Road Extraction Research Papers - Academia.edu (original) (raw)

2025, IAES International Journal of Robotics and Automation (IJRA)

Existing automatic road map extraction approaches on remote sensing images often fail because they cannot understand the spatial context of an image. Mainly because they could not learn the spatial context of the image and only knew the structure or texture of the image. These approaches only focus on regional accuracy instead of connectivity. Therefore, most approaches produce discontinuous outputs caused by buildings, shadows, and similarity to rivers. This study addresses the challenge of automatic road extraction, focusing on enhancing road connectivity and segmentation accuracy by proposing a network-based road extraction that uses a spatial intensifier module (DULR) and densely connected U-Net architecture (SDUNet) with a connectivity-preserving loss function (CP_clDice) called CP_SDUNet. This study analyzes the CP_clDice loss function for the road extraction task compared to the BCE Loss function to train the SDUNet model. The result shows that CP_SDUNet, performs best using an image size of 128×128 pixels and trained with the whole dataset with a combination of 20% clDice and 80% dice loss. The proposed method obtains a clDice score of 0.85 and an Interest over Union (IoU) score of 0.65 for the testing data. These findings demonstrate the potential of CP_SDUNet for reliable road extraction.

2025

This paper addresses extensions of the classical least-squares matching approaches of (Wrobel, 1987, Ebner and particularly in the direction of full three-dimensional (3D) reconstruction. We use as unknowns the movement in the direction of the normals for a triangulation of the surface. To regularize the ill-posed inverse reconstruction problem, we smooth the surface by enforcing a low curvature in terms of that the vertices of the triangulation are close to the average plane of their direct neighbors. We employ a hierarchy of resolutions for the triangulation linked to adequate levels of image pyramids, to expand the range of convergence, and robust estimation, to deal with occlusions and non-Lambertian reflection. First results using highly precise and reliable, but sparse points from the automatic orientation of images sequences as input for the triangulation show the potential of the approach.

2025

In this paper we propose an approach for automatic road extraction from pan-sharpened IKONOS images which makes use of the 1 m resolution as well as the multispectral information. The approach consists of three main steps and can be seen as an add-on to the approach of Wiedemann ( ). In the first step, training areas for a multispectral classification are extracted in all channels using the Steger differential geometric line operator. Additionally, there have to be parallel edges with a homogeneous area between them within a neighborhood of the extracted lines. In the second step, the training areas are used for a fuzzy multispectral classification. With it, additional road pixels are extracted. The number of misclassifications is minimized by means of a rank filter. In the final step, gaps in the preliminary road network generated with the approach of Wiedemann ( ) are closed by means of ziplock line snakes employing the classification results. Examples show the validity of the approach.

2025, Lecture Notes in Computer Science

The geographic information system industry would benefit from flexible automated systems capable of extracting linear structures from satellite imagery. Quadratic snakes allow global interactions between points along a contour, and are well suited to segmentation of linear structures such as roads. However, a single quadratic snake is unable to extract disconnected road networks and enclosed regions. We propose to use a family of cooperating snakes, which are able to split, merge, and disappear as necessary. We also propose a preprocessing method based on oriented filtering, thresholding, Canny edge detection, and Gradient Vector Flow (GVF) energy. We evaluate the performance of the method in terms of precision and recall in comparison to ground truth data. The family of cooperating snakes consistently outperforms a single snake in a variety of road extraction tasks, and our method for obtaining the GVF is more suitable for road extraction tasks than standard methods.

2024

Reliable and up-to-date road network information is crucial to guarantee efficient logistic distribution, emergency response, urban planning, etc. Road networks in developing urban areas tend to change rapidly. Periodic remapping is necessary to maintain the temporal quality of the road network information. Updating the road network using conventional methods can be a tedious task. This paper presents a methodology to extract road network automatically from an airborne LiDAR point cloud combined with color information from an aerial orthophoto. First, ground points are separated from non-ground points. We then classify the filtered ground points to road and non-road points using the Random Forest (RF) algorithm. Parallel thinning, method for skeletonization of the road segment, is carried out on a binary image extracted by a so-called density map of the classified road points. Finally, road centerline is obtained by our proposed topological order and regularization approach. The pro...

2024, European Transport Research Review

Purpose Large area traffic monitoring with high spatial and temporal resolution is a challenge that cannot be served by today available static infrastructure. Therefore, we present an automatic near real-time traffic monitoring approach using data of an airborne digital camera system with a frame rate of up to 3 fps. Methods By performing direct georeferencing on the obtained aerial images with the use of GPS/IMU data we are able to conduct near real-time traffic data extraction. The traffic processor consists mainly of three steps which are road extraction supported by a priori knowledge of road axes obtained from a road database, vehicle detection by edge extraction, and vehicle tracking based on normalized cross correlation. Results Traffic data is obtained with a correctness of up to 79% at a completeness of 68%. Conclusions With this system we are able to perform area-wide traffic monitoring with high actuality independent from any stationed infrastructure which makes the syste...

2024

Remote sensing refers to the activities of recording, observing, and perceiving (sensing) objects or events in far-away (remote) places. In remote sensing, the sensors are not in direct contact with the objects or events being observed. Electromagnetic radiation normally is used as the information carrier in remote sensing. The output of a remote sensing system is usually an image representing the scene being observed. A further step of image analysis and interpretation is required to extract useful information from the image. In this paper, we will analyze the remote sensing image which can have different types of objects (depends upon the application area). We will identify the object in the given image by using active contour method and after that we will calculate the object‟s statistics. Object statistics deals with different features like area, region, boundary, texture and threshold etc.

2024

Este trabalho propõe um método monoscópico para a determinação automática da altura de edifícios em fotografias aéreas digitais, baseada no deslocamento radial dos pontos projetados no plano-imagem e na geometria no momento da obtenção da fotografia. A determinação da altura de edifícios pode ser utilizada para a modelagem da superfície em áreas urbanas, planejamento e gerenciamento urbano, entre outros. A metodologia proposta emprega um conjunto de etapas para a detecção de bordas dispostas radialmente em relação ao sistema de coordenadas fotogramétrico, sendo que estas bordas caracterizam as arestas laterais verticais dos edifícios presentes numa fotografia. Na primeira etapa realiza-se a redução do espaço de busca por meio da detecção das sombras projetadas pelos edifícios, gerando sub-imagens das áreas no entorno de cada sombra detectada. Em seguida, para cada sub-imagem são extraídas automaticamente bordas que passam por testes de consistência de modo que sejam caracterizados como segmentos de reta dispostos radialmente. Deste modo, a partir destas arestas, e com o conhecimento da altura de vôo, são estimadas as alturas dos edifícios. Os resultados alcançados em experimentos com imagens reais digitalizadas, obtidas com câmaras métricas, mostraram-se promissores para a determinação das alturas de edifícios.

2024

The goal of this research is to extract and delineate building ground plans from LIDAR data. Our approach consists of three steps: first of all, the raw point cloud has to be classified into terrain points and off-terrain points. Secondly, the off-terrain points (the potential buildings) have to be aggregated to form connected building blobs. Those blobs that exceed a certain size and have certain characteristics (e.g. consisting of planes) are supposed to be building candidates. For them, in a third step the outline is simplified. This is a generalization task, which has to take the characteristics of buildings into account to produce a meaningful 2D building shape. In the paper, these steps are described in detail. The focus lies on the different possibilities to generalize the building ground plans.

2024, International Journal of Applied Earth Observation and Geoinformation

In recent years, many approaches have been exploited for automatic road extraction. Most of these approaches are based on edge detection algorithms. In this paper, a new object-based approach for automatic extraction of main roads in large scale imagemaps is proposed. The gray-scale imagemap is converted to a simplified imagemap using Gray scale Morphological Algorithms (GMA). At this point, the proposed algorithm consists of two parallel stages. The first stage deals with straight lines extraction and the second stage deals with roads skeleton extraction. In the first stage, the simplified imagemap is segmented and converted to a binary image. Next, the binary imagemap objects are labeled and then, the straight line segments are extracted. In the second stage, the resolution of the simplified imagemap is reduced so that the width of roads are reduced to 2-3 pixels. Next, the reduced resolution image is converted to a binary image. Then, the skeleton of roads in the binary reduced resolution image is extracted. By combining the results from the two stages, the roadsides are extracted. The skeleton of roads and straight line segments are combined using searching roadside algorithm. The test area was an imagemap with a scale of 1:8000 over the Kish region of Iran. The program for this study is developed in Visual C++ language under Windows 98 operating system.

2024

A possibility to filter airborne laser scanning data is the approximation of the terrain with polynomials. A major issue here is the determination of an appropriate degree of the polynomial. The quality of the approximation depends directly on this polynomial degree. In this article we present a method where the terrain is approximated using polynomials of different degree at the same time. To show the capability of the methods some exemplary results are compared to the results of a traditional, polynomial based terrain approximation.

2024, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing

Moving vehicle detection is very important for transportation management and traffic monitoring. Due to the submeter spatial resolution of very high resolution (VHR) imagery, vehicles can be identified from this type of imagery. Furthermore, because of the slight time difference between image acquisition of onboard sensors, (i.e. Pan and MS sensors) in VHR satellite such as Quickbird and GeoEye-1, a moving vehicle is observed, by the satellite, at two different locations. Consequently, moving vehicles can be distinguished from the stationary ones by applying a proper change detection algorithm. WorldView2 possess three sensors, i.e. a Pan and two MS sensors (MS1 and MS2). Therefore, a moving vehicle is observed at three different locations. This feature together with the new spectral bands of WV2 adds opportunity to improve moving vehicle detection and extraction. This paper, utilizing an object-based framework, compares the automatic moving vehicle extraction by using the three pairs of WV2 sensors (i.e. Pan-MS1, Pan-Ms2 and MS1-MS2). The results show that of three image pairs, the MS1-MS2 is the best choice for moving vehicle extraction because of the larger time lag between MS1 and MS2 than between the Pan and MS1 or MS2.

2024

2024, JES. Journal of Engineering Sciences

Roads extraction from VHR satellite images are very paramount for GIS and map updating. Due to the high resolution of satellite images, there are many obstacles broken roads such as shadow, and vehicles. The present work aims to find the most suitable road extraction approach that can be applied in the Egyptian environment. In this study, two satellite images from WorldView-2 and WorldView-3 were used. Classification of image by pixel-based and object-based was carried out to find the appropriate classification method for road extraction. Then, road class refinement by morphology and angular texture signature are performed to decrease the misclassifications between roads and other spectrally similar objects. After that, an iterative and localized Hough transform method was compared with the thinning algorithm method to find the proper method that can extract road centerline segments from the refined images. The performance of the extracted roads was estimated by using the common metrics; completeness, correctness, and quality. The results of this work demonstrate that the random tree in object-based classification achieves the highest overall accuracy than other classification methods. Also, thinning algorithm has more advantages than Hough transform.

2024, Indonesian Journal of Electrical Engineering and Computer Science

Accurate road extraction from remote sensing images is a challenging task. Several methods of extraction have been developed but the precision of extraction is still limited for the unpaved and small-width roads. This paper proposes an accurate road extraction approach called DAA-SSEG since it uses data augmentation architecture (DAA) and semantic segmentation model (SSEG). The proposed approach DAA-SSEG is based on a modified full convolutional neural network that overcomes the vanishing gradient and the training saturation issues. It recognizes roads at the pixel level. Furthermore, The DAA-SSEG approach uses a new plan of data augmentation based on geometric transformation and images refinement techniques. It allows getting a richer dataset thus better training and an accurate extraction. The experiment denotes that the proposed approach DAA-SSEG, that combine data augmentation architecture and semantic segmentation method, outperforms some state-of-the-art methods in terms of F-...

2024, ISPRS International Journal of Geo-Information

The 3D information of road infrastructures is growing in importance with the development of autonomous driving. In this context, the exact 2D position of road markings as well as height information play an important role in, e.g., lane-accurate self-localization of autonomous vehicles. In this paper, the overall task is divided into an automatic segmentation followed by a refined 3D reconstruction. For the segmentation task, we applied a wavelet-enhanced fully convolutional network on multiview high-resolution aerial imagery. Based on the resulting 2D segments in the original images, we propose a successive workflow for the 3D reconstruction of road markings based on a least-squares line-fitting in multiview imagery. The 3D reconstruction exploits the line character of road markings with the aim to optimize the best 3D line location by minimizing the distance from its back projection to the detected 2D line in all the covering images. Results showed an improved IoU of the automatic ...

2024, Journal of Machine Vision and Applications

We will discuss how hypotheses of geomemc structure can be generated in an active computer vision system. These hypotheses are built on sparse but reliable observations and can communicate with higher level hypotheses as well as the basic classified data hypotheses. We present experiments with such hypotheses, where we have chosen to form hypotheses about the existence of planar surfaces in a scene, given classified edges and junctions. The results of the experiments illustrate clearly the benefit of the approach. A goal directed active vision system could easily use this idea to make both vowerful verifications of generated predictions as well as hew hypotheses and predictions at different level of abstraction.

2024, Intelligent Decision Technologies

The novel approach for automatic detection and classification of road defects is proposed based on shape and texture features analysis. The system includes three main steps: defects position detection, feature contour extraction followed by classification of defects. The proposed approach is implemented in Matlab for automatic detection and classification of defects based on digital images analysis combined with machine learning algorithms such as the random forest algorithm and boosting. Segmentation is implemented using graph-cuts method and Markov random fields. The efficiency of proposed approach is demonstrated on the real data set.

2024, Remote Sensing

A serious earthquake could trigger thousands of landslides and produce some slopes more sensitive to slide in future. Landslides could threaten human’s lives and properties, and thus mapping the post-earthquake landslide susceptibility is very valuable for a rapid response to landslide disasters in terms of relief resource allocation and posterior earthquake reconstruction. Previous researchers have proposed many methods to map landslide susceptibility but seldom considered the spatial structure information of the factors that influence a slide. In this study, we first developed a U-net like model suitable for mapping post-earthquake landslide susceptibility. The post-earthquake high spatial airborne images were used for producing a landslide inventory. Pre-earthquake Landsat TM (Thematic Mapper) images and the influencing factors such as digital elevation model (DEM), slope, aspect, multi-scale topographic position index (mTPI), lithology, fault, road network, streams network, and ...

2024, 2007 IEEE International Conference on Image Processing

In this paper, an automatic road tracking method is presented for detecting roads from satellite images. This method is based on shape classification of a local homogeneous region around a pixel. The local homogeneous region is enclosed by a polygon, called the pixel footprint. We introduce a spoke wheel operator to obtain the pixel footprint and propose a Fourier-based approach to classify footprints for automatic seeding and growing of the road tracker. We experimentally demonstrate that our proposed road tracker can extract the centerlines of roads with sharp turns and intersections effectively, and has relatively small amount of leakage.

2024, IEEE Transactions on Geoscience and Remote Sensing

2024, Remote Sensing

Rapid global warming is catalyzing widespread permafrost degradation in the Arctic, leading to destructive land-surface subsidence that destabilizes and deforms the ground. Consequently, human-built infrastructure constructed upon permafrost is currently at major risk of structural failure. Risk assessment frameworks that attempt to study this issue assume that precise information on the location and extent of infrastructure is known. However, complete, high-quality, uniform geospatial datasets of built infrastructure that are readily available for such scientific studies are lacking. While imagery-enabled mapping can fill this knowledge gap, the small size of individual structures and vast geographical extent of the Arctic necessitate large volumes of very high spatial resolution remote sensing imagery. Transforming this ‘big’ imagery data into ‘science-ready’ information demands highly automated image analysis pipelines driven by advanced computer vision algorithms. Despite this, ...

2024

Roads are important objects for many applications of topographic data. They are often acquired manually and as this entails significant effort, automation is highly desirable. Deficits in the automatic extraction hindering a wide-scale practical use have led to the idea of setting-up a EuroSDR test comparing different approaches for automatic road extraction. The goal is to show the potential of the state-of-the-art approaches as well as to identify promising directions for research and development. After describing the data and the evaluation criteria used, we present the approaches of a number of groups which have submitted results and give a detailed discussion of the outcome of the evaluation of the submitted results. We finally present a summary and conclusions.

2024

The appearance of roads in northern Africa differs from that of roads, e.g., in central Europe, which most of the approaches for automated road extraction in literature focus on. In this paper we propose a road model for areas with different road appearance in IRS satellite image data with a panchromatic resolution of 5 m and 20 m multispectral resolution. We model areas where water makes agriculture possible on one hand, and areas dominated by the desert and dry mountainous areas on the other hand. In the desert and mountainous areas paved roads appear as more or less distinct lines and the Steger line extraction algorithm can be used to extract roads in combination with global grouping. In mountainous areas detected, e.g., in a DEM, much larger curvatures are expected to occur than in the desert. In agricultural areas, on which we focus in this paper, roads often do not appear as distinct lines. Borders of the fields represented by edges in the image and the knowledge that these borders can be collinearly grouped, possibly together with lines, into longer linear structures are used to construct road sections. To close gaps, pairs of lines or edges are connected by ziplock snakes. To verify these road sections, the paths of the snakes are evaluated using the line strength and the gradient image. The verified road sections are finally globally grouped using the knowledge that roads construct a network between important points. Gaps which have a high impact on the network topology are closed if evidence supporting this is found in the image. Results show the validity of the approach.

2024, … Archives of the Photogrammetry, Remote Sensing …

In this paper we propose an approach for automatic road extraction from high resolution multispectral imagery, such as IKONOS or Quickbird, in rural areas. While aerial imagery usually consists of 3 spectral bands, high resolution... more

2024

In this paper we propose an approach for automatic road extraction from pan-sharpened IKONOS images which makes use of the 1 m resolution as well as the multispectral information. The approach consists of three main steps and can be seen as an add-on to the approach of Wiedemann (2001). In the first step, training areas for a multispectral classification are extracted in all channels using the Steger differential geometric line operator. Additionally, there have to be parallel edges with a homogeneous area between them within a neighborhood of the extracted lines. In the second step, the training areas are used for a fuzzy multispectral classification. With it, additional road pixels are extracted. The number of misclassifications is minimized by means of a rank filter. In the final step, gaps in the preliminary road network generated with the approach of Wiedemann (2001) are closed by means of ziplock line snakes employing the classification results. Examples show the validity of the approach.

2024

In this work a method is proposed to allow the indirect orientation of images using photogrammetric control extracted through integration of data derived from Photogrammetry and Light Detection and Ranging (LiDAR) system. The photogrammetric control is obtained by using an inverse photogrammetric model, which allows the projection of image space straight lines onto the object space. This mathematical model is developed based on the intersection between the collinearity-based straight line and a DSM of region, derived from LiDAR data. The mathematical model used in the indirect orientation of the image is known as the model of equivalent t planes. This mathematical model is based on the equivalence between the vector normal to the projection plane in the image space and to the vector normal to the rotated projection plane in the object space. The goal of this work is to verify the quality, efficiency and potential of photogrammetric control straight lines obtained with proposed method applied to the indirect orientation of images. The quality of generated photogrammetric control was statistically available and the results showed that proposed method is promising and it has potential for the indirect orientation of images.

2024, Indonesian Journal of Electrical Engineering and Computer Science

2024, Proceedings of the ICA

Label placement is a tedious task in map design, and its automation has long been a goal for researchers in cartography, but also in computational geometry. Methods that search for an optimal or nearly optimal solution that satisfies a set of constraints, such as label overlapping, have been proposed in the literature. Most of these methods mainly focus on finding the optimal position for a given set of labels, but rarely allow the removal of labels as part of the optimization. This paper proposes to apply an optimization technique called Reversible-Jump Markov Chain Monte Carlo that enables to easily model the removal or addition during the optimization iterations. The method, quite preliminary for now, is tested on a real dataset, and the first results are encouraging.

2024, The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences

Immense use of topographical data in spatial data visualization, business GIS (Geographic Information Systems) solutions and applications, mobile and location-based services forced the topo-data providers to create standard, up-to-date and complete data sets in a sustainable frame. Data quality has been studied and researched for more than two decades. There have been un-countable numbers of references on its semantics, its conceptual logical and representations and many applications on spatial databases and GIS. However, there is a gap between research and practice in the sense of spatial data quality which increases the costs and decreases the efficiency of data production. Spatial data quality is well-known by academia and industry but usually in different context. The research on spatial data quality stated several issues having practical use such as descriptive information, metadata, fulfillment of spatial relationships among data, integrity measures, geometric constraints etc. The industry and data producers realize them in three stages; pre-, co-and post data capturing. The pre-data capturing stage covers semantic modelling, data definition, cataloguing, modelling, data dictionary and schema creation processes. The co-data capturing stage covers general rules of spatial relationships, data and model specific rules such as topologic and model building relationships, geometric threshold, data extraction guidelines, object-object, object-belonging class, object-non-belonging class, class-class relationships to be taken into account during data capturing. And post-data capturing stage covers specified QC (quality check) benchmarks and checking compliance to general and specific rules. The vector data quality criteria are different from the views of producers and users. But these criteria are generally driven by the needs, expectations and feedbacks of the users. This paper presents a practical method which closes the gap between theory and practice. Development of spatial data quality concepts into developments and application requires existence of conceptual, logical and most importantly physical existence of data model, rules and knowledge of realization in a form of geo-spatial data. The applicable metrics and thresholds are determined on this concrete base. This study discusses application of geo-spatial data quality issues and QA (quality assurance) and QC procedures in the topographic data production. Firstly we introduce MGCP (Multinational Geospatial Co-production Program) data profile of NATO (North Atlantic Treaty Organization) DFDD (DGIWG Feature Data Dictionary), the requirements of data owner, the view of data producers for both data capturing and QC and finally QA to fulfil user needs. Then, our practical and new approach which divides the quality into three phases is introduced. Finally, implementation of our approach to accomplish metrics, measures and thresholds of quality definitions is discussed. In this paper, especially geometry and semantics quality and quality control procedures that can be performed by the producers are discussed. Some applicable best-practices that we experienced on techniques of quality control, defining regulations that define the objectives and data production procedures are given in the final remarks. These quality control procedures should include the visual checks over the source data, captured vector data and printouts, some automatic checks that can be performed by software and some semi-automatic checks by the interaction with quality control personnel. Finally, these quality control procedures should ensure the geometric, semantic, attribution and metadata quality of vector data.

2023, Mathematical Problems in Engineering

Automatic detection and monitoring of the condition of cracks in the road surface are essential elements to ensure road safety and quality of service. A crack detection method based on wavelet transforms (2D-DWT) and Jerman enhancement filter is used. This paper presents different contributions corresponding to the three phases of the proposed system. The first phase presents the contrast enhancement technique to improve the quality of roads surface image. The second phase proposes an effective detection algorithm using discrete wavelet (2D-DWT) with “db8” and two-level sub-band decomposition. Finally, in the third phase, the Jerman enhancement filter is usually used with different parameters of the control response uniformity “ τ ” to enhance for cracks detection. The experimental results in this article provide very powerful results and the comparisons with five existing methods show the effectiveness of the proposed technique to validate the recognition of surface cracks.

2023, Journal of The Indian Society of Remote Sensing

Manual extraction of road network by human operator is an expensive and time-consuming procedure. Alternatively, automation of the extraction process would be a great advancement. For this purpose, an automatic method is proposed to... more

2023, Remote Sensing

Immediately after an earthquake, rapid disaster management is the main challenge for relevant organizations. While satellite images have been used in the past two decades for building-damage mapping, they have rarely been utilized for the timely damage monitoring required for rescue operations. Unmanned aerial vehicles (UAVs) have recently become very popular due to their agile deployment to sites, super-high spatial resolution, and relatively low operating cost. This paper proposes a novel deep-learning-based method for rapid post-earthquake building damage detection. The method detects damages in four levels and consists of three steps. First, three different feature types—non-deep, deep, and their fusion—are investigated to determine the optimal feature extraction method. A “one-epoch convolutional autoencoder (OECAE)” is used to extract deep features from non-deep features. Then, a rule-based procedure is designed for the automatic selection of the proper training samples requir...

2023, Proceedings of the 3rd International Conference on Geographical Information Systems Theory, Applications and Management

The issue of regular spatial databases updating is partly solved by the abundance of satellite images. It is, though, time consuming, requires qualified human resources, high financial costs and requests efficiency (Bernard, 2007). This article presents a semi-automatic tool for urban detection, to guide the stakeholders and the producers throughout the updating process. The industrial context of the study implies a fast, instantaneous applicative workflow, operational on various landscapes with different sensors; it is thus based on existing algorithms and software resources. The process is generic and adaptable, with a phase of uncorrelation, chaining a Minimum Noise Fraction transformation with a textural analysis, a learning phase, processed from an existing database, and an automatic modelling of the detected objects. The quantification of the results shows the successful recreation of the existing database (90% of its surface) with a 7% rate of potential big omissions. A specific highlight is made on the detection of disappeared buildings, corresponding to 17.5% of the potential important omissions. This process has run in "real" updating operations, on 1.5 and 6 meters resolution Spot6 images, a 15 meters Landsat-8 image and a 1.5 meters resolution Pleiades image. A quantification of its results is also proposed in this study.

2023, ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences

Transportation agencies require up-to-date, reliable, and feasibly acquired information on road geometry and features within proximity to the roads as input for evaluating and prioritizing new or improvement road projects. The information needed for a robust evaluation of road projects includes road centerline, width, and extent together with the average grade, cross-sections, and obstructions near the travelled way. Remote sensing is equipped with a large collection of data and well-established tools for acquiring the information and extracting aforementioned various road features at various levels and scopes. Even with many remote sensing data and methods available for road extraction, transportation operation requires more than the centerlines. Acquiring information that is spatially coherent at the operational level for the entire road system is challenging and needs multiple data sources to be integrated. In the presented study, we established a framework that used data from multiple sources, including one-foot resolution color infrared orthophotos, airborne LiDAR point clouds, and existing spatially non-accurate ancillary road networks. We were able to extract 90.25% of a total of 23.6 miles of road networks together with estimated road width, average grade along the road, and cross sections at specified intervals. Also, we have extracted buildings and vegetation within a predetermined proximity to the extracted road extent. 90.6% of 107 existing buildings were correctly identified with 31% false detection rate.

2023, Indonesian Journal of Electrical Engineering and Computer Science

2023, Remote Sensing

2023

The paper concerns the generalization of DTM extracted from LiDAR data. The essence of generalization is reducing details while enhancing important features at the same time; so for the purpose of terrain surface visualization special attention has to be given to the enhancement and generalization of topographic objects like dams, roads etc. The focus of this work is laid on the extraction of road objects and their contribution into the enhancement of the generalized terrain model. An algorithm for the extraction of roads is developed and is followed by a generalization algorithm that weights together road networks and filtered LiDAR point clouds. Following the presentation of the algorithm results for this approach are shown and evaluated.

2023, ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences

Nowadays coastline extraction and tracking of its changes become of high importance because of the climate change, global warming and rapid growth of human population. Coastal areas play a significant role for the economy of the entire region. In this paper we propose a new methodology for automatic extraction of the coastline using aerial images. A combination of a four step algorithm is used to extract the coastline in a robust and generalizable way. First, noise distortion is reduced in order to ameliorate the input data for the next processing steps. Then, the image is segmented into two regions, land and sea, through the application of a local threshold to create the binary image. The result is further processed by morphological operators with the aim that small objects are being eliminated and only the objects of interest are preserved. Finally, we perform edge detection and active contours fitting in order to extract and model the coastline. These algorithmic steps are illustrated through examples, which demonstrate the efficacy of the proposed methodology.

2023, Journal of Applied Sciences

2023, IEEE Transactions on Geoscience and Remote Sensing

Extended Kalman filter (EKF) has previously been employed to extract road maps in satellite images. This filter traces a single road until a stopping criterion is satisfied. In our new approach, we have combined EKF with a special particle filter (PF) in order to regain the trace of the road beyond obstacles, as well as to find and follow different road branches after reaching to a road junction. In this approach, first, EKF traces a road until a stopping criterion is met. Then, instead of terminating the process, the results are passed to the PF algorithm which tries to find the continuation of the road after a possible obstacle or to identify all possible road branches that might exist on the other side of a road junction. For further improvement, we have modified the procedure for obtaining the measurements by decoupling this process from the current state prediction of the filter. Removing the dependence of the measurement data to the predicted state reduces the potential for instability of the road-tracing algorithm. Furthermore, we have constructed a method for dynamic clustering of the road profiles in order to maintain tracking when the road profile undergoes some variations due to changes in the road width and intensity.

2023, Revista Brasileira de Cartografia

Este artigo propõe uma nova metodologia para a extração de rodovias a partir de imagens digitais. A inovação nesta metodologia está relacionada com o uso do algoritmo de programação dinâmica (PD) para realizar um processo de otimização no espaço objeto, em vez de fazê-lo no espaço imagem, conforme as metodologias tradicionais baseadas em PD. O princípio básico da metodologia proposta é inspirado no funcionamento dos restituidores fotogramétricos. Em ambos os casos, as feições são traçadas no espaço objeto, implicando na necessidade de se estabelecer uma rigorosa relação matemática entre pontos dos espaços objeto e imagem. Entretanto, a metodologia proposta não necessita da participação do operador na compilação das feições no espaço objeto. O operador participa apenas na medição no espaço imagem de alguns pontos sementes descrevendo esparsa e grosseiramente as rodovias, os quais devem ser transformados para o espaço objeto para possibilitar a inicialização do processo de otimização por PD. Uma das grandes vantagens da metodologia proposta é sua flexibilidade, podendo operar em diferentes modos (mono ou estéreo) e com variados tipos de imagens, incluindo o processamento de imagens multisensor. Este artigo apresenta os fundamentos da nova metodologia de extração de rodovias no espaço objeto, bem como apresenta em detalhes a versão da metodologia baseada em uma única imagem, juntamente com os resultados experimentais.

2023, Boletim De Ciencias Geodesicas

The purpose of this paper is to introduce a methodology for semi-automatic road extraction from aerial digital image pairs by using dynamic programming and epipolar geometry. The method uses both images from where each road feature pair is extracted. The operator identifies the corresponding road features and s/he selects sparse seed points along them. After all road pairs have been extracted, epipolar geometry is applied to determine the automatic point-to-point correspondence between each correspondent feature. Finally, each correspondent road pair is georeferenced by photogrammetric intersection. Experiments were made with rural aerial images. The results led to the conclusion that the methodology is robust and efficient, even in the presence of shadows of trees and buildings or other irregularities.

2023, Applied Computing and Informatics

We make use of the Volunteered Geographic Information (VGI) data to extract the total extent of the roads using remote sensing images. VGI data is often provided only as vector data represented by lines and not as full extent. Also, high geolocation accuracy is not guaranteed and it is common to observe misalignment with the target road segments by several pixels on the images. In this work, we use the prior information provided by the VGI and extract the full road extent even if there is significant mis-registration between the VGI and the image. The method consists of image segmentation and traversal of multiple agents along available VGI information. First, we perform image segmentation, and then we traverse through the fragmented road segments using autonomous agents to obtain a complete road map in a semi-automatic way once the seed-points are defined. The road center-line in the VGI guides the process and allows us to discover and extract the full extent of the road network based on the image data. The results demonstrate the validity and good performance of the proposed method for road extraction that reflects the actual road width despite the presence of disturbances such as shadows, cars and trees which shows the efficiency of the fusion of the VGI and satellite images.

2023, Photogrammetric Engineering and Remote Sensing

An accurate inventory of unpaved road network length and condition within a county, state, or region is important for efficient use of resources to manage and maintain this critical transportation asset. Object-based classification techniques provide a cost-effective way to identify unpaved roads within a local agency's road network when the road type (i.e., paved versus unpaved) attribute is missing. We present a Trimble eCognition® algorithm using four band optical aerial imagery and object-based classification to classify roads as paved or unpaved. The ruleset evaluates relationships between bands and allows separation and segmentation of unpaved roads from other pavement classes. The algorithm is applied to unincorporated areas of a six county region in Southeastern Michigan. Tree shadows on roads and the spectral similarity of road construction materials pose challenges to classification accuracy. An accuracy assessment of the classification indicated that the algorithm works well with overall classification accuracy between 82 and 94 percent.

2023, alexandria engineering journal

2023, Abstract of applied sciences and engineering

2023

The aim of this study is to develop automatic road extraction algorithm in satellite images. As roads have different width and surface material characteristics in urban and rural areas, a modular approach for road extraction algorithm is desired. In this study, edge detection, segmentation, clustering and vegetation and land cover analyses are used. In order to combine the results of different methods, a score map based on segmentation analysis is constructed. Quantitative and visual results show that this method is successful in road extraction from satellite images.