# Novel Approach in Vegetation Detection Using Multi-Scale Convolutional Neural Network
## 3 Results
(**a**) Input image, (**b**) ground truth, (**c**) proposed MSCNN result, (**d**) DeepLab V3+ result, (**e**) U-Net result, and (**f**) SegNet result. Visual inspections of the segmentation outputs revealed that the MSCNN effectively delineated different vegetation types, including herbaceous plants, shrubs, and trees. The model's ability to leverage multi-scale feature extraction allowed it to capture both fine details (such as individual leaves) and broader patterns (like entire plant communities). When compared to state-of-the-art architectures such as DeepLab V3+, U-Net, and SegNet, the MSCNN consistently outperformed these models in terms of both accuracy and robustness. The integration of multi-scale features enabled the proposed model to maintain high performance even in challenging conditions characterized by occlusion and varying illumination. Table 2 shows the evaluation metrics for the proposed MSCNN compared to the state-of-the-art methods. This table summarizes the evaluation results across three distinct datasets: CamVid, FloodNet, and RIT-18. The table highlights key metrics that reflect the effectiveness of the MSCNN in vegetation segmentation tasks. It can be seen in this table that the CamVid dataset achieved a global accuracy of 98.4%, demonstrating exceptional  
Figure 4: Visual results using FloodNet dataset. (**a**) Input image, (**b**) ground truth, (**c**) proposed MSCNN result, (**d**) DeepLab V3+ result, (**e**) U-Net result, and (**f**) SegNet result. Figure 5: Visual results using RIT-18 dataset. (**a**) Input RGB image, (**b**) ground truth, (**c**) proposed MSCNN result, (**d**) DeepLab V3+ result, (**e**) U-Net result, and (**f**) SegNet result. performance in urban environments. The FloodNet dataset recorded a global accuracy of 97.3%, effectively handling post-flood scenarios where vegetation detection is critical. The RIT-18 dataset attained a global accuracy of 96.9%, showcasing strong capabilities in multispectral imagery analysis. The mean accuracy metric assesses the average accuracy across all classes, providing insight into how well the model performs on less frequent classes. In the CamVid dataset, the mean accuracy is 98.1%, indicating consistent performance across various vegetation types. The FloodNet dataset's mean accuracy is 96.3%, reflecting reliable segmentation even amid challenging conditions. The RIT-18 dataset's mean accuracy is 96.8%, highlighting effective classification across diverse vegetation species. The mean intersection over union (IoU) metric measures the overlap between predicted segmentation and ground truth, providing a more nuanced view of segmentation quality. The CamVid dataset recorded a mean IoU of 95.5%, suggesting high-quality segmentation with minimal misclassification. The FloodNet dataset had a mean IoU of 94.1%, indicating strong performance in distinguishing between flooded and non-flooded areas. The RIT-18 dataset had a mean IoU of 93.9%, demonstrating effective differentiation among various vegetation types. The weighted IoU metric accounts for class imbalances by weighting each class's IoU by its frequency in the dataset. The CamVid dataset recorded a weighted IoU of 97.6%, reflecting robust performance across frequently occurring classes. The FloodNet dataset had a weighted IoU of 96.7%, showcasing effectiveness in accurately segmenting both common and rare classes. The RIT-18 dataset showed a weighted IoU of 93.9%, indicating balanced performance despite class imbalances. Finally, the mean boundary F-score (BF score) evaluates the model's ability to accurately delineate boundaries between different classes, which is particularly important for segmentation tasks. The CamVid dataset recorded a mean BF score of 95.2%, highlighting the model's precision in boundary detection within urban landscapes. The FloodNet dataset attained a mean BF score of 95.4%, demonstrating effectiveness in accurately classifying vegetation amidst complex backgrounds.