17 citations · 50 across the 6 of their papers we have counts for
6 papers · 1 filter
How accurate are existing land cover maps for agriculture in Sub-Saharan Africa?
Hannah Kerner, Catherine Nakalembe, Adam Yang +4
Satellite Earth observations (EO) can provide affordable and timely information for assessing crop conditions and food production. Such monitoring systems are essential in Africa,…
GEO-Bench: Toward Foundation Models for Earth Monitoring
Alexandre Lacoste, Nils Lehmann, Pau Rodriguez +14
Recent progress in self-supervision has shown that pre-training large neural networks on vast amounts of unsupervised data can lead to substantial increases in generalization to do…
TIML: Task-Informed Meta-Learning for Agriculture
Gabriel Tseng, Hannah Kerner, David Rolnick
Labeled datasets for agriculture are extremely spatially imbalanced. When developing algorithms for data-sparse regions, a natural approach is to use transfer learning from data-ri…
Using transfer learning to study burned area dynamics: A case study of refugee settlements in West Nile, Northern Uganda
Robert Huppertz, Catherine Nakalembe, Hannah Kerner +2
With the global refugee crisis at a historic high, there is a growing need to assess the impact of refugee settlements on their hosting countries and surrounding environments. Beca…
Integrating Novelty Detection Capabilities with MSL Mastcam Operations to Enhance Data Analysis
Paul Horton, Hannah R. Kerner, Samantha Jacob +3
While innovations in scientific instrumentation have pushed the boundaries of Mars rover mission capabilities, the increase in data complexity has pressured Mars Science Laboratory…
Resilient In-Season Crop Type Classification in Multispectral Satellite Observations using Growth Stage Normalization
Hannah Kerner, Ritvik Sahajpal, Sergii Skakun +5
Crop type classification using satellite observations is an important tool for providing insights about planted area and enabling estimates of crop condition and yield, especially…