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Found 12 papers, 6 papers with code
Bridging Remote Sensors with Multisensor Geospatial Foundation Models
A key discovery of our research is that representations derived from natural images are not always compatible with the distinct characteristics of geospatial remote sensors, underscoring the limitations of existing representations in this field.
Causal hybrid modeling with double machine learning
Hybrid modeling integrates machine learning with scientific knowledge to enhance interpretability, generalization, and adherence to natural laws.
Multi-modal learning for geospatial vegetation forecasting
Our study breaks new ground by introducing GreenEarthNet, the first dataset specifically designed for high-resolution vegetation forecasting, and Contextformer, a novel deep learning approach for predicting vegetation greenness from Sentinel 2 satellite images with fine resolution across Europe.
Learning to forecast vegetation greenness at fine resolution over Africa with ConvLSTMs
Forecasting the state of vegetation in response to climate and weather events is a major challenge.
On the Generalization of Agricultural Drought Classification from Climate Data
Climate change is expected to increase the likelihood of drought events, with severe implications for food security.
EarthNet2021: A large-scale dataset and challenge for Earth surface forecasting as a guided video prediction task
We frame Earth surface forecasting as the task of predicting satellite imagery conditioned on future weather.
Ranked #5 on
Earth Surface Forecasting
on EarthNet2021 OOD Track
EarthNet2021: A novel large-scale dataset and challenge for forecasting localized climate impacts
Here, we define high-resolution Earth surface forecasting as video prediction of satellite imagery conditional on mesoscale weather forecasts.
A Methodology to Derive Global Maps of Leaf Traits Using Remote Sensing and Climate Data
The processing chain exploits machine learning techniques along with optical remote sensing data (MODIS/Landsat) and climate data for gap filling and up-scaling of in-situ measured leaf traits.
Applications Applied Physics
A Perspective on Gaussian Processes for Earth Observation
Earth observation (EO) by airborne and satellite remote sensing and in-situ observations play a fundamental role in monitoring our planet.
Predicting Landscapes from Environmental Conditions Using Generative Networks
We demonstrate that for many purposes the generated landscapes behave as real with immediate application for global change studies.
The FLUXCOM ensemble of global land-atmosphere energy fluxes
Here we use machine learning to merge energy flux measurements from FLUXNET eddy covariance towers with remote sensing and meteorological data to estimate net radiation, latent and sensible heat and their uncertainties.
Maximally Divergent Intervals for Anomaly Detection
We present new methods for batch anomaly detection in multivariate time series.
