Extracting roads from satellite maps with the help of machine learning

Document Type : Research Paper

Author

Basrah University For Oil and Gas, Iraq

10.22075/ijnaa.2023.29941.4297

Abstract

Remote sensing’s biggest challenge is identifying roads in VHR satellite images. This project aimed to develop a model for classifying map roads as roads or non-roads. Hand-sort satellite images. Because human error is so common, separation should be done automatically. Transfer learning and a convolutional neural network built the model. The authors classified pictures using three transfer-learning models. AlexNet, VGG-16, Resnet50, and DenseNet-121. CNN’s module has a 95.3% accuracy rate, VGG-16’s 97.6%, Resnet50’s 96.5%, and Alex’s Net’s 84.6%. Because each spatial
feature point can now refer to additional contextual data, road segmentation is more accurate. In the official Deep Learning Challenge, our unprocessed models outperformed every published state-of-the-art ensemble model. The authors also present empirical evaluations of how to use non-local blocks in the base model. The methodology used transfer learning and a large dataset to build an unrevealed best alternative identification procedure. The architecture uses data augmentation, therefore custom data extraction is unnecessary.

Keywords


Articles in Press, Accepted Manuscript
Available Online from 23 March 2023
  • Receive Date: 09 January 2023
  • Revise Date: 16 February 2023
  • Accept Date: 11 March 2023