Andere Kunden interessierten sich auch für
![Artificial neural networks based automatic feature extraction]( "Artificial neural networks based automatic feature extraction")
Artificial neural networks based automatic feature extraction36,99 €![Super Resolution Mapping of Trees from Satellite Images]( "Super Resolution Mapping of Trees from Satellite Images")
Super Resolution Mapping of Trees from Satellite Images23,99 €![Feature Papers of Drones]( "Feature Papers of Drones")
Feature Papers of Drones126,99 €![Feature Papers of Drones]( "Feature Papers of Drones")
Feature Papers of Drones152,99 €![Image Processing Algorithms for Automated Cadastral Feature Extraction]( "Image Processing Algorithms for Automated Cadastral Feature Extraction")
Image Processing Algorithms for Automated Cadastral Feature Extraction24,99 €![Deep learning in Remote sensing]( "Deep learning in Remote sensing")
Deep learning in Remote sensing29,99 €![Integrated high-resolution geophysical investigation of a monastery]( "Integrated high-resolution geophysical investigation of a monastery")
Integrated high-resolution geophysical investigation of a monastery13,99 €
Road feature detection from remotely sensed images is crucial for maintaining an up-to-date and reliable road network, essential for transportation, emergency planning, and navigation. While convolutional neural networks have shown promise in automating this process, existing methods often trade off accuracy for complexity. This study aims to develop an accurate road extraction method without sacrificing computational efficiency. We propose a semantic segmentation neural network combining transfer learning and U-net architecture with minimal complexity. Post-processing techniques are employed to enhance output quality. Our method achieves an F1 score of 0.83 and 95.57% accuracy, outperforming other models on the Massachusetts dataset. This approach demonstrates superior performance and reduced network complexity compared to existing methods.