Detection of Railway Tracks from Satellite images using U-Net Architecture

Railway track detection plays a vital role in ensuring the safety and efficiency of railway systems. Traditional methods for track detection often rely on manual inspection, which is time-consuming and prone to human error. In recent years, the availability of high-resolution satellite imagery and advancements in deep learning techniques have opened up new possibilities for automating this process. This research provides a method for the detection of tracks using the U-Net model, a convolutional neural network architecture developed for image segmentation applications. By leveraging the U-Net architecture, the proposed approach effectively captures both local and global contextual information to accurately identify railway tracks in satellite images. The methodology involves training the U-Net model using a large dataset of annotated satellite images. During the training process, the model learns to segment and distinguish railway tracks from the background. Extensive experiments are conducted to evaluate the performance of the proposed method on diverse satellite images containing railway tracks. The results demonstrate the effectiveness and robustness of the U-Net model for detecting railway tracks, showcasing its potential for enhancing railway safety and maintenance through automated track detection systems.. With high accuracy, the proposed model is very useful for detecting the railway tracks from the satellite images accordingly.