54 citations · 187 across the 12 of their papers we have counts for
12 papers
Reflections from the Workshop on AI-Assisted Decision Making for Conservation
Lily Xu, Esther Rolf, Sara Beery +21
In this white paper, we synthesize key points made during presentations and discussions from the AI-Assisted Decision Making for Conservation workshop, hosted by the Center for Res…
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…
Lightweight, Pre-trained Transformers for Remote Sensing Timeseries
Gabriel Tseng, Ruben Cartuyvels, Ivan Zvonkov +3
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-superv…
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…
Toward Foundation Models for Earth Monitoring: Proposal for a Climate Change Benchmark
Alexandre Lacoste, Evan David Sherwin, Hannah Kerner +9
Recent progress in self-supervision shows that pre-training large neural networks on vast amounts of unsupervised data can lead to impressive increases in generalisation for downst…