Toward Sustainable Agriculture: DPA-UNet for Semantic Segmentation of Landscapes Using Remote Sensing Imagery

Particularly in agricultural contexts where exact delineation of land cover types is vital for resource management and planning, semantic segmentation is a key technique for high-resolution image interpretation. Although U-Net-based designs have shown significant success in Land Use and Land Cover (LULC) identification, they sometimes struggle when segmenting classes with comparable spectral, textural, and intensity traits. Moreover, generating large-scale ground truth across wide agricultural areas is still time consuming and labor intensive, which greatly limits the creation of scalable segmentation solutions. This work presents a new architecture called Deep Pro Agri-UNet (DPA-UNet) meant specifically for agricultural field segmentation with high-resolution images sourced from Google Earth Pro in order to solve these problems. DPA-UNet improves the model’s ability to extract discriminative features and properly separate spectrally similar land classes by means of a multi-branch spatial and channel attention mechanism at the model’s bottleneck layer. A Dice loss function is included into the training goal to offset the consequences of class imbalance common in agricultural datasets. Attention gates are also included into the decoder path to selectively hone feature maps from the encoder to emphasize spatially relevant areas during the upsampling process. Experimental results show that DPA-UNet significantly outperforms traditional U-Net models, with an 81.8% total accuracy and an 82.8% Intersection over Union (IoU). While keeping a lower computational load, the proposed model efficiently lowers segmentation mistakes in heterogeneous agricultural areas. The findings confirm that DPA-UNet provides a scalable, precise, and computationally efficient solution for large scale agricultural monitoring, therefore supporting applications in sustainable land management, informed policy decision making, and precision farming.