Road Extraction Research Articles

Deep Learning-Based Road Extraction From Historical Maps

Automatic road extraction from historical maps is an important task to understand past transportation conditions and conduct spatiotemporal analysis revealing information about historical events and human activities over the years. This research aimed to propose the ideal architecture, encoder, and hyperparameter settings for the historical road extraction task. We used a dataset including 7076 patches with the size of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$256 \times256$ </tex-math></inline-formula> pixels generated from scanned historical Deutsche Heereskarte 1:200 000 Türkei (DHK 200 Turkey) maps and their corresponding digitized ground truth masks for five different roads types. We first tested the widely used Unet++ and Deeplabv3 architectures. We also evaluated the contribution of attention models by implementing Unet++ with the concurrent spatial and channel-squeeze and excitation block and multiscale attention net. We achieved the best results with split-attention network (Timm-resnest200e) encoder and Unet++ architecture, with 98.99% overall accuracy, 41.99% intersection of union, 51.41% precision, 69.7% recall, and 57.72% F1 score values. Our output weights could be directly used for the inference of other DHK maps and transfer learning for similar or different historical maps. The proposed architecture could also be implemented in different road extraction studies.

Clustering Point Process Based Network Topology Structure Constrained Urban Road Extraction From Remote Sensing Images

To extract complicated road network from remote sensing images on urban scenes, this article presents a clustering point process (CPP) based network topology structure constrained road extraction algorithm. Firstly, the CPP is constructed to model the feature points, such as endpoints, bends, and crossroads in a road system. Based on that, an initial network topology structure is constructed by connecting the points with lines. Then, according to the network structure characteristic and the spectral characteristic of road, a network topology structure constraining model and a spectral measurement constraining model are constructed, respectively. By combining the models above, a road extraction model is built under the framework of Bayes&#x2019; theorem. Finally, to simulate from the road extraction model and extract an optimal road network, a solution strategy, reversible jump Markov Chain Monte Carlo (RJMCMC) simulation algorithm with related transfer operations, is designed according to the CPP and network topology structure. Several high-resolution remote sensing images on urban scenes are tested. According to a buffer evaluation method, and compared with the comparing algorithms, accuracy and extraction rate of results from the proposed algorithm are increased by 10.86&#x0025; and 8.75&#x0025; on average, respectively. It is proved that the proposed algorithm can extract the complicated road network effectively.

Road and Car Extraction Using UAV Images via Efficient Dual Contextual Parsing Network

The rapid development and commercialization of Unmanned Aerial Vehicle (UAV) technology has made it possible to conduct urban traffic information extraction using UAV images. However, the large variations of targets in urban environments, complex foregrounds and backgrounds in cities, and severe tree and shadow occlusions pose great challenges in car and road extraction using UAV images. In this study, we propose a lightweight, Efficient Dual Contextual Parsing Network (EDCPNet) to address the above issues. The proposed EDCP module in EDCPNet is mainly composed of spatial contextual parsing (SCP) and channel contextual parsing (CCP), which can effectively acquire rich contextual features in both spatial and channel dimensions, adaptively recalibrate the attention weights, perceive the salient features of targets in images, and suppress the importance of irrelevant elements. It thus leads to improved performance and adaptability that facilitate the practical applications of large-scale urban traffic monitoring in complex urban scenes. We conduct experiments on two benchmark datasets (UAVid and UDD) by comparing the proposed EDCPNet with six other competing methods, i.e., U-Net, PSPNet, Deelabv3+, SegNet, ESNet, and ERFNet, and validate the effectiveness of the proposed EDCP module via extensive ablation studies. The results suggest that the proposed network outperforms all competing methods in car and road extraction from UAV images with a balanced computational cost. Its great performance and low computational demand (with only 2.37M model parameters) facilitate its deployment on edge computing devices with memory constraints.

Local Road Area Extraction in CSAR Imagery Exploiting Improved Curvilinear Structure Detector

Road extraction is an important part of synthetic aperture radar (SAR) image interpretation. In recent years, circular SAR (CSAR) has attracted extensive attention from researchers owing to its ability of 360-degree observation. Due to the unique imaging geometry of CSAR, CSAR images contain more complete road information. However, the curvilinear-structured appearance of roads in CSAR images and the complexity of the scene result in difficulties in the road extraction. The Curvilinear structure detector (CSD) is capable of extracting the curvilinear structures with a specific width from complicated image scene. Based on the traditional CSD, an improved CSD (ICSD) for local road area extraction from CSAR images is introduced in this paper. First, by adopting ICSD, the edges of a CSAR image and centerlines of local roads are extracted, as well as their direction. Second, the local roads are obtained by geometrical and radiometrical rules. Finally, the missing intersections and edge pixels are repaired to acquire high-precision and high-quality extraction results of local road area. The experimental results on different band CSAR images reveal that the proposed method exhibit enhanced performance than the three state-of-the-art methods.

Split Depth-Wise Separable Graph-Convolution Network for Road Extraction in Complex Environments From High-Resolution Remote-Sensing Images

Road information from high-resolution remote-sensing images is widely used in various fields, and deep-learning-based methods have effectively shown high road-extraction performance. However, for the detection of roads sealed with tarmac, or covered by trees in high-resolution remote-sensing images, some challenges still limit the accuracy of extraction: 1) large intraclass differences between roads and unclear interclass differences between urban objects, especially roads and buildings; 2) roads occluded by trees, shadows, and buildings are difficult to extract; and 3) lack of high-precision remote-sensing datasets for roads. To increase the accuracy of road extraction from high-resolution remote-sensing images, we propose a split depth-wise (DW) separable graph convolutional network (SGCN). First, we split DW-separable convolution to obtain channel and spatial features, to enhance the expression ability of road features. Thereafter, we present a graph convolutional network to capture global contextual road information in channel and spatial features. The Sobel gradient operator is used to construct an adjacency matrix of the feature graph. A total of 13 deep-learning networks were used on the Massachusetts roads dataset and nine on our self-constructed mountain road dataset, for comparison with our proposed SGCN. Our model achieved a mean intersection over union (mIOU) of 81.65% with an F1-score of 78.99% for the Massachusetts roads dataset, and an mIOU of 62.45% with an F1-score of 45.06% for our proposed dataset. The visualization results showed that SGCN performs better in extracting covered and tiny roads and is able to effectively extract roads from high-resolution remote-sensing images.

BDTNet: Road Extraction by Bi-Direction Transformer From Remote Sensing Images

The past several years have witnessed the rapid development of the task of road extraction in high-resolution remote sensing images. However, due to the complex background and road distribution, road extraction is still a challenging research in remote sensing images. In convolutional neural networks (CNNs), the U-shaped architecture network has shown its effectiveness. But the global representation cannot be captured effectively by CNNs. While in the transformer, the self-attention (SA) module can capture the long-distance feature dependencies. A hybrid encoder-decoder method called BDTNet is proposed in this letter, which enhance the extraction of global and local information in remote sensing images. Firstly, feature maps of different scales are obtained through the backbone network. And then, on the basis of reducing the computational cost of self-attention, the Bi-Direction Transformer Module (BDTM) is constructed to capture the contextual road information in feature maps of different scales. Finally, the Feature Refinement Module (FRM) is introduced to integrate the features extracted from the backbone network and BDTM, which enhances the semantic information of the feature maps and obtains more detailed segmentation results. The results show that the proposed method achieved a high IoU of 67.09% in the DeepGlobe dataset. Extensive experiments also verify the effectiveness of the proposed method on three public remote sensing road datasets.

Extraction of straight field roads between farmlands based on agricultural vehicle-mounted LiDAR

The application of autonomous agricultural vehicles is gaining popularity as a way to increase production efficiency and lower operational costs. To achieve high performance, perception tasks (such as obstacle detection, road extraction, and drivable area extraction) are of great importance. Compared with structured roads, field roads between farmlands, including unstructured roads and semi-structured roads, are unfavorable for autonomous agricultural vehicle driving due to their bumpiness and unstructured nature. This study proposed an extraction method for the straight field roads between farmlands. The proposed method was based on the point cloud data acquired by LiDAR (Velodyne VLP-16) mounted on a John Deere 1204 6B-1204 tractor. The proposed method has three aspects: Euclidean Clustering-based extraction, boundary-based extraction, and road point cloud curve segment modification. Firstly, Euclidean Clustering with K-Dimensional (KD)-Tree data structure was adopted to extract the road curve segments close to the LiDAR composed of road points. Secondly, the boundary lines constraint was constructed to extract the distant road curve segments. Thirdly, the local distance ratio was used to modify the extracted road curve segments. The average extraction accuracy for both semi-structured and unstructured roads exceeded 98%, and the false positive rate (FPR) was less than 0.5%. These experimental findings demonstrated that the proposed road extraction method was precise and effective. The proposed method of this study can be applied to enhance the perception ability of autonomous agricultural vehicles thereby increasing the efficiency and safety of field road driving. Keywords: road extraction, straight field road, autonomous agricultural vehicle, LiDAR, farmland DOI: 10.25165/j.ijabe.20221505.6933 Citation: Yang L L, Xu Y Y, Liang Y J, Qin J, Li Y B, Wang X X, et al. Extraction of straight field roads between farmlands based on agricultural vehicle-mounted LiDAR. Int J Agric & Biol Eng, 2022; 15(5): 155–162.

SC-RoadDeepNet: A New Shape and Connectivity-Preserving Road Extraction Deep Learning-Based Network From Remote Sensing Data

Existing automated road extraction approaches concentrate on regional accuracy rather than road shape and connectivity quality. Most of these techniques produce discontinuous outputs caused by obstacles, such as shadows, buildings, and vehicles. This study proposes a shape and connectivity-preserving road identification deep learning-based architecture called SC-RoadDeepNet to overcome the discontinuous results and the quality of road shape and connectivity. The proposed model comprises a state-of-the-art deep learning-based network, namely, the recurrent residual convolutional neural network, boundary learning (BL), and a new measure based on the intersection of segmentation masks and their (morphological) skeleton called connectivity-preserving centerline Dice (CP_clDice). The recurrent residual convolutional layers accumulate low-level features for segmentation tasks, thus allowing for better feature representation. Such representation enables us to construct a UNet network with the same number of network parameters but improved segmentation effectiveness. BL also aids the model in improving the road’s boundaries by penalizing boundary misclassification and fine-tuning the road form. Furthermore, the CP_clDice method aids the model in maintaining road connectivity and obtaining accurate segmentations. We demonstrate that CP_clDice ensures connection preservation for binary segmentation, thereby allowing for efficient road network extraction at the end. The proposed model improves F1 score accuracy to 5.49%, 4.03%, 3.42%, and 2.27% compared with other comparative models, such as LinkNet, ResUNet, UNet, and VNet, respectively. Furthermore, qualitative and quantitative assessments demonstrate that the proposed SC-RoadDeepNet can improve road extraction by tackling shadow and occlusion-related interruptions. These assessments can also produce high-resolution results, particularly in the area of road network completeness.

TransRoadNet: A Novel Road Extraction Method for Remote Sensing Images via Combining High-Level Semantic Feature and Context

Road extraction is a significant research hotspot in the area of remote sensing images. Extracting an accurate road network from remote sensing images is still challenging because some objects in the images are similar to the road, and some results are discontinuous due to the occlusion. Recently, convolutional neural networks (CNNs) have shown their power in road extraction process. However, the contextual information can not be captured effectively by those CNNs. Based on CNNs, combining with high-level semantic features and foreground contextual information, a novel road extraction method for remote sensing images is proposed in this paper. Firstly, the position attention (PA) mechanism is designed to enhance the expression ability for the road feature. Then the contextual information extraction module (CIEM) is constructed to capture the road contextual information in the images. At last, foreground contextual information supplement module (FCISM) is proposed to provide foreground context information at different stages of the decoder, which can improve the inference ability for the occluded area. Extensive experiments on the DeepGlobal road dataset showed the proposed method outperforms the existing methods in accuracy, IoU, Precision, F1-score, and yields competitive recall results, which demonstrated the efficiency of the new model.

DBRANet: Road Extraction by Dual-Branch Encoder and Regional Attention Decoder

Although widely exploited in recent decades, road extraction is still a very significant and challenging research in the field of remote sensing image processing due to the complex background and road distribution. Among the existing CNN-based methods, U-shape architectures composed of encoders and decoders have shown their effectiveness. In this letter, we propose an improved encoder–decoder method, named DBRANet, for extracting roads from remote sensing images. In the encoding phase, we present a dual-branch network module (DBNM) to construct more effective features, thus improving the fusion feature maps of different scales. One branch utilizes the residual block, and the other branch utilizes the refined asymmetric block, which effectively increases the feature extraction capability of the backbone. In the decoding phase, considering the sinuous shape and the unbalanced distribution of roads in remote sensing images, we design a novel attention module, named the regional attention network module (RANM), to automatically learn the importance of each channel according to the regional information. Extensive experiments on several public remote sensing road data sets show that our DBRANet achieves higher segmentation [ <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$F1$ </tex-math></inline-formula> score and Intersection over Union (IoU)] and connectivity [average path length similarity (APLS)] accuracy, which verifies the effectiveness of our approach.

Learn to Be Clear and Colorful: An End-to-End Network for Panchromatic Image Enhancement

Benefiting from the high coverage and re-visiting frequency, the satellite imagery is an ideal data for large-scale, real-time earth observation. However, due to the limited resolution and chromatic information, the satellite images, especially the panchromatic images, are not capable of being used for accurate earth observations, such as road extraction, vehicle detection, and building segmentation. In this research, we propose a cascaded fully convolutional network (CFCN) consists of a residual dense super-resolution network (RDSRN) for grayscale image super-resolution (SR) and a residual deconvolution colorization network (RDCN) for grayscale image colorization. The unique architecture can simultaneously learn texture detail and color information from aerial images and then transfer to enhance panchromatic images. Furthermore, we also introduce an indirect evaluation metric, learned extraction similarity (LES), to estimate the image quality of the generated image in the absence of the ground truth. Experiments on a multispectral image dataset demonstrate that panchromatic images enhanced by the proposed CFCN are with both texture and color fidelity as compared to aerial image. For pre-trained U-Net, compared to the performance on raw panchromatic images, the CFCN enhanced images increase 12.8% of LES of overall accuracy (96.4% versus 83.6%).

Road Extraction from Remote Sensing Images Based on Adaptive Morphology

Road Extraction from Remote Sensing Images Based on Adaptive Morphology

ConDinet++: Full-Scale Fusion Network Based on Conditional Dilated Convolution to Extract Roads From Remote Sensing Images

Extracting roads from aerial images is an issue that has attracted much attention. Using semantic segmentation methods to extract roads often faces the problem of narrow and occluded roads. In this letter, we propose a network called ConDinet&#x002B;&#x002B;, which improves the general codec architecture. In the encoder part, the VGG16 with pretraining parameters is utilized for the feature extraction. In the decoder part, we perform a feature fusion mechanism on the full-scale feature map. In order to improve the ability of the network to extract and integrate semantic information and further increase the receptive field, we recommend adopting the conditional dilated convolution blocks (CDBs) in the encoder, and each CDB consists of a group of cascaded conditional dilated convolutions. More importantly, the designed codec architecture can adjust the number of convolutions and the parameters of the convolution kernel according to the input data. For a slender area like a road, which occupies a small area in the picture, we use the joint loss function and introduce the joint loss of Lovasz loss and cross-entropy loss to avoid the segmentation model having a serious bias caused by highly unbalanced object sizes between roads and background. The proposed method was tested on two public datasets Massachusetts Roads Dataset and Mini DeepGlobe Road Extraction Challenge. Compared with some previous semantic segmentation networks, the proposed ConDinet&#x002B;&#x002B; achieved the best values of recall, F-score, and mIoU.

Urban Road Extraction Based on Morphological Filtering and Trajectory Detection

Urban Road Extraction Based on Morphological Filtering and Trajectory Detection

NIGAN: A Framework for Mountain Road Extraction Integrating Remote Sensing Road-Scene Neighborhood Probability Enhancements and Improved Conditional Generative Adversarial Network

Mountain roads are a source of important basic geographic data used in various fields. The automatic extraction of road images through high-resolution remote sensing imagery using deep learning has attracted considerable attention. But the interference of context information limited extraction accuracy, especially for roads in mountain area. Furthermore, when pursuing research in a new district, many algorithms are difficult to train due to a lack of data. To address these issues, a framework based on remote sensing road-scene neighborhood probability enhancement and improved conditional generative adversarial network (NIGAN) is proposed in this article. This framework can be divided into two sections: 1) road scenes classification section. A remote sensing road-scene neighborhood confidence enhancement method was designed for classifying road scenes of the study area to reduce the impact of nonroad information on subsequent fine-road segmentation and 2) fine-road segmentation section. An improved dilated convolution module, which is helpful in extracting small objects such as road, was added into the conditional generative adversarial network (CGAN) to increase the receptive field and pay attention to global information, and segment roads from the results of road scenes classification section. To validate the NIGAN framework, new mountain road-scene and label datasets were constructed, and diverse comparison experiments were performed. The results indicate that the NIGAN framework can improve the integrity and accuracy of mountain road-scene extraction in diverse and complex conditions. The results further confirm the validity of the NIGAN framework in small samples. In addition, the mountain road-scene datasets can serve as benchmark datasets for studying mountain road extraction.

CaT: CAVS Traversability Dataset for Off-Road Autonomous Driving

In the context of autonomous driving, the existing semantic segmentation concept strongly supports on-road driving where hard inter-class boundaries are enforced and objects can be categorized based on their visible structures with high confidence. Due to the well-structured nature of typical on-road scenes, current road extraction processes are largely successful and most types of vehicles are able to traverse through the area that is detected as road. However, the off-road driving domain has many additional uncertainties such as uneven terrain structure, positive and negative obstacles, ditches, quagmires, hidden objects, etc. making it very unstructured. Traversing through such unstructured area is constrained by a vehicle’s type and its capability. Therefore, an alternative approach to segmentation of the off-road driving trail is required that supports consideration of the vehicle type in a way that is not considered in state-of-the-art on-road segmentation approaches. To overcome this limitation and facilitate the path extraction in the off-road driving domain, we propose traversability concept and corresponding dataset which is based on the notion that the driving trails should be finely resolved into different sub-trails and areas corresponding to the capability of different vehicle classes in order to achieve safe traversal. Based on this, we consider three different classes of vehicles (sedan, pickup, and off-road) and label the images corresponding to the traversing capability of those vehicles. So the proposed dataset facilitates the segmentation of off-road driving trail into three regions based on the nature of the driving area and vehicle capability. We call this dataset as CaT (CAVS Traversability, where CAVS stands for Center for Advanced Vehicular Systems) dataset and is publicly available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://www.cavs.msstate.edu/resources/downloads/CaT/CaT.tar.gz</uri> .

Road extraction method of high-resolution remote sensing image on the basis of the spatial information perception semantic segmentation model

Road extraction method of high-resolution remote sensing image on the basis of the spatial information perception semantic segmentation model

DDU-Net: Dual-Decoder-U-Net for Road Extraction Using High-Resolution Remote Sensing Images

Extracting roads from high-resolution remote sensing images (HRSIs) is vital in a wide variety of applications, such as autonomous driving, path planning, and road navigation. Due to the long and thin shape as well as the shades induced by vegetation and buildings, small-sized roads are more difficult to discern. In order to improve the reliability and accuracy of small-sized road extraction when roads of multiple sizes coexist in an HRSI, an enhanced deep neural network model termed Dual-Decoder-U-Net (DDU-Net) is proposed in this paper. Motivated by the U-Net model, a small decoder is added to form a dual-decoder structure for more detailed features. In addition, we introduce the dilated convolution attention module (DCAM) between the encoder and decoders to increase the receptive field as well as to distill multi-scale features through cascading dilated convolution and global average pooling. The convolutional block attention module (CBAM) is also embedded in the parallel dilated convolution and pooling branches to capture more attention-aware features. Extensive experiments are conducted on the Massachusetts Roads dataset with experimental results showing that the proposed model outperforms the state-of-the-art DenseUNet, DeepLabv3+ and D-LinkNet by 6.5%, 3.3%, and 2.1% in the mean Intersection over Union (mIoU), and by 4%, 4.8%, and 3.1% in the F1 score, respectively. Both ablation and heatmap analyses are presented to validate the effectiveness of the proposed model.

Road extraction model derived from integrated attention mechanism and dilated convolution

Road extraction model derived from integrated attention mechanism and dilated convolution

Dual-Path Morph-UNet for Road and Building Segmentation From Satellite Images

Building footprints and road network detection have gained significant attention for map preparation, humanitarian aid dissemination, disaster management, to name a few. Traditionally, morphological filters excel at extracting shape features from remotely sensed images and have been widely used in the literature. However, the structural element (SE) dimension selection impedes these classical and learning-based methods utilizing any morphological operators. To overcome this aspect, we propose a novel framework to extract road and building from remote sensing (RS) images by exploiting morphological networks. The method predominantly aims at learning an optimized SE to capture variably-sized building and road footprints. We substitute convolutions with 2-D morphological operations in the basic building blocks of the network architecture (Dual-path Morph-UNet) to manage the intricate task of optimizing the SE in addition to the actual segmentation task. The dual-path framework incorporates parallel residual and dense paths in an encoder-decoder architecture, which permits learning of higher-level feature representations with fewer parameters. Finally, we implement the proposed framework on the benchmarked Massachusetts roads and buildings dataset and demonstrate superior results than the state-of-the-art (SOTA). In addition, the proposed network consists of 10x less learnable parameters than the SOTA methods.