54 citations · 187 across the 12 of their papers we have counts for
4 papers · 1 filter
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…
Integrating Machine Learning for Planetary Science: Perspectives for the Next Decade
Abigail R. Azari, John B. Biersteker, Ryan M. Dewey +13
Machine learning (ML) methods can expand our ability to construct, and draw insight from large datasets. Despite the increasing volume of planetary observations, our field has seen…
Rapid Response Crop Maps in Data Sparse Regions
Hannah Kerner, Gabriel Tseng, Inbal Becker-Reshef +5
Spatial information on cropland distribution, often called cropland or crop maps, are critical inputs for a wide range of agriculture and food security analyses and decisions. Howe…
Field-Level Crop Type Classification with k Nearest Neighbors: A Baseline for a New Kenya Smallholder Dataset
Hannah Kerner, Catherine Nakalembe, Inbal Becker-Reshef
Accurate crop type maps provide critical information for ensuring food security, yet there has been limited research on crop type classification for smallholder agriculture, partic…