# Novel Approach in Vegetation Detection Using Multi-Scale Convolutional Neural Network
## 3 Results  
The performance of the proposed multi-scale convolutional neural network (MSCNN) was rigorously evaluated against state-of-the-art semantic segmentation architectures, specifically DeepLab V3+ [9], U-Net [10], and SegNet [11]. To ensure a comprehensive and fair comparison, all models, including the proposed MSCNN, were tested on three diverse and challenging datasets: CamVid, FloodNet, and RIT-18. To maintain experimental integrity and eliminate any potential bias from pre-training, all compared models were trained from scratch on these datasets. This approach ensures that each architecture was exposed to identical training data, allowing for a direct and unbiased comparison of their inherent capabilities in learning and generalizing from the given datasets. The first evaluation focuses on the CamVid dataset, which presents significant challenges for vegetation detection due to its complex urban scenes and varying illumination conditions. Visual inspection in Figure 3 reveals that while the other state-of-the-art methods demonstrate superior performance in detecting vegetation within the challenging images of the CamVid dataset, the proposed MSCNN achieves notably fine results when compared to the ground truth. The MSCNN's ability to leverage multi-scale feature extraction allows it to effectively capture both fine and coarse vegetation patterns, which is essential for accurate segmentation in diverse environments. The comparative analysis highlights the strengths and weaknesses of each architecture. For instance, DeepLab V3+ excels in handling complex backgrounds but may struggle with finer details due to its reliance on atrous convolutions. In contrast, U-Net's encoder-decoder structure provides robust segmentation capabilities, particularly in biomedical imaging, but may not generalize as effectively to varied landscapes like those found in the CamVid dataset. SegNet, while effective for semantic segmentation tasks, often requires extensive post-processing to refine its outputs. Despite these challenges faced by existing methods, the MSCNN architecture demonstrates a significant improvement in accurately identifying and segmenting vegetation. The results underscore the potential of the approach to enhance vegetation detection in real-world applications, contributing valuable insights for environmental monitoring and land management strategies. In the evaluations of the proposed MSCNN using the FloodNet dataset (see Figure 4), the MSCNN architecture demonstrated exceptional performance in accurately classifying and segmenting vegetation amidst complex post-flood environments. The model's ability to integrate multi-scale feature extraction allowed it to capture both fine details (such as individual trees) and broader patterns (like flooded areas). Visual inspections of the segmentation outputs in Figure 4 revealed that the MSCNN effectively delineated between flooded and non-flooded vegetation areas. The model successfully identified trees and grass in various conditions, highlighting its robustness in handling real-world challenges such as shadows and cluttered backgrounds. When compared to state-of-the-art architectures like DeepLab V3+, U-Net, and SegNet, the MSCNN showed superior results in terms of both accuracy and computational efficiency. While other models struggled with certain challenging images within the FloodNet dataset, the proposed architecture maintained high performance across diverse scenarios. The FloodNet dataset's inherent complexities such as distinguishing between natural water bodies and floodwaters were effectively managed by the MSCNN's multi-scale approach. This capability is critical for applications in disaster management where precise information is needed for rapid response and recovery efforts. In experimentations utilizing the RIT-18 dataset, the MSCNN architecture demonstrated remarkable performance in accurately segmenting and classifying vegetation types across diverse scenarios, as shown in Figure 5.  
Figure 3: Visual results using Camvid dataset.