Satellite imagery for greenhouse mapping in Morocco using U-net model

Abstract

Greenhouse agriculture has become a crucial element of agricultural practices in Morocco, yet its spatial and temporal evolution remain insufficiently quantified. This study aims to map greenhouse structures at the Souss-Massa region scale in order to assess the progress of covered agriculture and examine its relationship with socio-economic development in Morocco. Using hand-annotated greenhouse data from the Chtouka region as ground truth, we develop a deep learning–based detection framework relying exclusively on open-source tools. Multispectral Sentinel-2 satellite imagery at 10 m spatial resolution is used as input to a U-Net convolutional neural network, which is trained, validated, and tested for greenhouse segmentation. The proposed model achieves an overall accuracy of up to 94%, demonstrating strong generalization capability. The resulting plug-and-play methodology enables scalable, cost-effective, and open-source greenhouse mapping, and provides valuable insights into the dynamics of covered agriculture and its role in Morocco’s agricultural and socio-economic development.