Automatic Extraction of Road Network from Aerial Images

2006 ◽  
Author(s):  
Xuemei Ding ◽  
Wenjing Kang ◽  
Jiwen Cui ◽  
Lei Ao
Keyword(s):  
Road Network ◽  
Aerial Images ◽  
Automatic Extraction

scholarly journals FPGA IMPLEMENTATION OF ROAD NETWORK EXTRACTION USING MORPHOLOGICAL OPERATOR

2016 ◽  
Vol 35 (2) ◽  
pp. 93 ◽  
Author(s):  
Sujatha Chinnathevar ◽  
Selvathi Dharmar
Keyword(s):  
Road Network ◽  
Hardware Implementation ◽  
Ground Truth ◽  
Aerial Images ◽  
Automatic Extraction ◽  
Average Value ◽  
Road Map ◽  
Road Network Extraction ◽  
Morphological Operator

In the remote sensing analysis, automatic extraction of road network from satellite or aerial images is the most needed approach for efficient road database creation, refinement, and updating. Mathematical morphology is a tool for extracting the features of an image that are useful in the representation and description of region shape. Morphological operator plays a significant role in the extraction of road network from satellite images. Most of the image processing algorithms need to handle large amounts of data, high repeatability, and general software is relatively slow to implement, so the system cannot achieve real-time requirements. In this paper, field programmable gate array (FPGA) architecture designed for automatic extraction of road centerline using morphological operator is proposed. Based on simulation and implementation, results are discussed in terms of register transfer level (RTL) design, FPGA editor and resource estimation. For synthesis and implementation of the above architecture, Spartan 3 XC3S400TQ144-4 device is used. The hardware implementation results are compared with software implementation results. The performance of proposed method is evaluated by comparing the results with ground truth road map as reference data and performance measures such as completeness, correctness and quality are calculated. In the software imple-mentation, the average value of completeness, correctness, and quality of various images are 90%, 96%, and 87% respectively. In the hardware implementation, the average value of completeness, correctness, and quality of various images are 87%, 94%, and 85% respectively. These measures prove that the proposed work yields road network very closer to reference road map.

scholarly journals AUTOMATIC EXTRACTION OF ROAD CENTERLINES AND EDGE LINES FROM AERIAL IMAGES VIA CNN-BASED REGRESSION

2020 ◽  
Vol V-2-2020 ◽  
pp. 925-932
Author(s):  
Y. Wei ◽  
X. Hu ◽  
M. Zhang ◽  
Y. Xu
Keyword(s):  
Road Network ◽  
Aerial Images ◽  
Automatic Extraction ◽  
Tensor Voting ◽  
Road Extraction ◽  
The Road ◽  
Road Width ◽  
Road Intersections ◽  
On The Road ◽  
Segmentation Problem

Abstract. Extracting roads from aerial images is a challenging task in the field of remote sensing. Most approaches formulate road extraction as a segmentation problem and use thinning and edge detection to obtain road centerlines and edge lines, which could produce spurs around the extracted centerlines/edge lines. In this study, a novel regression-based method is proposed to extract road centerlines and edge lines directly from aerial images. The method consists of three major steps. First, an end-to-end regression network based on CNN is trained to predict confidence maps for road centerlines and estimate road width. Then, after the CNN predicts the confidence map, non-maximum suppression and road tracking are applied to extract accurate road centerlines and construct road topology. Meanwhile, Road edge lines are generated based on the road width estimated by the CNN. Finally, in order to improve the connectivity of extracted road network, tensor voting is applied to detect road intersections and the detected intersections are used as guidance for the overcome of discontinuities. The experiments conducted on the SpaceNet and DeepGlobe datasets show that our approach achieves better performance than other methods.

scholarly journals Towards Automatic Extraction and Updating of VGI-Based Road Networks Using Deep Learning

2019 ◽  
Vol 11 (9) ◽  
pp. 1012 ◽  
Author(s):  
Prajowal Manandhar ◽  
Prashanth Reddy Marpu ◽  
Zeyar Aung ◽  
Farid Melgani
Keyword(s):  
Deep Learning ◽  
United Arab Emirates ◽  
Road Network ◽  
Aerial Images ◽  
Automatic Extraction ◽  
The Road ◽  
Second Stage ◽  
Road Map ◽  
Road Network Extraction ◽  
Multi Agent

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.

RESEARCH ON AUTOMATIC EXTRACTION OF DEFOCUS IMAGES IN UAV AERIAL IMAGES

2019 ◽  
Vol 75 (2) ◽  
pp. I_106-I_113
Author(s):  
Etsuji KITAGAWA ◽  
Ryo KATO ◽  
Satoshi ABIKO ◽  
Takumi TSUMURA ◽  
Yusuke NAKATANI
Keyword(s):  
Aerial Images ◽  
Automatic Extraction

scholarly journals With Geospatial in Path of Smart City

2015 ◽  
Vol XL-7/W3 ◽  
pp. 1381-1387 ◽  
Author(s):  
A. S. Homainejad
Keyword(s):  
Information And Communication Technologies ◽  
Smart City ◽  
Traffic Management ◽  
Road Network ◽  
Aerial Images ◽  
Transport Systems ◽  
City Management ◽  
Traffic Jam ◽  
The Road ◽  
On The Road

With growth of urbanisation, there is a requirement for using the leverage of smart city in city management. The core of smart city is Information and Communication Technologies (ICT), and one of its elements is smart transport which includes sustainable transport and Intelligent Transport Systems (ITS). Cities and especially megacities are facing urgent transport challenge in traffic management. Geospatial can provide reliable tools for monitoring and coordinating traffic. In this paper a method for monitoring and managing the ongoing traffic in roads using aerial images and CCTV will be addressed. In this method, the road network was initially extracted and geo-referenced and captured in a 3D model. The aim is to detect and geo-referenced any vehicles on the road from images in order to assess the density and the volume of vehicles on the roads. If a traffic jam was recognised from the images, an alternative route would be suggested for easing the traffic jam. In a separate test, a road network was replicated in the computer and a simulated traffic was implemented in order to assess the traffic management during a pick time using this method.

scholarly journals A NEW APPROACH FOR URBAN ROADS DETECTION USING LASER DATA AND AERIAL DIGITAL IMAGES

2016 ◽  
Vol 10 (1) ◽  
pp. 83-97 ◽  
Author(s):  
Francisco Assis Da Silva ◽  
Anderson Akio Gohara ◽  
Mário Augusto Pazoti ◽  
Danillo Roberto Pereira ◽  
Almir Olivette Artero ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Digital Image ◽  
Aerial Images ◽  
Automatic Extraction ◽  
New Approach ◽  
Additional Information ◽  
Automatic Feature Extraction ◽  
Laser Data ◽  
Sensitivity Rate ◽  
Road Segments

The automatic feature extraction from digital aerial images is not a trivial task mainly due to occlusion problems, shadows and different viewpoints. To obtain an improved feature extraction we used laser data, which have additional information such as height and material type of the surface. In this paper we performed the combination of digital image and laser data in order to improve the results of automatic extraction of urban roads. Initially, the urban roads were detected from the response of laser information; in the sequence we applied two different approaches to connect the disconnected road segments. The results were very promising, with sensitivity rate of 92%.

scholarly journals Comparison of different methods to assess the distribution of alien plants along the road network and use of Google Street View panoramas interpretation in Sicily (Italy) as a case study

2021 ◽  
Vol 9 ◽  
Author(s):  
Giulio Barone ◽  
Gianniantonio Domina ◽  
Emilio Di Gristina
Keyword(s):  
Road Network ◽  
Google Earth ◽  
Alien Plants ◽  
Aerial Images ◽  
Photo Interpretation ◽  
Aerial Photos ◽  
Google Street View ◽  
The Road ◽  
Street View ◽  
Complete Survey

The survey by foot in the field is compared to the survey from a car, the photo-interpretation of Google Street View (GSV) panoramas continuously and at intervals of 1.5 km and the photo-interpretation of Google Earth aerial images on a 10 km stretch of road in Sicily. The survey by foot was used as reference for the other methods. The interpretation of continuous GSV panoramas gave similar results as the assessment by car in terms of the number of species identified and their location, but with lower cost. The interpretation online of aerial photos allowed the identification of a limited number of taxa, but gave a good localisation for them. Interpretation of GSV panoramas, each of 1.5 km, allowed the recognition of twice as many taxa as the interpretation of aerial photos and taking half the time, but did not allow a complete localisation. None of these methods alone seems sufficient to carry out a complete survey. A mixture of different techniques, which may vary according to the available resources and the goal to be achieved, seems to be the best compromise. To further test the capabilities of the survey using the interpretation of GSV panoramas every 1.5 km along the roads, we proceeded to study the alien plants along 3500 km of the road network on the island of Sicily. This survey identified only 10% of the known species for the region, but allowed us to trace the distribution of invasive species whose distribution is currently poorly recorded.

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