Road Extraction Techniques From Remote Sensing Images: A Review

Kahraman, I.; Karaş, İsmail Rakıp; Akay, Abdullah E.

doi:10.5194/isprs-archives-xlii-4-w9-339-2018

Cited by 15 publications

(4 citation statements)

References 12 publications

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“…For road extraction, various classification-based, knowledge-based, mathematical morphology and dynamic programming approaches have been explored [26]. Previous works using CNN on road extraction mostly tend to detect road pixels or patches, and then uses complex post-processing heuristics to infer graph connectivity.…”

Section: Background and Related Workmentioning

confidence: 99%

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

et al. 2019

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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.

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Section: Background and Related Workmentioning

confidence: 99%

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

et al. 2019

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“…Road extraction methods can be divided into two categories: traditional methods [12] and methods developed based on various deep learning frameworks [13]. Traditional methods heavily rely on manually designed features, thereby making it challenging to accurately identify road shape features in images [14][15][16]. Moreover, early manual extraction methods required significant time and human resources and were prone to subjective interference [17,18].…”

Section: Introductionmentioning

confidence: 99%

Global–Local Information Fusion Network for Road Extraction: Bridging the Gap in Accurate Road Segmentation in China

Wang,

Cai,

et al. 2023

Remote Sensing

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Road extraction is crucial in urban planning, rescue operations, and military applications. Compared to traditional methods, using deep learning for road extraction from remote sensing images has demonstrated unique advantages. However, previous convolutional neural networks (CNN)-based road extraction methods have had limited receptivity and failed to effectively capture long-distance road features. On the other hand, transformer-based methods have good global information-capturing capabilities, but face challenges in extracting road edge information. Additionally, existing excellent road extraction methods lack validation for the Chinese region. To address these issues, this paper proposes a novel road extraction model called the global–local information fusion network (GLNet). In this model, the global information extraction (GIE) module effectively integrates global contextual relationships, the local information extraction (LIE) module accurately captures road edge information, and the information fusion (IF) module combines the output features from both global and local branches to generate the final extraction results. Further, a series of experiments on two different Chinese road datasets with geographic robustness demonstrate that our model outperforms the state-of-the-art deep learning models for road extraction tasks in China. On the CHN6-CUG dataset, the overall accuracy (OA) and intersection over union (IoU) reach 97.49% and 63.27%, respectively, while on the RDCME dataset, OA and IoU reach 98.73% and 84.97%, respectively. These research results hold significant implications for road traffic, humanitarian rescue, and environmental monitoring, particularly in the context of the Chinese region.

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“…Finding an efficient way to extract road networks automatically or semi-automatically is an important topic that has been discussed in many studies [6] [7] [8] [9] [10], in which different methods and algorithms have been used. Most studies agree that extracting roads from aerial images is a complicated task due to occlusion, shadows, and trees, as well as the different types of roads that appear in aerial images, and these conditions make it difficult to accurately extract roads [11] [12] [13] [14].…”

Section: Introductionmentioning

confidence: 99%

Remote Sensing Applied to the Extraction of Road Geometric Features Based on Optimum Path Forest Classifiers, Northeastern Brazil

Macedo¹,

Maia²,

Rabbani³

et al. 2020

JGIS

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One of the principal difficulties related to road safety management in Brazil is the lack of data on road projects, especially those on rural roads, which makes it difficult to use road safety studies and models from other countries as a reference. Updating road networks through the use of hyperspectral remote sensing images can be a good alternative. However, accurately recognizing and extracting hyperspectral images from roads has been recognized as a challenging task in the processing of hyperspectral data. In order to solve the aforementioned challenges, Hyperion hyperspectral images were combined with the Optimum Forest Path (OPF) algorithm for supervised classification of rural roads and the effectiveness of the OPF and SVM classifiers when applied to these areas was compared. Both classifiers produced reasonable results, however, the OPF algorithm outperformed SVM. The higher classification accuracy obtained by the OPF was mainly attributed to the ability to better distinguish between regions of exposed soil and unpaved roads.

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Road Extraction Techniques From Remote Sensing Images: A Review

Cited by 15 publications

References 12 publications

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

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

Global–Local Information Fusion Network for Road Extraction: Bridging the Gap in Accurate Road Segmentation in China

Remote Sensing Applied to the Extraction of Road Geometric Features Based on Optimum Path Forest Classifiers, Northeastern Brazil

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