5 citations · 6 across the 8 of their papers we have counts for
9 papers
SAGE: A sampling-aware global evaluation benchmark for species distribution modeling
Emilia Arens, Nina van Tiel, Robin Zbinden +8
Knowing where species occur is fundamental for biodiversity research and conservation. Species distribution models (SDMs) link species observations to environmental conditions to e…
Toward a foundation model for forest point clouds
Yuanwen Yue, Stefano Puliti, Damien Robert +7
Forest inventories increasingly rely on artificial intelligence (AI) models to derive forest attributes from large-scale 3D point clouds. Current models are typically specialized t…
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…
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…
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…
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…
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…
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…