The increasing frequency and severity of extreme climatic events and their impacts are
being realized in many regions of the world, particularly in smallholder crop and livestock …

Machine learning methods for satellite data have a range of societally relevant applications,
but labels used to train models can be difficult or impossible to acquire. Self-supervision is a …

Remote sensing datasets pose a number of interesting challenges to machine learning
researchers and practitioners, from domain shift (spatially, semantically and temporally) to …

The desired output for most real-world tasks using machine learning (ML) and remote
sensing data is a set of dense predictions that form a predicted map for a geographic region …

Ground-truth labels on crop type and other variables are critically needed to develop
machine learning methods that use satellite observations to combat climate change and …

In most developing countries, smallholder farms are the ultimate source of income and
produce a significant portion of overall crop production for the major crops. Accurate crop …

Agriculture in sub-Saharan Africa consists primarily of smallholder farms of rainfed crops.
Historically, satellite data were too coarse to account for the heterogeneity in these …

The desired output for most real-world tasks using machine learning (ML) and remote
sensing data is a set of dense predictions that form a predicted map for a geographic region …

The increasing frequency and severity of extreme climatic events and their impacts are
being realized in many regions of the world, particularly in smallholder crop and livestock …