# Semantic Segmentation of Aerial Imagery Using U-Net with Self-Attention and Separable Convolutions
## 4 Results and Discussions
### Model Evaluation Performance Metrics  
The model was trained on a labeled dataset of aerial imagery from Dubai, grouped into eight larger tiles with a total of 72 images. Each image was annotated with pixel-wise semantic segmentation in six classes: Building, Land, Road, Vegetation, Water, and Unlabeled. The dataset underwent preprocessing, including patch-based data augmentation and normalization. The number of images after augmentation was increased to 945. There were 803 training images, which makes 85 percent, and 142 were testing images. The predictions were performed on original training images and masked images. The trained model exhibited promising performance on both the training and validation sets, achieving competitive IoU scores and demonstrating its ability to segment different land features accurately. A visual inspection of the predicted segmentation masks on new images further  
Figure 6: Preprocessed images after being patched with patch size of 256 with no overlapping. confirmed the model's effectiveness in capturing complex spatial patterns in the aerial imagery of Dubai. Furthermore, the utilization of pixel-wise semantic segmentation enabled the model to precisely delineate boundaries between different land classes, contributing to its overall accuracy and reliability. This capability is crucial for tasks such as urban planning, environmental monitoring, and infrastructure development in Dubai and similar urban environments. The proposed SA-SC-U-Net outperforms the benchmark models, including FCN, U-Net, and Dense + U-Net, across various performance metrics. Notably, SA-SC-U-Net achieves the highest scores in accuracy (91.78%) and IoU (81.82%) can be seen in Table 1. The superior performance of the proposed model can be attributed to its unique combination of self-attention mechanisms and separable convolutions. The self-attention mechanism allows the model to focus on more relevant image regions, capturing long-range dependencies and enhancing feature extraction. Additionally, the use of separable convolutions reduces computational complexity while preserving model efficiency, enabling more effective learning of spatial hierarchies in the data. This combination results in improved segmentation accuracy and robustness, making SA-SC-U-Net a compelling choice for semantic segmentation tasks in aerial imagery compared to the traditional architectures like FCN and U-Net. Post completion of model training, predictions were made on new images using the SA-SC U-Net architecture. The model demonstrated its ability to segment objects effectively, providing pixel-wise predictions for each class--Building, Land, Road, Vegetation, Water, and Unlabeled. These predictions showcased the model's capacity to comprehend complex visual information and accurately assign semantic labels to various regions within the images. By visualizing the predicted masks alongside the original images, it becomes evident how well the SA-SC U-Net captures intricate patterns and nuances, making it a reliable tool for image segmentation tasks. These predictions serve as a testament to the model's practical utility and its potential application in diverse domains, from urban planning to environmental monitoring. The successful execution of predictions validates the model's learning capabilities and signifies its readiness for real-world deployment. The SA-SC-U-Net model stands out for its exceptional performance, achieving the highest accuracy (91.78%) and IoU (81.82%) scores compared to other benchmark models. This success is due to its unique combination of self-attention mechanisms and separable convolutions. The self-attention mechanism helps the model focus on important parts of the image, capturing distant relationships and improving feature extraction. At the same time, separable convolutions reduce the complexity of calculations while keeping the model efficient, allowing it to better understand the structure of the data.