Road Network Extraction Research Articles

Fractional derivative based weighted skip connections for satellite image road segmentation

Segmentation of a road portion from a satellite image is challenging due to its complex background, occlusion, shadows, clouds, and other optical artifacts. One must combine both local and global cues for an accurate and continuous/connected road network extraction. This paper proposes a model using fractional derivative-based weighted skip connections on a densely connected convolutional neural network for road segmentation. Weights corresponding to the skip connections are determined using Grunwald–Letnikov fractional derivative. Fractional derivatives being non-local in nature incorporates memory into the system and thereby combine both local and global features. Experiments have been performed on two open source widely used benchmark databases viz. Massachusetts Road database (MRD) and Ottawa Road database (ORD). Both these datasets represent different road topography and network structure including varying road widths and complexities. Result reveals that the proposed system demonstrated better performance than the other state-of-the-art methods by achieving an F1-score of 0.748 and the mIoU of 0.787 at fractional order 0.4 on the MRD and a mIoU of 0.9062 at fractional order 0.5 on the ORD.

Road Network Detection from Aerial Imagery of Urban Areas Using Deep ResUNet in Combination with the B-snake Algorithm

Road network detection is critical to enhance disaster response and detecting a safe evacuation route. Due to expanding computational capacity, road extraction from aerial imagery has been investigated extensively in the literature, specifically in the last decade. Previous studies have mainly proposed methods based on pixel classification or image segmentation as road/non-road images, such as thresholding, edge-based segmentation, k-means clustering, histogram-based segmentation, etc. However, these methods have limitations of over-segmentation, sensitivity to noise, and distortion in images. This study considers the case study of Hawkesbury Nepean valley, NSW, Australia, which is prone to flood and has been selected for road network extraction. For road area extraction, the application of semantic segmentation along with residual learning and U-Net is suggested. Public road datasets were used for training and testing purposes. The study suggested a framework to train and test datasets with the application of the deep ResUnet architecture. Based on maximal similarity, the regions were merged, and the road network was extracted with the B-snake algorithm application. The proposed framework (baseline + region merging + B-snake) improved performance when evaluated on the synthetically modified dataset. It was evident that in comparison with the baseline, region merging and addition of the B-snake algorithm improved significantly, achieving a value of 0.92 for precision and 0.897 for recall.

Satellite road extraction method based on RFDNet neural network

<abstract><p>The road network system is the core foundation of a city. Extracting road information from remote sensing images has become an important research direction in the current traffic information industry. The efficient residual factorized convolutional neural network (ERFNet) is a residual convolutional neural network with good application value in the field of biological information, but it has a weak effect on urban road network extraction. To solve this problem, we developed a road network extraction method for remote sensing images by using an improved ERFNet network. First, the design of the network structure is based on an ERFNet; we added the DoubleConv module and increased the number of dilated convolution operations to build the road network extraction model. Second, in the training process, the strategy of dynamically setting the learning rate is adopted and combined with batch normalization and dropout methods to avoid overfitting and enhance the generalization ability of the model. Finally, the morphological filtering method is used to eliminate the image noise, and the ultimate extraction result of the road network is obtained. The experimental results show that the method proposed in this paper has an average F1 score of 93.37% for five test images, which is superior to the ERFNet (91.31%) and U-net (87.34%). The average value of IoU is 77.35%, which is also better than ERFNet (71.08%) and U-net (65.64%).</p></abstract>

Extraction of Road Network in Urban Area from Orthophoto Using Deep Learning and Douglas-Peucker Post-Processing Algorithm

A Large-scale base map is needed by metropolitan cities such as Surabaya City for urban planning and smart city development. The most needed information from a large-scale base map is road geospatial information. Road network extraction is a challenging task for many reasons, including the heterogeneous attribute of the geometry and spectral, the complexity of objects that are difficult to model, and poor sensor data. Visual interpretation by operators is still a commonly used approach for extracting information from orthophoto. Interpretation accuracy depends on the skill and experience of the operator. So, there can be inconsistencies in the data generated by different operators. In recent years, the automatic extraction of road from orthophoto or VHR image has become an important and challenging research issue. Many recent studies have explored deep learning to improve the quality of building and road extraction. In this study, we applied a Mask Region-based Convolutional Neural Network (Mask R-CNN) model for the road network extraction using orthophoto in urban area in Surabaya City. The quality of the extracted geometry needs to be improved. Several post-processing strategies, including polygon regularization using Douglas-Peucker algorithm and polygon smoothing are designed to achieve optimal extraction results. The method produces a good performance for road extraction, the precision is 90.28%; recall 85.85%; F1-score 88.01%; and IoU 78.59%; and the overall accuracy is 95.25% and the kappa value is 90.5%.

Automatic Extraction of Road Networks by Using Teacher–Student Adaptive Structural Deep Belief Network and Its Application to Landslide Disaster

Automatic Extraction of Road Networks by Using Teacher–Student Adaptive Structural Deep Belief Network and Its Application to Landslide Disaster

Deep Learning Multimodal Fusion for Road Network Extraction: Context and Contour Improvement

Deep Learning Multimodal Fusion for Road Network Extraction: Context and Contour Improvement

TERNformer: Topology-Enhanced Road Network Extraction by Exploring Local Connectivity

TERNformer: Topology-Enhanced Road Network Extraction by Exploring Local Connectivity

Research on Urban Road Network Extraction Based on Web Map API Hierarchical Rasterization and Improved Thinning Algorithm

Most existing research on the vector road network is based on GPS trajectory travel information extraction, and urban GPS trajectory data are large and difficult to obtain. Based on this, this study proposes a road network extraction method based on network map API and designs a vector road network based on an improved image-processing algorithm using trajectory data. Firstly, a large number of trajectory data are processed by hierarchical rasterization. The trajectory points of the regional OD matrix are obtained by using the map API interface to generate the trajectory. Then, the image expansion processing is performed on the road network raster image to complete the information loss problem. The improved Zhang–Suen refinement algorithm is used to refine the idea to obtain the road center line, and the vector road network in the study area is obtained. Finally, taking the Harbin City of Heilongjiang Province as an example, compared with the road network of the network map, it has been demonstrated that using this technology may improve the traveler experience and the sustainability of urban traffic flow while reducing the number of manual procedures required, performing online incremental rapid change detection, and updating the present road network at a cheaper cost.

A Land Cover Background-Adaptive Framework for Large-Scale Road Extraction

Background: Road network data are crucial in various applications, such as emergency response, urban planning, and transportation management. The recent application of deep neural networks has significantly boosted the efficiency and accuracy of road network extraction based on remote sensing data. However, most existing methods for road extraction were designed at local or regional scales. Automatic extraction of large-scale road datasets from satellite images remains challenging due to the complex background around the roads, especially the complicated land cover types. To tackle this issue, this paper proposes a land cover background-adaptive framework for large-scale road extraction. Method: A large number of sample image blocks (6820) are selected from six different countries of a wide region as the dataset. OpenStreetMap (OSM) is automatically converted to the ground truth of networks, and Esri 2020 Land Cover Dataset is taken as the background land cover information. A fuzzy C-means clustering algorithm is first applied to cluster the sample images according to the proportion of certain land use types that obviously negatively affect road extraction performance. Then, the specific model is trained on the images clustered as abundant with that certain land use type, while a general model is trained based on the rest of the images. Finally, the road extraction results obtained by those general and specific modes are combined. Results: The dataset selection and algorithm implementation were conducted on the cloud-based geoinformation platform Google Earth Engine (GEE) and Google Colaboratory. Experimental results showed that the proposed framework achieved stronger adaptivity on large-scale road extraction in both visual and statistical analysis. The C-means clustering algorithm applied in this study outperformed other hard clustering algorithms. Significance: The promising potential of the proposed background-adaptive network was demonstrated in the automatic extraction of large-scale road networks from satellite images as well as other object detection tasks. This search demonstrated a new paradigm for the study of large-scale remote sensing applications based on deep neural networks.

Road Network Extraction from SAR Images with the Support of Angular Texture Signature and POIs

Urban road network information is an important part of modern spatial information infrastructure and is crucial for high-precision navigation map production and unmanned driving. Synthetic aperture radar (SAR) is a widely used remote-sensing data source, but the complex structure of road networks and the noises in images make it very difficult to extract road information through SAR images. We developed a new method of extracting road network information from SAR images by considering angular (A) and texture (T) features in the sliding windows and points of interest (POIs, or P), and we named this method ATP-ROAD. ATP-ROAD is a sliding window-based semi-automatic approach that uses the grayscale mean, grayscale variance, and binary segmentation information of SAR images as texture features in each sliding window. Since POIs have much-duplicated information, this study also eliminates duplicated POIs considering distance and then selects a combination of POI linkages by discerning the direction of these POIs to initially determine the road direction. The ATP-ROAD method was applied to three experimental areas in Shanghai to extract the road network using China’s Gaofen-3 imagery. The experimental results show that the extracted road network information is relatively complete and matches the actual road conditions, and the result accuracy is high in the three different regions, i.e., 89.57% for Area-I, 96.88% for Area-II, and 92.65% for Area-III. Our method together with our extraction software can be applied to extract information about road networks from SAR images, providing an alternative for enriching the variety of road information.

RoadFormer: Pyramidal deformable vision transformers for road network extraction with remote sensing images

The data-complete and detail-correct road network information serves as important evidence in numerous transportation-associated applications. Regular and rapid road network inventory updating is significantly necessary and meaningful to provide better services. Remote sensing images, due to their advantageous overlooking earth observation properties, have been widely used to assist in the road network interpretation tasks. However, it is still an open issue to accurately separate the road contents from the surrounding land covers in the remote sensing image with good connectivity and integrality because of the remarkably challengeable conditions of roads. In this regard, we develop a pyramidal deformable vision transformer architecture, termed as RoadFormer, to extract road networks with remote sensing images. Specifically, designed by a multi-context patch embedding scheme, a higher-quality token embedding can be obtained by adopting a multi-range, multi-view context observation strategy. Furthermore, formulated with a deformable transformer architecture, the semantic-relevant features can be focused on in a sparse global manner, which effectively promotes the feature representation quality and robustness. The proposed RoadFormer is elaborately evaluated on three large-scale road network extraction datasets. Quantitative assessments show that the RoadFormer achieves an overall performance of 0.8886 and 0.9407 with respect to the intersection over union (IoU) and F1-score metrics. In addition, contrastive evaluations also convince the promising potentiality and outstanding superiority of the RoadFormer for interpreting the road sections of varying circumstances under diverse challenging image scenarios.

Multiple Objects and Road Detection in Unmanned Aerial Vehicle

Unmanned Aerial Vehicles have greater potential to widely used in military and civil applications. Additionally equipped with the cameras can also be used in agriculture and surveillance. Aerial imagery has its own unique challenges that differ from the training set of modern-day object detectors, since it is made of images of larger areas compared to the regular datasets and the objects are very small on the contrary. These problems do not allow us to use common object detection models. Currently there are many computer vision algorithm that are designed using human centric photographs, But from the top view imagery taken vertically the objects of interest are small and fewer features mostly appearing flat and rectangular, certain objects closer to each other can also overlap. So detecting most of the objects from the birds eye view is a challenging task. Hence the work will be focusing on detecting multiple objects from those images using enhanced ResNet, FPN, FasterRCNN models thereby providing an effective surveillance for the UAV and extraction of road networks from aerial images has fundamental importance.

SW-GAN: Road Extraction from Remote Sensing Imagery Using Semi-Weakly Supervised Adversarial Learning

Road networks play a fundamental role in our daily life. It is of importance to extract the road structure in a timely and precise manner with the rapid evolution of urban road structure. Recently, road network extraction using deep learning has become an effective and popular method. The main shortcoming of the road extraction using deep learning methods lies in the fact that there is a need for a large amount of training datasets. Additionally, the datasets need to be elaborately annotated, which is usually labor-intensive and time-consuming; thus, lots of weak annotations (such as the centerline from OpenStreetMap) have accumulated over the past a few decades. To make full use of the weak annotations, we propose a novel semi-weakly supervised method based on adversarial learning to extract road networks from remote sensing imagery. Our method uses a small set of pixel-wise annotated data and a large amount of weakly annotated data for training. The experimental results show that the proposed approach can achieve a maintained performance compared with the methods that use a large number of full pixel-wise annotations while using less fully annotated data.

Identifying Urban Functional Regions from High-Resolution Satellite Images Using a Context-Aware Segmentation Network

The automatic identification of urban functional regions (UFRs) is crucial for urban planning and management. A key issue involved in URF classification is to properly determine the basic functional units, for which popular practices are usually based upon existing land use boundaries or road networks. Such practices suffer from the unavailability of existing datasets, leading to difficulty in large-scale mapping. To deal with this problem, this paper presents a method to automatically obtain functional units for URF classification using high-resolution remote sensing images. We develop a context-aware segmentation network to simultaneously extract buildings and road networks from remote sensing images. The extracted road networks are used for partitioning functional units, upon which five main building types are distinguished considering building height, morphology, and geometry. Finally, the UFRs are classified according to the distribution of building types. We conducted experiments using a GaoFen-2 satellite image with a spatial resolution of 0.8 m acquired in Fuzhou, China. Experimental results showed that the proposed segmentation network performed better than other convolutional neural network segmentation methods (i.e., PSPNet, Deeplabv3+, DANet, and JointNet), with an increase of F1-score up to 1.37% and 1.19% for road and building extraction, respectively. Results also showed that the residential regions, accounting for most of the urban areas, identified by the proposed method had a user accuracy of 94%, implying the promise of the proposed method for deriving the spatial units and the types of urban functional regions.

RoadCapsFPN: Capsule Feature Pyramid Network for Road Extraction From VHR Optical Remote Sensing Imagery

Road detection plays an important role in a wide range of applications. However, due to size variations, spectral diversities, occlusions, and complex scenarios, it is still challenging to accurately extract roads from very-high resolution (VHR) optical remote sensing images. This paper proposes a capsule feature pyramid network for extracting road networks from VHR optical images, termed as RoadCapsFPN. By designing a capsule feature pyramid network, the RoadCapsFPN extracts and integrates multiscale capsule features to recover a high-resolution and semantically strong road feature representation. Next, we also design a contextual feature module, including dense atrous convolution (DAC) and residual multi-kernel pooling (RMP) units, to further exploit rich contextual properties of the roads at a high-resolution perspective. Benefitting from the multiscale feature abstraction and context augmentation, our RoadCapsFPN shows impressing results in processing variedly-sized and diversely-spectral roads in complex environments. Two testing datasets, Google and Massichusate Roads Datasets, are used for evaluating the proposed RoadCapsFPN via four testing indicators - <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">precision</i> , <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">recall</i> , intersection-over-union ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">IoU</i> ), and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$F_{1}$ </tex-math></inline-formula> - <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">score</i> . Comparative studies also confirm the superior performance of the RoadCapsFPN in accurately extracting road networks.

Weakly supervised road network extraction for remote sensing image based scribble annotation and adversarial learning

Performing accurate road extraction from remote sensing images has great practical importance. Nevertheless, the accuracy of segmentation can be seriously affected by complex and variable objective factors. Complex topological structures lead to discontinuity of extracted roads. State-of-the-art road extraction methods rely heavily on intensive annotation with high acquisition costs. In this work, we propose a scribble-based weakly supervised remote sensing road extraction network (WR2E), which can extract roads from remote sensing images based on scribble annotations. A road trimap generation algorithm is introduced to allow the propagation of semantic information for scribble annotations. The WR2E network contains two key modules, i.e., the road positioning module (RPM) and the oriented attention module (OAM). The road positioning module is designed to locate the initial position of the road from a global perspective, and the oriented attention module guides the propagation of high-level semantic information based on image affinity features. The proposed modal achieved the highest structural measure of 89.1%, the highest adaptive E measure of 92.0%, the highest weighted F measure of 88.6%, the highest mean absolute error of 3.6%, the highest IoU of 86.9%, and the highest F1-score of 93.7%, indicating the WR2E can accurately and effectively implement remote sensing image road extraction.

ROAD NETWORK EXTRACTION USING GPS TRAJECTORIES BASED ON MORPHOLOGICAL AND SKELETONIZATION ALGORITHMS

Abstract. In this article, a method for road network extraction is proposed, based on GPS (Global Positioning System) trajectories. Unlike existing methods, it is not necessary to resample the GPS trajectories into a raster structure; instead, all analyses are based on the polylines that represent the GPS trajectories. Basically, a morphological analysis and a skeletonization technique are used by the proposed method. Two main steps of the method can be identified: the first step consists in generating an elongated polygon (that delimitates an elongated ribbon) that represents the selected road; and the second step aims at reconstructing the road network. The proposed method was evaluated based on four GPS trajectory datasets and the results obtained can be considered good, but some inconsistencies were noted, as for example: extraction failures occur in places with very low trajectory density (such as 3–4 trajectories); merging of very close and parallel roads; some road crossings that are close to one another have been merged into a single point. The proposed method was also compared with existing methods in the literature and the obtained results showed good consistency between them.

Semi-Automatic Method of Extracting Road Networks from High-Resolution Remote-Sensing Images

Road network extraction plays a critical role in data updating, urban development, and decision support. To improve the efficiency of labeling road datasets and addressing the problems of traditional methods of manually extracting road networks from high-resolution images, such as their slow speed and heavy workload, this paper proposes a semi-automatic method of road network extraction from high-resolution remote-sensing images. The proposed method needs only a few points to extract a single road in the image. After the roads are extracted one by one, the road network is generated according to the width of each road and the spatial relationships among the roads. For this purpose, we use regional growth, morphology, vector tracking, vector simplification, endpoint modification, road connections, and intersection connections to generate road networks. Experiments on four images with different terrains and different resolutions show that this method has high extraction accuracy under different image conditions. The comparisons with the semi-automatic GVF-snake method based on regional growth also showed its advantages and potentiality. The proposed method is a novel form of semi-automatic road network extraction, and it significantly increases the efficiency of road network extraction.

Road Extraction Convolutional Neural Network with Embedded Attention Mechanism for Remote Sensing Imagery

Roads are closely related to people’s lives, and road network extraction has become one of the most important remote sensing tasks. This study aimed to propose a road extraction network with an embedded attention mechanism to solve the problem of automatic extraction of road networks from a large number of remote sensing images. Channel attention mechanism and spatial attention mechanism were introduced to enhance the use of spectral information and spatial information based on the U-Net framework. Moreover, residual densely connected blocks were introduced to enhance feature reuse and information flow transfer, and a residual dilated convolution module was introduced to extract road network information at different scales. The experimental results showed that the method proposed in this study outperformed the compared algorithms in overall accuracy. This method had fewer false detections, and the extracted roads were closer to ground truth. Ablation experiments showed that the proposed modules could effectively improve road extraction accuracy.

Towards the automated large-scale reconstruction of past road networks from historical maps

Transportation infrastructure, such as road or railroad networks, represent a fundamental component of our civilization. For sustainable planning and informed decision making, a thorough understanding of the long-term evolution of transportation infrastructure such as road networks is crucial. However, spatially explicit, multi-temporal road network data covering large spatial extents are scarce and rarely available prior to the 2000s. Herein, we propose a framework that employs increasingly available scanned and georeferenced historical map series to reconstruct past road networks, by integrating abundant, contemporary road network data and color information extracted from historical maps. Specifically, our method uses contemporary road segments as analytical units and extracts historical roads by inferring their existence in historical map series based on image processing and clustering techniques. We tested our method on over 300,000 road segments representing more than 50,000 km of the road network in the United States, extending across three study areas that cover 42 historical topographic map sheets dated between 1890 and 1950. We evaluated our approach by comparison to other historical datasets and against manually created reference data, achieving F-1 scores of up to 0.95, and showed that the extracted road network statistics are highly plausible over time, i.e., following general growth patterns. We demonstrated that contemporary geospatial data integrated with information extracted from historical map series open up new avenues for the quantitative analysis of long-term urbanization processes and landscape changes far beyond the era of operational remote sensing and digital cartography.