Zindi.Africa and NASA Harvest have organized a machine learning challenge to design algorithms to classify crop field boundaries using multispectral observations. Small farms in low and middle-income countries produce about 35% of the world’s food, and mapping these farms can help policy-makers allocate resources and monitor the impacts on food production and security. The challenge provides a classic Satellite Image Time Series problem that includes six months of satellite imagery and labels tiled into 256 by 256 chips totaling up to 70 tiles. A Unet-based architecture using a pretrained Efficientnet-B7 as the encoder is used to train the model for 50 epochs with an AdamW optimizer and learning rate of 1e-4. An ablation study was conducted to explore various approaches to the image preprocessing, model architectures, loss functions, optimizers, and metrics. The code for this study is available in the reference list.