Name
Title | ||
|---|---|---|
Towards Automatic Extraction and Updating of VGI-Based Road Networks Using Deep Learning |
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. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.3390/rs11091012 | REMOTE SENSING |
Keywords | Field | DocType |
road extraction,road updating,VGI,CNN,segment connection | Computer vision,Road networks,Volunteered geographic information,Artificial intelligence,Deep learning,Geology,Machine learning | Journal |
Volume | Issue | Citations |
11 | 9 | 0 |
PageRank | References | Authors |
0.34 | 0 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Prajowal Manandhar | 1 | 0 | 1.01 |
| Prashanth Reddy Marpu | 2 | 260 | 15.00 |
| Zeyar Aung | 3 | 199 | 25.87 |
| Farid Melgani | 4 | 1100 | 80.98 |