Learning Visual Patterns in Remote Sensing: An Overview of Agricultural Applications

Deep Learning based on Remote Sensing has become a powerful tool to increase agricultural productivity, mitigate the effects of climate change, and monitor deforestation. However, there is a lack of standardization and appropriate taxonomic classification of the literature available in the context of informatics. Against this background, this survey provides an overview of the relevant literature categorized into five main applications: Parcel Segmentation, Crop Mapping, Crop Yielding, Land Use and Land Cover, and Change Detection. We address notable trends, including transitioning from traditional to deep learning, convolutional models, recurrent and attention-based models, generative strategies, and self-supervised pre-training. The supplementary material also includes a comprehensive review of publicly available datasets for these applications. We hope that our work can be useful as a guide for future work in this context.