Road Extraction Research Articles

Road-MobileSeg: Lightweight and Accurate Road Extraction Model from Remote Sensing Images for Mobile Devices.

Current road extraction models from remote sensing images based on deep learning are computationally demanding and memory-intensive because of their high model complexity, making them impractical for mobile devices. This study aimed to develop a lightweight and accurate road extraction model, called Road-MobileSeg, to address the problem of automatically extracting roads from remote sensing images on mobile devices. The Road-MobileFormer was designed as the backbone structure of Road-MobileSeg. In the Road-MobileFormer, the Coordinate Attention Module was incorporated to encode both channel relationships and long-range dependencies with precise position information for the purpose of enhancing the accuracy of road extraction. Additionally, the Micro Token Pyramid Module was introduced to decrease the number of parameters and computations required by the model, rendering it more lightweight. Moreover, three model structures, namely Road-MobileSeg-Tiny, Road-MobileSeg-Small, and Road-MobileSeg-Base, which share a common foundational structure but differ in the quantity of parameters and computations, were developed. These models varied in complexity and were available for use on mobile devices with different memory capacities and computing power. The experimental results demonstrate that the proposed models outperform the compared typical models in terms of accuracy, lightweight structure, and latency and achieve high accuracy and low latency on mobile devices. This indicates that the models that integrate with the Coordinate Attention Module and the Micro Token Pyramid Module surpass the limitations of current research and are suitable for road extraction from remote sensing images on mobile devices.

3D Road Boundary Extraction Based on Machine Learning Strategy Using LiDAR and Image-Derived MMS Point Clouds.

The precise extraction of road boundaries is an essential task to obtain road infrastructure data that can support various applications, such as maintenance, autonomous driving, vehicle navigation, and the generation of high-definition maps (HD map). Despite promising outcomes in prior studies, challenges persist in road extraction, particularly in discerning diverse road types. The proposed methodology integrates state-of-the-art techniques like DBSCAN and RANSAC, aiming to establish a universally applicable approach for diverse mobile mapping systems. This effort represents a pioneering step in extracting road information from image-based point cloud data. To assess the efficacy of the proposed method, we conducted experiments using a large-scale dataset acquired by two mobile mapping systems on the Yıldız Technical University campus; one system was configured as a mobile LiDAR system (MLS), while the other was equipped with cameras to operate as a photogrammetry-based mobile mapping system (MMS). Using manually measured reference road boundary data, we evaluated the completeness, correctness, and quality parameters of the road extraction performance of our proposed method based on two datasets. The completeness rates were 93.2% and 84.5%, while the correctness rates were 98.6% and 93.6%, respectively. The overall quality of the road curb extraction was 93.9% and 84.5% for the two datasets. Our proposed algorithm is capable of accurately extracting straight or curved road boundaries and curbs from complex point cloud data that includes vehicles, pedestrians, and other obstacles in urban environment. Furthermore, our experiments demonstrate that the algorithm can be applied to point cloud data acquired from different systems, such as MLS and MMS, with varying spatial resolutions and accuracy levels.

Enhancing Road Extraction in Large-Scale Complex Terrain through Multi-Source Remote Sensing Image Fusion and Optimization

In large-scale remote sensing scenarios characterized by intricate terrain, the straightforward road imaging features in synthetic aperture radar (SAR) images make them susceptible to interference from other elements such as ridges, compromising the robustness of conventional SAR image road extraction methods. This paper introduces a method that integrates Gaofen-3 (GF-3) with a resolution of 3.0 m, Digital Elevation Models (DEMs), and Gaofen-2 (GF-2) remote sensing image data with a resolution of 4.0 m, aiming to improve the performance of road extraction in complex terrain. Leveraging DEMs, this study addresses the limitations in feature-based SAR algorithms, extending their application to complex remote sensing scenarios. Decision-level fusion, integrating SAR and multispectral images, further refines road extraction precision. To overcome issues related to terrain interference, including fragmented road segments, an adaptive rotated median filter and graph-theory-based optimization are introduced. These advancements collectively enhance road recognition accuracy and topological precision. The experimental results validate the effectiveness of the multi-source remote sensing image fusion and optimization methods. Compared to road extraction from multispectral images, the F1-score of the proposed method on the test images increased by 2.18%, 4.22%, and 1.4%, respectively.

Earth Observation Data and Geospatial Deep Learning AI to Assign Contributions to European Municipalities Sen4MUN: An Empirical Application in Aosta Valley (NW Italy)

Nowadays, European program Copernicus’ Sentinel missions have allowed the development of several application services. In this regard, to strengthen the use of free satellite data in ordinary administrative workflows, this work aims to evaluate the feasibility and prototypal development of a possible service called Sen4MUN for the distribution of contributions yearly allocated to local municipalities and scalable to all European regions. The analysis was focused on the Aosta Valley region, North West Italy. A comparison between the Ordinary Workflow (OW) and the suggested Sen4MUN approach was performed. OW is based on statistical survey and municipality declaration, while Sen4MUN is based on geospatial deep learning techniques on aerial imagery (to extract roads and buildings to get real estate units) and yearly Land Cover map components according to European EAGLE guidelines. Both methods are based on land cover components which represent the input on which the financial coefficients for assigning contributions are applied. In both approaches, buffers are applied onto urban class (LCb). This buffer was performed according to the EEA-ISPRA soil consumption guidelines to avoid underestimating some areas that are difficult to map. In the case of Sen4MUN, this is applied to overcome Sentinel sensor limits and spectral mixing issues, while in the case of OW, this is due to limits in the survey method itself. Finally, a validation was performed assuming as truth the approach defined by law as the standard, i.e., OW, although it has limitations. MAEs involving LCb, road lengths and real estate units demonstrate the effectiveness of Sen4MUN. The developed approach suggests a contribution system based on Geomatics and Remote sensing to the public administration.

A new two-step road extraction method in high resolution remote sensing images.

High-resolution remote sensing technology is an efficient and low-cost space-to-earth observation strategy, which can carry out simultaneous monitoring of large-scale areas. It has incomparable advantages over ground monitoring solutions. Traditional road extraction methods are mainly based on image processing techniques. These methods usually only use one or a few features of images, which is difficult to fully deal with the real situation of roads. This work proposes a two-steps network for the road extraction. First, we optimize a pix2pix model for image translation to obtain the required map style image. Images output by the optimized model is full of road features and can relief the occlusion issues. It can intuitively reflect information such as the position, shape and size of the road. After that, we propose a new FusionLinkNet model, which has a strong stability in the road information by fusing the DenseNet, ResNet and LinkNet. Experiments show that our accuracy and learning rate have been improved. The MIOU (Mean Intersection Over Union) value of the proposed model in road extraction is over 80% in both DeepGlobe and Massachusetts road dataset. The figures are available from https://github.com/jsit-luwei/training-dataset.

Road Extraction From High-Resolution Remote Sensing Images of Open-Pit Mine Using D-SegNeXt

Road Extraction From High-Resolution Remote Sensing Images of Open-Pit Mine Using D-SegNeXt

Research on Road Extraction From High-Resolution Remote Sensing Images Based on Improved UNet++

Research on Road Extraction From High-Resolution Remote Sensing Images Based on Improved UNet++

Road Extraction From Satellite Images Using Attention-Assisted UNet

Road Extraction From Satellite Images Using Attention-Assisted UNet

Utilization of Improved Annotations From Object-Based Image Analysis as Training Data for DeepLab V3+ Model: A Focus on Road Extraction in Very High-Resolution Orthophotos

Utilization of Improved Annotations From Object-Based Image Analysis as Training Data for DeepLab V3+ Model: A Focus on Road Extraction in Very High-Resolution Orthophotos

DEGANet: Road Extraction Using Dual-branch Encoder with Gated Attention Mechanism

DEGANet: Road Extraction Using Dual-branch Encoder with Gated Attention Mechanism

Road Extraction From Remote Sensing Images via Channel Attention and Multilayer Axial Transformer

Road Extraction From Remote Sensing Images via Channel Attention and Multilayer Axial Transformer

A Label Refining Framework Based on Road Matching and Integration Algorithm for Road Extraction

A Label Refining Framework Based on Road Matching and Integration Algorithm for Road Extraction

An Attention Augmented Convolution-based Tiny-Residual UNet for Road Extraction

An Attention Augmented Convolution-based Tiny-Residual UNet for Road Extraction

MCMCNet: A Semi-Supervised Road Extraction Network for High-Resolution Remote Sensing Images via Multiple Consistency and Multitask Constraints

MCMCNet: A Semi-Supervised Road Extraction Network for High-Resolution Remote Sensing Images via Multiple Consistency and Multitask Constraints

Residual Channel Attention Fusion Network for Road Extraction Based on Remote Sensing Images and GPS Trajectories

Residual Channel Attention Fusion Network for Road Extraction Based on Remote Sensing Images and GPS Trajectories

DBARCT: Road Extraction Based on Double-Branch Architecture and Random Block Coding Transformer

DBARCT: Road Extraction Based on Double-Branch Architecture and Random Block Coding Transformer

Neighborhood Attention Makes the Encoder of ResUNet Stronger for Accurate Road Extraction

Neighborhood Attention Makes the Encoder of ResUNet Stronger for Accurate Road Extraction

Weakly Supervised Network with Scribble-Supervised and Edge-Mask for Road Extraction from High-Resolution Remote Sensing Images

Weakly Supervised Network with Scribble-Supervised and Edge-Mask for Road Extraction from High-Resolution Remote Sensing Images

Lightweight Cross-Modal Information Measure and Propagation for Road Extraction From Remote Sensing Image and Trajectory/LiDAR

Lightweight Cross-Modal Information Measure and Propagation for Road Extraction From Remote Sensing Image and Trajectory/LiDAR

BIR-Net: A Lightweight and Efficient Bilateral Interaction Road Extraction Network

BIR-Net: A Lightweight and Efficient Bilateral Interaction Road Extraction Network