A critical analysis of road network extraction using remote sensing images with deep learning

Sharma, Palvi; Kumar, Rakesh; Gupta, Meenu; Nayyar, Anand

doi:10.1007/s41324-024-00576-y

Cited by 7 publications

References 36 publications

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An Enhanced Loss Function for Semantic Road Segmentation in Remote Sensing Images

Nanni,

Brahnam,

Loreggia

2024

IEEE Access

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The analysis of road continuity in satellite images is a complex challenge. This is due to the difficulty in identifying the directional vector of road sections, especially when the satellite view of roads is obstructed by trees or other structures. Today, most research focuses on optimizing the deep learning network topology, however, the accuracy of segmentation is affected by the loss function used in training; currently, little research has been published on ad-hoc loss functions for road segmentation.To solve this problem, we proposed loss functions based on topological pixel analysis, in which more weight is given to problematic pixels representing non-real road breaks. We report the results of different tests, obtaining state-of-the-art performance among convolution neural network based approaches. Both the code and information for replicating our experiments are available on https://github.com/LorisNanni/An-Enhanced-Loss-Function-for-Semantic-Road-Segmentation-in-Remote-Sensing-Images , so as to enable future reliable comparisons.

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