Asymm-Unet: A Concise Model for Segmentation of Satellite Imagery for Crop Mapping

Accurate mapping of crop production is vital for food security, resource management, and sustainable agriculture. Deep learning models, especially CNNs, have shown promise in an-alyzing high-resolution satellite imagery to identify the types of crops being grown in different regions. Crop mapping is essentially a task of satellite image segmentation, for which U-Net is a highly successful family. However, deep CNNs involve more parameters and hence require heavier computations. To address this, we propose integrating the Asymmetric Convolution technique into the UNet architecture. We curated a dataset for crop production mapping using USGS Landsat 8 data and USDA NASS Cropland Data Layer. By incorporating Asymmetric Convolution into UNet, we found improved accuracy, and efficiency. Our proposed AsymmUNet model demonstrated superior performance compared to existing state-of-the-art models, such as U-Net, UNet++, and HRNet, with reduced computational complexity. Hence this approach furthers the idea of GreenAI and Sustainable Computing while solving a task that is relevant to the Sustainable Development Goals.