How accurate are existing land cover maps for agriculture in Sub-Saharan Africa?

1 code implementation • 5 Jul 2023 • Hannah Kerner, Catherine Nakalembe, Adam Yang, Ivan Zvonkov, Ryan McWeeny, Gabriel Tseng, Inbal Becker-Reshef

Satellite Earth observations (EO) can provide affordable and timely information for assessing crop conditions and food production.

Lightweight, Pre-trained Transformers for Remote Sensing Timeseries

1 code implementation • 27 Apr 2023 • Gabriel Tseng, Ruben Cartuyvels, Ivan Zvonkov, Mirali Purohit, David Rolnick, Hannah Kerner

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.

Crop Classification Self-Supervised Learning +1

TIML: Task-Informed Meta-Learning for Agriculture

1 code implementation • 4 Feb 2022 • Gabriel Tseng, Hannah Kerner, David Rolnick

When developing algorithms for data-sparse regions, a natural approach is to use transfer learning from data-rich regions.

Meta-Learning Transfer Learning

Rapid Response Crop Maps in Data Sparse Regions

2 code implementations • 23 Jun 2020 • Hannah Kerner, Gabriel Tseng, Inbal Becker-Reshef, Catherine Nakalembe, Brian Barker, Blake Munshell, Madhava Paliyam, Mehdi Hosseini

A major challenge for developing crop maps is that many regions do not have readily accessible ground truth data on croplands necessary for training and validating predictive models, and field campaigns are not feasible for collecting labels for rapid response.

Humanitarian