TIML: Task-Informed Meta-Learning for crop type mapping

Gabriel Tseng, Hannah Kerner, David Rolnick

Keywords: meta-learning, crop mapping, remote sensing, few-short learning, agriculture, land cover mapping

TL;DR: Task-Informed Meta-Learning (TIML) - conditioning meta learning models on task-specific metadata

Abstract: Labeled datasets for agriculture are extremely spatially imbalanced. When developing algorithms for data-sparse regions, a previously explored approach is to use transfer learning from data-rich regions. While standard transfer learning approaches typically leverage only direct inputs and outputs, geospatial imagery and agricultural data is rich in metadata that can inform transfer learning algorithms, such as the spatial coordinates of data-points. We build on previous work exploring use of meta-learning to crop type mapping in data-sparse regions and introduce task-informed meta-learning (TIML), an augmentation to model-agnostic meta-learning which takes advantage of this metadata. We apply TIML to the CropHarvest dataset, a global dataset of agricultural class labels paired with remote sensing data. In addition, we introduce the concept of forgetfulness when training meta-learning models on many similar tasks to mitigate memorization of training tasks. We find that TIML significantly improves average performance across the CropHarvest evaluation tasks compared to a range of benchmark models, measured using AUC ROC and F1 scores.