion in the amount of available Earth Observation (EO) data. The unprecedented coverage of the Earth's surface and atmosphere by satel…
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…