Automated Method of Road Extraction from Aerial Images Using a Deep Convolutional Neural Network
Updating road networks using remote sensing imagery is among the most important topics in city planning, traffic management and disaster management. As a good alternative to manual methods, which are considered to be expensive and time consuming, deep learning techniques provide great improvements i...
Main Authors: | Tamara Alshaikhli, Wen Liu, Yoshihisa Maruyama |
---|---|
Format: | Article |
Language: | English |
Published: |
MDPI AG
2019-11-01
|
Series: | Applied Sciences |
Subjects: | |
Online Access: | https://www.mdpi.com/2076-3417/9/22/4825 |
Similar Items
-
Simultaneous Extraction of Road and Centerline from Aerial Images Using a Deep Convolutional Neural Network
by: Tamara Alshaikhli, et al.
Published: (2021-03-01) -
Road Extraction from High-Resolution Remote Sensing Imagery Using Refined Deep Residual Convolutional Neural Network
by: Lin Gao, et al.
Published: (2019-03-01) -
Genetic diversity assessment in sorghum accessions using qualitative morphological and amplified fragment length polymorphism markers
by: Abe Shegro Gerrano, et al.
Published: (2014-10-01) -
DSCImageCalc – Software for Determining Similarity Coefficients for the Analysis of Image Segmentations
by: Tom Lawton
Published: (2017-10-01) -
Road Extraction by Using Atrous Spatial Pyramid Pooling Integrated Encoder-Decoder Network and Structural Similarity Loss
by: Hao He, et al.
Published: (2019-04-01)