AUTOMATION OF ROAD FEATURE EXTRACTION FROM HIGH RESOLUTION IMAGES
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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. .
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£27.18
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