Abstract
This work presents an approach to road network extraction in remote sensing images. In our earlier work, we worked on the extraction of the road network using a multi-agent approach guided by Volunteered Geographic Information (VGI). The limitation of this VGI-only approach is its inability to update the new road developments as it only follows the VGI. In this work, we employ a deep learning approach to update the road network to include new road developments not captured by the existing VGI. The output of the first stage is used to train a Convolutional Neural Network (CNN) in the second stage to generate a general model to classify road pixels. Post-processing is used to correct the undesired artifacts such as buildings, vegetation, occlusions, etc. to generate a final road map. Our proposed method is tested on the satellite images acquired over Abu Dhabi, United Arab Emirates and the aerial images acquired over Massachusetts, United States of America, and is observed to produce accurate results.
Highlights
In our earlier work, we worked on extraction of roads guided by Volunteered Geographic Information (VGI) [1]
The limitation of this VGI-only approach is its inability to update the new road developments which are not captured in VGI; In this paper, we introduce an approach based on Convolutional Neural Network (CNN) to extract the complete network by extracting segments that are not available in the VGI
Convolutional neural network (CNN) is a multi-level feed-forward artificial neural network belonging to the category of deep learning
Summary
We worked on extraction of roads guided by Volunteered Geographic Information (VGI) [1]. Our previous approach works based on the assumption that road segments are always connected with local geometrical homogeneity. The limitation of this VGI-only approach is its inability to update the new road developments which are not captured in VGI; In this paper, we introduce an approach based on Convolutional Neural Network (CNN) to extract the complete network by extracting segments that are not available in the VGI. The output of the first stage where VGI is used to extract the full extent of the road is employed as a labelled class input to train the CNN in the second stage. We develop a graph-theoretic approach as a post-processing step to enhance the accuracy of the extracted road network by connecting disjoint road segments. The fully connected layer has connections to all neurons of previous layers, with each connection having its own individual weight [15]
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