Final results comparison of SimCLR-S2 models versus the supervised baseline.

An interactive data vizualization of performance results for the distilled models.

In the final set of the SimCLR-S2 process we perform knowledge distillation to attempt to gain similar performance to the big ResNet152 model with fewer parameters. While it is possible to use some labeled data during distillation we experimented with a completely self-supervised approach.

We leverage a similar approach as the baseline models to fine tune our pretrained contrastive learning model. We train multipe data splits on our largest CNN architecture ResNet152, to maximize performance of the fine-tuned model.

An exploratory analysis on the data to understand the distributions for irrigated data as well as identify additional features that can help with irrigation detection.

The data for our research are Sentinel-2 multispectral images of earth’s surface reflectance sourced from two archives: BigEarthNet-S2 and Sentinel-2A collection from the Copernicus program.