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Results in Journal The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences: 11,034

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N. A. Andriyanov, A. A. Lutfullina
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, pp 1-5; doi:10.5194/isprs-archives-xliv-2-w1-2021-1-2021

Today, possibilities of artificial intelligence allow us to see the emergence of autonomous cars. However, there are still many problems in this area at present. Often, such vehicles are “too slow to think”, are not able to reliably process data from video cameras in the event of reflections, glare, and there are also questions about the safety of such driving in difficult weather conditions or in heavy traffic. At the same time, the human factor plays a major role in accidents of driven vehicles. Many accidents involve driver fatigue, distraction, or even falling asleep. At the same time, it is potentially possible to monitor the state of a person behind the wheel by a video sequence received from a camera installed in the car's interior and registering the driver's face in video sequence. In this paper, the existing databases of images of faces and eyes are considered, and an algorithm is presented that detects the state of closed eyes based on Haar detectors and convolutional neural networks.
A. G. Zotin, A. V. Proskurin
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, pp 213-219; doi:10.5194/isprs-archives-xliv-2-w1-2021-213-2021

In recent years, digital watermarking of photo and video materials has become more and more important in connection with the transmission of multimedia data over unsecured communication channels. Digital watermarking allows to reduce the amount of transmitted information and to protect embedded metadata. Improving robustness and security of embedded data increases computational costs, which obstruct usage of digital watermarks in mobile devices. In this research, we propose a number of improvements to the digital watermarking process based on Arnold and discrete wavelet transforms to reduce the computational cost. Considering the watermark as a linear sequence of pixels allows us to speed up its processing. The two-dimensional lookup table allows performing an Arnold transform in constant time regardless of the number of iterations. Number of iteration for each block of watermark is determined using hash function applied to the secret key. Also, the structure of the lookup table is proposed to accelerate the embedding of watermark. This table allows to determine the frequency coefficients for embedding based on the key hash code. Proposed improvements allow to speed up the watermark preparation by an average 14 times and the overall embedding process by 1.22 times for 1920×1080 images.
A. V. Gaboutchian, V. A. Knyaz, S. V. Apresyan, M. S. Navrazhnykh, S. V. Vasyliev
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, pp 55-60; doi:10.5194/isprs-archives-xliv-2-w1-2021-55-2021

Over the past few years closer cooperation can be observed in various aspects of digital techniques in such disciplines as dentistry and anthropology. And in most cases that consists in imaging and image processing which results in obtaining 3D reconstructions. And indeed, they can significantly improve research and practice. Thus, depending on imaging technique and application, they can support CAD/CAM technology or precisely reconstruct morphology of invisible structures. However the currently presented study refers to technical aspects of shade and texture mapping, which is more aimed to obtain more realistic 3D reconstructions of palaeoanthropological material. Colour or shade matching has become an integral part of dental practice. It can be carried in a traditional manner though matching the tooth with conventional shade-guides, or, which is in line with the subjects of our study, by means of spectrophotometry. And the main procedures of shade detection have been performed by SpectroShade (MHT). Necessary attention has been paid to conditions influencing shade detection process with respect to the studied material teeth taken from the Bronze Age findings. Reconstructive techniques have traditionally been a scientific and practical part of palaeoanthropological research which is directed at appearance reconstruction. Though the leading part of this branch has been always aimed at analysis of skull morphology. In our time of rapidly developing digital techniques reconstructions have become to a large extent a matter of improvements of imaging and image processing techniques. Even though this doesnt directly refer to soft tissue reconstruction, it undoubtedly applies to dental reconstructions. And the current study presents improvements in reconstruction through combining imaging with shade and texture mapping on 3D models of teeth.
V. Kozlov, A. Maysuradze
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, pp 149-154; doi:10.5194/isprs-archives-xliv-2-w1-2021-149-2021

Part-based object representation and part matching problem often appear in various areas of data analysis. A special case of particular interest is when parts are not fully separated, but in relations with each other. The natural way to model such objects are graphs, and part matching problem becomes graph matching problem. Over the years, many methods to solve graph matching problems have been proposed, but it remains relevant due to its complexity. We propose a novel approach to solving graph matching problem based on learning distance metric on graph vertices. We empirically demonstrate that our method outperforms traditional methods based on solving quadratic assignment problem. We also provide an theoretical estimation of computational complexity of proposed method.
E. Farazdaghi, M. Eslahi, R. El Meouche
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, pp 41-45; doi:10.5194/isprs-archives-xliv-2-w1-2021-41-2021

The human desire to live in an urban area increases every day. However, citizens’ expectation of urban life is very different compared to the past. It is, thus, essential to satisfy their requirements and ensure their safety within their cities. As a result, there is a huge trend in the implementation of smart cities around the world. A smart city is a solution to improve the quality of life of the citizens, and governing the city in an efficient and systematic. Besides, significant advances have been raised in biometrics technologies, which have made many aspects of urban life easier, more efficient, and more secure. Accordingly, to be compliant with the demands of a smart city in the future, biometrics-based technologies will certainly play a significant role from now on. Thus, it is necessary to list the different biometrics methods that could be used in smart cities and to review the variety of applications for each method. In this article, we have listed the potential biometrics systems that can be employed in smart cities, such as facial recognition, age estimation, gender detection, facial expression detection and sentiment recognition, and gait recognition. We also have listed different applications imagined for each biometrics system such as their application in identification systems and security, smart healthcare, smart advertising, education, and high-risk lifestyle behaviours prevention. We believe that this work can help to better use of these methods, improve their technical quality, and also employing them in the advance and more effective ways.
V. V. Kniaz, P. Moshkantseva
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, pp 131-136; doi:10.5194/isprs-archives-xliv-2-w1-2021-131-2021

Object Re-Identification (ReID) is the task of matching a given object in the new environment with its image captured in a different environment. The input for a ReID method includes two sets of images. The probe set includes one or more images of the object that must be identified in the new environment. The gallery set includes images that may contain the object from the probe image. The ReID task’s complexity arises from the differences in the object appearance in the probe and gallery sets. Such difference may originate from changes in illumination or viewpoint locations for multiple cameras that capture images in the probe and gallery sets. This paper focuses on developing a deep learning ThermalReID framework for cross-modality object ReID in thermal images. Our framework aims to provide continuous object detection and re-identification while monitoring a region from a UAV. Given an input probe image captured in the visible range, our ThermalReID framework detects objects in a thermal image and performs the ReID. We evaluate our ThermalReID framework and modern baselines using various metrics. We use the IoU and mAP metrics for the object detection task. We use the cumulative matching characteristic (CMC) curves and normalized area-under-curve (nAUC) for the ReID task. The evaluation demonstrated encouraging results and proved that our ThermalReID framework outperforms existing baselines in the ReID accuracy. Furthermore, we demonstrated that the fusion of the semantic data with the input thermal gallery image increases the object detection and localization scores. We developed the ThermalReID framework for cross-modality object re-identification. We evaluated our framework and two modern baselines on the task of object ReID for four object classes. Our framework successfully performs object ReID in the thermal gallery image from the color probe image. The evaluation using real and synthetic data demonstrated that our ThermalReID framework increases the ReID accuracy compared to modern ReID baselines.
A. V. Gaboutchian, V. A. Knyaz, H. Y. Simonyan, G. R. Petrosyan, D. V. Korost, M. M. Novikov, A. A. Kudaev, S. A. Cherebylo, N. V. Kharlamova
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, pp 73-77; doi:10.5194/isprs-archives-xliv-2-w1-2021-73-2021

Findings from Bronze Age burials of Shengavit settlement have become a source of multiple studies referred to anthropological, and especially odontological, research based on 3d imaging and image processing techniques. The currently presented case is an example of palaeopathological analysis of bone tissue resorption caused by complications of dental pathologies. Thus by analogy with diagnostic procedures in clinical dentistry, conventional x-ray based cone-beam tomographic scanning have been applied and have shown its effectiveness as a study method. Through CBCT imaging we managed to reveal a hidden pathological process in the body of the studied semi-mandible fragment, though initially another pathological area located on the same finding was planned to be studied. Application of micro-computed tomography has improved analytical, or diagnostic, part of the current palaepathological study. It has brought to finding unusual morphological features hypothetically causing bone resorption as a complication of dental pathological conditions. However our intention to obtain 3d reconstructions as evidence supporting the most likely version required several attempts to correct image processing in line with the increase of imaging resolution.
A. S. Kents, Y. A. Hamad, K. V. Simonov, A. G. Zotin
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, pp 99-105; doi:10.5194/isprs-archives-xliv-2-w1-2021-99-2021

In recent years computed tomography of the lungs has been the most common diagnostic procedure aimed at detection of the pathological changes associated with COVID-19. The study is aimed at the use of the developed algorithmic support in combination with texture (geometric) analysis to highlight a number of indicators characterizing the clinical state of the object of interest. Processing is aimed at the solution of a number of diagnostic tasks such as highlighting and contrasting the objects of interest, taking into account the color coding. Further, an assessment is performed according to the appropriate criteria in order to find out the nature of the changes and increase both the visualization of pathological changes and the accuracy of the X-ray diagnostic report. For these purposes, it is proposed to use preprocessing algorithms for a series of images in dynamics. Segmentation of the lungs and areas of possible pathology are performed using wavelet transform and Otsu threshold value. Delta-maps and maps obtained using Shearlet transform with contrasting color coding are used as a means of visualization and selection of features (markers). The analysis of the experimental and clinical material carried out in the work shows the effectiveness of the proposed combination of methods for studying of the variability of the internal geometric features (markers) of the object of interest in the images.
V. A. Mizginov, V. V. Kniaz, N. A. Fomin
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, pp 155-162; doi:10.5194/isprs-archives-xliv-2-w1-2021-155-2021

The active development of neural network technologies and optoelectronic systems has led to the introduction of computer vision technologies in various fields of science and technology. Deep learning made it possible to solve complex problems that a person had not been able to solve before. The use of multi-spectral optical systems has significantly expanded the field of application of video systems. Tasks such as image recognition, object re-identification, video surveillance require high accuracy, speed and reliability. These qualities are provided by algorithms based on deep convolutional neural networks. However, they require to have large databases of multi-spectral images of various objects to achieve state-of-the-art results. While large and various databases of color images of different objects are widely available in public domain, then similar databases of thermal images are either not available, or they represent a small number of types of objects. The quality of three-dimensional modeling for the thermal imaging spectral range remains at an insufficient level for solving a number of important tasks, which require high precision and reliability. The realistic synthesis of thermal images is especially important due to the complexity and high cost of obtaining real data. This paper is focused on the development of a method for synthesizing thermal imaging images based on generative adversarial neural networks. We developed an algorithm for a multi-spectral image-to-image translation. We have changed to the original GAN architecture and converted the loss function. We presented a new learning approach. For this, we prepared a special training dataset including about 2000 image tensors. The evaluation of the results obtained showed that the proposed method can be used to expand the available databases of thermal images.
M. M. Knyazkov, A. V. Polyakov, V. M. Usov
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, pp 143-147; doi:10.5194/isprs-archives-xliv-2-w1-2021-143-2021

In case of COVID-19 epidemic spread the requirements for protection of medical personnel were increased. This category of specialists has exposure of high risk of COVID-19, due to inevitable numerous contacts with infected persons.Because of this, existing practicable models of medical care are needed to be upgraded (Tavakoli, 2020). The emergency response path was implemented through the opening of new infectious hospitals, re-profiling clinics, as well as increasing workload on medical personnel. This approach is associated with the possible rapid drop-up of qualified medical specialists due to the illness, which is a strong limiting factor to respond to new threats of COVID-19 due to the risks of exhaustion of the human resource. In the worst scenario, a threat of collapse of the emergency and specialized medical care system due to peak load of severe patients under the shortage of doctors and support personnel. To prevent such an emergency, a complex of anti-epidemic events is provided, most of the purpose of interrupting infection contacts, isolation of the most vulnerable contingents, extended population testing for virus or contact with infected persons, this allows you to follow unwanted contacts with the subsequent mobility limitation.Digital monitoring technologies with digitization of incoming data of significant events played the increasing role in all these options. Today they are complemented by robotic supporting. Due to the high risk of the infection in the COVID-19 spread, including the intra-clinic infections, the requirements for reliable disinfection of closed premises intended for patients and medical personnel should be fulfilled. At the same time, the following circumstance should be taken into account. When these work-time activities are imposed on the employee who is forced to stay long in an infected air environment, he is subjected to additional impact of pathogenes. To reduce these threats, robotic support for the work for disinfection of premises with automated air environment control and tracing contacts.
A. E. Bondarev, A. V. Bondarenko, V. A. Galaktionov
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, pp 21-26; doi:10.5194/isprs-archives-xliv-2-w1-2021-21-2021

The presented research considers the problems of studying the cluster structure of multidimensional data volumes. This paper presents the results of numerical experiments on the study of data volumes consisting of frequencies of joint use of words from different parts of speech, for instance “noun + verb” or “adjective + noun”. The volumes of data are obtained from samples from text collections in Russian. The aim of the research is to analyze the cluster structure of the studied volume and semantic proximity of words in clusters and subclusters. The hypothesis was used that words with similar meaning should occur in approximately the same context. In this regard, in the space of features, they will be at a relatively close distance from each other, while differing words will be at a more distant distance from each other. Research is carried out using elastic maps, which are effective tools for visual analysis of multidimensional data. The construction of elastic maps and their extensions in the space of the first three principal components makes it possible to determine the cluster structure of the studied multidimensional data volumes. Such analysis can be useful in the tasks of confronting negative verbal influences such as fake news, hidden propaganda, involvement in sects, verbal manipulation, etc. Also this approach can be applied to text collections having medical origin.
M. N. Favorskaya, V. V. Buryachenko
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, pp 47-53; doi:10.5194/isprs-archives-xliv-2-w1-2021-47-2021

Mobile devices provide a huge amount of multimedia information sending to the members of social groups every day. Sometimes it is required to authorize the sending information using the limited computational resources of smartphones, tablets or laptops. The hardest problem is with smartphones, which have the limited daily energy and battery life. There are two scenarios for using mobile watermarking techniques. The first scenario is to implement the embedding and extraction schemes using proxy server. In this case, the watermarking scheme does not differ from conventional techniques, including the advanced ones based on adaptive paradigms, deep learning, multi-level protection, and so on. The main issue is to hide the embedding and extracting information from the proxy server. The second scenario is to provide a pseudo-optimized algorithm respect to robustness, imperceptibility and capacity using limited mobile resources. In this paper, we develop the second approach as a light version of adaptive image and video watermarking schemes. We propose a simple approach for creating a patch-based set for watermark embedding using texture estimates in still images and texture/motion estimates in frames that are highly likely to be I-frames in MPEG notation. We embed one or more watermarks using relevant large-sized patches according to two main criteria: high texturing in still images and high texturing/non-significant motion in videos. The experimental results confirm the robustness of our approach with minimal computational costs.
A. V. Gaboutchian, V. A. Knyaz, E. N. Maschenko, D. V. Korost, A. A. Kudaev
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, pp 61-65; doi:10.5194/isprs-archives-xliv-2-w1-2021-61-2021

Findings of teeth play a significant role in palaeoanthropology. And excavations in Vietnamese LangTrank cave serve as a vivid example and evidence of this statement. Teeth constitute the majority of the paleontological material dated to Middle and Late Pleistocene periods. This is to some extent the result of dietary preferences of porcupines as these rodents include in their diets bones of animals however avoiding extremely hard coronal parts of teeth. Under such circumstances teeth serve a key to taxonomic differentiation of findings as genetic analysis is often hindered by a lack of preserved DNA at such dating of material. However morphological analysis is difficult in some cases either, as teeth can be worn out or broken. In that case enamel thickness measurements become an effective study instrument as this feature varies between species. In the current study two teeth with clear signs of expressed dental wear, presumably upper fourth premolars of wild boar required more detailed analysis. Thus they were reconstructed after micro-computed tomography scanning similarly to other upper teeth picked for comparison: orang-utan tooth from the same location and two teeth from the Upper Palaeolithic Sunghir (they have been scanned earlier). This study required new approaches to image processing and measurement methodology due to marked attrition of the samples. The workflow and results of enamel thickness assessments which facilitated taxonomical differentiation of the findings are presented in the article.
A. V. Nasonov, O. S. Volodina, A. S. Krylov
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, pp 167-170; doi:10.5194/isprs-archives-xliv-2-w1-2021-167-2021

We address the problem of constructing single low noise image from a sequence of multiple noisy images. We use the approach based on finding and averaging similar blocks in the image and extend it to multiple images. Unlike traditional multi-frame super-resolution algorithms, the block-matching approach does not require computationally expensive motion estimation for multi-frame image denoising. In this work, we use an algorithm based on weighted nuclear minimization for image denoising. The evaluation of the algorithm shows noticeable improvement of image quality when using multiple input images instead of single one. The improvement is the most noticeable in the areas with complex non-repeated structure.
T. B. Sagindykov, E. A. Pavelyeva
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, pp 183-187; doi:10.5194/isprs-archives-xliv-2-w1-2021-183-2021

Image matting often requires advanced image processing, especially in conditions, when small details such as hair are present in the image. In this article the hybrid method for human image matting based on convolutional neural network and principal curvatures is proposed. The U-Net based neural network is used to predict a rough foreground segmentation mask. Then the obtained foreground mask is refined by principal curvatures method to process the elongated hair-like structures. Test results show that the proposed method can improve the coarse human segmentation.
Y. V. Vizilter, S. Y. Zheltov, M. A. Lebedev
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, pp 207-211; doi:10.5194/isprs-archives-xliv-2-w1-2021-207-2021

A lot of image matching applications require image comparison to be invariant relative to intensity values variations. The Pyt’ev theory for Morphological Image Analysis (MIA) was developed based on image-to-shape matching with mosaic shape models. Within the framework of this theory, the problem of shape-to-shape comparison was previously considered too. The most sophisticated and weakest part of MIA theory is the comparison of mosaic shapes with some arbitrary restrictions described by graphs or relations. In this paper we consider the possible options for comparing images and shapes using morphological projection and morphological correlation. Our contribution is a new scheme of morphological shape-to-image projection and, correspondingly, the new morphological correlation coefficient (MCC) for shape-to-image correlation with restricted mosaic models. We also refine the expressions for shape-to-shape comparison.
А. Axyonov, D. Ryumin, I. Kagirov
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, pp 7-13; doi:10.5194/isprs-archives-xliv-2-w1-2021-7-2021

This paper presents a new method for collecting multimodal sign language (SL) databases, which is distinguished by the use of multimodal video data. The paper also proposes a new method of multimodal sign recognition, which is distinguished by the analysis of spatio-temporal visual features of SL units (i.e. lexemes). Generally, gesture recognition is a processing of a video sequence, which helps to extract information on movements of any articulator (a part of the human body) in time and space. With this approach, the recognition accuracy of isolated signs was 88.92%. The proposed method, due to the extraction and analysis of spatio-temporal data, makes it possible to identify more informative features of signs, which leads to an increase in the accuracy of SL recognition.
E. Ryumina, D. Ryumin, D. Ivanko, A. Karpov
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, pp 177-182; doi:10.5194/isprs-archives-xliv-2-w1-2021-177-2021

This paper proposes a new hybrid method for automatic detection and recognition of the presence/absence of a protective mask on human's face. It combines visual features extracted using Convolutional Neural Network (CNN) with image histograms that convey information about pixel intensity. Several pre-trained models for building feature extraction systems using a CNN and several types of image histograms are considered in this paper. We test our approach on the Medical Mask Dataset and perform cross-corpus analysis on two other databases named Masked Faces (MAFA) and Real-World Masked Face Dataset (RMFD). We demonstrate that the proposed hybrid method increases the Unweighted Average Recalls (UARs) of recognition of the presence/absence of a protective mask on human's face in comparison with traditional CNNs on the MAFA and RMFD databases by 0.96% and 1.32%, respectively. The proposed method can be generalized and used for other tasks of biometry, computer vision, machine learning and automatic face recognition.
V. V. Kniaz, L. Grodzitskiy, V. A. Knyaz
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, pp 125-130; doi:10.5194/isprs-archives-xliv-2-w1-2021-125-2021

Coded targets are physical optical markers that can be easily identified in an image. Their detection is a critical step in the process of camera calibration. A wide range of coded targets was developed to date. The targets differ in their decoding algorithms. The main limitation of the existing methods is low robustness to new backgrounds and illumination conditions. Modern deep learning recognition-based algorithms demonstrate exciting progress in object detection performance in low-light conditions or new environments. This paper is focused on the development of a new deep convolutional network for automatic detection and recognition of the coded targets and sub-pixel estimation of their centers.
S. Сherebylo, E. Ippolitov, M. Novikov, V. Vnuk
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, pp 27-31; doi:10.5194/isprs-archives-xliv-2-w1-2021-27-2021

The introduction of information technologies into the practice of healthcare significantly changes the methods of diagnosis and treatment, the forms of interaction of doctors with patients and colleagues, the organization of treatment and restoration of health. Modern digital medicine makes it possible to increase the availability, quality and efficiency of medical care.The development of modern three-dimensional modeling and the introduction of a new generation of spiral computed tomographs have significantly expanded the possibilities of using these information technologies in reconstructive surgery. The performed study deals with the problems of forming the personalized digital models of anatomical structures according to patient's tomographic data. The principles of tomographic image segmentation are considered. A review and comparison of the specialized software for obtaining of digital models with the use of tomographic data is given. The evaluation of the functionality, speed and quality of the models is presented. Practical recommendations on the use of the software for creating digital models for medical applications are given. The issues of certification of personalized models and medical products are discussed.
V. V. Danilov, O. M. Gerget, D. Y. Kolpashchikov, N. V. Laptev, R. A. Manakov, L. A. Hérnandez-Gómez, F. Alvarez, M. J. Ledesma-Carbayo
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, pp 33-40; doi:10.5194/isprs-archives-xliv-2-w1-2021-33-2021

In the era of data-driven machine learning algorithms, data represents a new oil. The application of machine learning algorithms shows they need large heterogeneous datasets that crucially are correctly labeled. However, data collection and its labeling are time-consuming and labor-intensive processes. A particular task we solve using machine learning is related to the segmentation of medical devices in echocardiographic images during minimally invasive surgery. However, the lack of data motivated us to develop an algorithm generating synthetic samples based on real datasets. The concept of this algorithm is to place a medical device (catheter) in an empty cavity of an anatomical structure, for example, in a heart chamber, and then transform it. To create random transformations of the catheter, the algorithm uses a coordinate system that uniquely identifies each point regardless of the bend and the shape of the object. It is proposed to take a cylindrical coordinate system as a basis, modifying it by replacing the Z-axis with a spline along which the h-coordinate is measured. Having used the proposed algorithm, we generated new images with the catheter inserted into different heart cavities while varying its location and shape. Afterward, we compared the results of deep neural networks trained on the datasets comprised of real and synthetic data. The network trained on both real and synthetic datasets performed more accurate segmentation than the model trained only on real data. For instance, modified U-net trained on combined datasets performed segmentation with the Dice similarity coefficient of 92.6±2.2%, while the same model trained only on real samples achieved the level of 86.5±3.6%. Using a synthetic dataset allowed decreasing the accuracy spread and improving the generalization of the model. It is worth noting that the proposed algorithm allows reducing subjectivity, minimizing the labeling routine, increasing the number of samples, and improving the heterogeneity.
K. K. Shcherbina, E. V. Fogt, M. A. Golovin, M. V. Chernikova, A. D. Kuzicheva
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, pp 189-193; doi:10.5194/isprs-archives-xliv-2-w1-2021-189-2021

Distance clothing technology is an actively developing area. For its implementation in the highly specialized area of manufacturing technical means of rehabilitation, and, in particular, in the manufacture of special functional and aesthetic clothing for disabled people, it is necessary to solve organizational and technical issues. An example of a technical issue is remote acquisition of dimensional features. The dimensional characteristics of the human body are an integral part of the technological process of manufacturing individual clothing. The use of 3D scanning makes it possible to implement remote technology for individual design and manufacture of clothing. The production of clothing for wheelchair users involves the adaptation of standard clothing design techniques to the specific properties of the posture. A case of a patient with a C5-C6 cervical vertebra fracture who has been using a wheelchair for more than 25 years is considered. The study used 3D human models obtained with a 3D scanner. The technique of scanning and an example of processing the obtained data are presented. The main features of dimensional features have been determined and an algorithm for their determination by anatomical landmarks has been developed. Recommendations are given for processing 3D scans and combining them into one 3D model. It is shown that the use of 3D scanning for the remote production of a set of functional and aesthetic clothing for a wheelchair user is a way to produce comfortable individual clothing.
V. B. S. Prasath, N. N. Hien, D. N. H. Thanh, S. Dvoenko
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, pp 171-176; doi:10.5194/isprs-archives-xliv-2-w1-2021-171-2021

Image restoration with regularization models is very popular in the image processing literature. Total variation (TV) is one of the important edge preserving regularization models used, however, to obtain optimal restoration results the regularization parameter needs to be set appropriately. We propose here a new parameter estimation approach for total variation based image restoration. By utilizing known noise levels we compute the regularization parameter by reducing the similarity between residual and noise variances. We use the split Bregman algorithm for the total variation along with this automatic parameter estimation step to obtain a very fast restoration scheme. Experimental results indicate the proposed parameter estimation obtained better denoised images and videos in terms of PSNR and SSIM measures and the computational overload is less compared with other approaches.
I. Basharov, D. Yudin
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, pp 15-20; doi:10.5194/isprs-archives-xliv-2-w1-2021-15-2021

The paper is devoted to the task of multiple objects tracking and segmentation on monocular video, which was obtained by the camera of unmanned ground vehicle. The authors investigate various architectures of deep neural networks for this task solution. Special attention is paid to deep models providing inference in real time. The authors proposed an approach based on combining the modern SOLOv2 instance segmentation model, a neural network model for embedding generation for each found object, and a modified Hungarian tracking algorithm. The Hungarian algorithm was modified taking into account the geometric constraints on the positions of the found objects on the sequence of images. The investigated solution is a development and improvement of the state-of-the-art PointTrack method. The effectiveness of the proposed approach is demonstrated quantitatively and qualitatively on the popular KITTI MOTS dataset collected using the cameras of a driverless car. The software implementation of the approach was carried out. The acceleration of the procedure for the formation of a two-dimensional point cloud in the found image segment was done using the NVidia CUDA technology. At the same time, the proposed instance segmentation module provides a mean processing time of one image of 68 ms, the embedding and tracking module of 24 ms using the NVidia Tesla V100 GPU. This indicates that the proposed solution is promising for on-board computer vision systems for both unmanned vehicles and various robotic platforms.
P. N. Skonnikov, D. V. Trofimov
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, pp 195-199; doi:10.5194/isprs-archives-xliv-2-w1-2021-195-2021

Some diseases, for instance, a glaucoma, cause visual field defects. For the timely diagnostics of such defects, various methods are used. One of the state-of-the-art diagnostic methods is automated static perimetry. The method of static perimetry consists in the light sensitivity determination in different parts of the visual field using stationary objects of variable luminosity. When scanning the visual field in this way, an important factor is the control of gaze fixation at the fixation point. The greatest accuracy in determining the gaze fixation position is achieved by the method of the pupil visual tracking using a video camera.In this paper, four groups of visual tracking algorithms are considered: segmentation-based methods, correlation methods, methods based on optical flow and on weighted average. An experimental comparison of these methods was carried out using the base of video recordings obtained in the automatic static perimetry apparatus. On these videos the ground truth tracks of pupil were marked. The comparison was conducted according to two criteria: center location error and tracking length. It is shown that only the weighted average method has an acceptable tracking length.
K. I. Kiy, D. A. Anokhin
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, pp 119-124; doi:10.5194/isprs-archives-xliv-2-w1-2021-119-2021

In this paper, a new technique for real-time object detection and tracking is presented. This technique is based on the geometrized histograms method (GHM) for segmenting and describing color images (frames of video sequences) and on the facilities for global image analysis provided by this method. Basic elements of the technique that make it possible to solve image understanding problems almost without using the pixel arrays of images are introduced and discussed.A real-time parallel software implementation of the developed technique is briefly discussed. This technique is applied to solving problems of road scene analysis. The application to finding small contrast objects in images, like traffic signals and signal zones of vehicles is given. The developed technique is applied also to detecting other vehicles in the frame. The results of processing different frame of videos of road scenes are presented and discussed.
S. M. Sokolov, N. D. Beklemishev, A. A. Boguslavsky
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, pp 201-205; doi:10.5194/isprs-archives-xliv-2-w1-2021-201-2021

The paper considers two directions in the use of visual data for information support of purposeful movements of ground vehicles. This is optical odometry and navigation by landmarks in the environment. Optical odometry builds the trajectory of movement of the vehicle based on the determination of displacements based on selective visual data from different fields of view. The choice and indication of landmarks at the described stage of research remains with the operator. The vision system (VS) monitors the specified landmarks and determines the position of the vehicle relative to them. The experiments used such fields of view as monocular forward looking, panoramic (fisheye type) and forward looking stereo system. When combining the data of the visual channel with each other and with the data of other navigation systems, the specificity of visual sensors is taken into account – a significant effect of the reliability and accuracy of the results from the observation conditions. Experimental verification of the VS layout showed the achievability of high accuracy in solving the navigation problem using the visual channel. All the components of the described process of organizing purposeful movements based on the use of the visual channel continue to be improved.
A. V. Gaboutchian, V. A. Knyaz, M. M. Novikov, S. V. Vazyliev, D. V. Korost, S. A. Cherebylo, A. A. Kudaev
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, pp 67-72; doi:10.5194/isprs-archives-xliv-2-w1-2021-67-2021

Studies of teeth represent a significant part of palaeoanthropological research. Over the past two decades these studies have significantly developed with implementation of high resolution imaging based on x-ray scanning techniques. Highly informative reconstructions based on image processing have provided an opportunity to study morphological layers and structures of teeth which are usually hidden under the outer layer of dental enamel. Thus micro-computed tomography of the studied teeth has been performed in order to obtain reconstructions of enamel and dentin surfaces. The material is represented by well-preserved teeth of an adolescent from Upper Palaeolithic archaeological site of Sunghir world-renowned archaeological site in Vladimir Oblast in the Russian Federation. The characteristic feature of the studied teeth is in their unusual, presumably archaic, morphology, which has been previously studied and described through measurements by application of automated digital odontometry method; however the mentioned study referred to the enamel surface. And in the current study these algorithms are applied to measure the surface of dentin. As this is the first successful attempt of measuring dentin surface morphology, the process has to be improved for complete automation. Nevertheless even currently applied approaches allow to compare enamel and dentin morphology through measurements.
I. A. Gracheva, A. V. Kopylov
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, pp 79-83; doi:10.5194/isprs-archives-xliv-2-w1-2021-79-2021

In medical image processing, image fusion is the process of combining complementary information from different (multimodality) images to obtain a fused image, which plays a vital role in further analysis and treatment planning. The main idea of this paper is to improve the image content by fusing computer tomography (CT) and magnetic resonance (MR) images. We propose here the new algorithm based on the probabilistic gamma-normal model with structure-transferring properties. Firstly, we select the areas with the highest pixel intensity on original CT and MR images. In parallel with this, the structures of original images are distinguished using the probabilistic gamma-normal model. The weighted-fusion image can be obtained based on detected objects and structure. Finally, we smooth the weighted-fusion image using the structure-transferring filter and combine the smoothed image with the weighted-fusion image for obtaining the resulting image. The key point here is that we do not need to re-allocate the structure, which leads to the reduction of computation time. The proposed method gives the best result in terms of the spatial frequency metric and lower computation time than other image fusion methods.
E. M. Kabaev, Y. A. Hamad, K. V. Simonov, A. G. Zotin
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, pp 91-97; doi:10.5194/isprs-archives-xliv-2-w1-2021-91-2021

The research results in the field of computer visualization of the shoulder joint biomechanics are presented. The possibilities of using biomechanical robotic mechanotherapy on the CON-TREX complex in the rehabilitation treatment of patients after arthroscopic shoulder surgery are shown. The possibilities of additional visualization of magnetic resonance imaging (MRI) data using spectral decomposition methods (Shearlet transform and contrasting with color coding) are studied. Experiments with the use of the proposed diagnostic technique are described. The relationship between the MRI data and CON-TREX protocols in planning and implementation of the rehabilitation procedures is demonstrated. The technique which allows to improve the quality and availability of the MRI data in the study of the shoulder joint biomechanics during restorative treatment is described.
D. Ivanko, D. Ryumin
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, pp 85-89; doi:10.5194/isprs-archives-xliv-2-w1-2021-85-2021

Visual information plays a key role in automatic speech recognition (ASR) when audio is corrupted by background noise, or even inaccessible. Speech recognition using visual information is called lip-reading. The initial idea of visual speech recognition comes from humans’ experience: we are able to recognize spoken words from the observation of a speaker's face without or with limited access to the sound part of the voice. Based on the conducted experimental evaluations as well as on analysis of the research field we propose a novel task-oriented approach towards practical lip-reading system implementation. Its main purpose is to be some kind of a roadmap for researchers who need to build a reliable visual speech recognition system for their task. In a rough approximation, we can divide the task of lip-reading into two parts, depending on the complexity of the problem. First, if we need to recognize isolated words, numbers or small phrases (e.g. Telephone numbers with a strict grammar or keywords). Or second, if we need to recognize continuous speech (phrases or sentences). All these stages disclosed in detail in this paper. Based on the proposed approach we implemented from scratch automatic visual speech recognition systems of three different architectures: GMM-CHMM, DNN-HMM and purely End-to-end. A description of the methodology, tools, step-by-step development and all necessary parameters are disclosed in detail in current paper. It is worth noting that for the Russian speech recognition, such systems were created for the first time.
A. V. Khvostikov, D. M. Korshunov, A. S. Krylov, M. A. Boguslavskiy
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, pp 113-118; doi:10.5194/isprs-archives-xliv-2-w1-2021-113-2021

Automatic identification of minerals in images of polished section is highly demanded in exploratory geology as it can provide a significant reduction in time spent in the study of ores and eliminate the factor of misdiagnosis of minerals. The development of algorithms for automatic analysis of images of polished sections makes it possible to create of a universal tool for comparing ores from different deposits, which is also much in demand. The main contribution of this paper can be summed up in three parts: i) creation of LumenStone dataset ( which unites high-quality geological images of different mineral associations and provides pixel-level semantic segmentation masks, ii) development of CNN-based neural network for automatic identification of minerals in images of polished sections, iii) implementation of software tool with graphical user interface that can be used by expert geologists to perform an automatic analysis of polished sections images.
V. A. Knyaz, A. A. Maksimov, M. M. Novikov, A. V. Urmashova
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, pp 137-142; doi:10.5194/isprs-archives-xliv-2-w1-2021-137-2021

Many anthropological researches require identification and measurement of craniometric and cephalometric landmarks which provide valuable information about the shape of a head. This information is necessary for morphometric analysis, face approximation, craniafacial identification etc. Traditional techniques use special anthropological tools to perform required measurements, identification of landmarks usually being made by an expert-anthropologist. Modern techniques of optical 3D measurements such as photogrammetry, computer tomography, laser 3D scanning provide new possibilities for acquiring accurate 2D and 3D data of high resolution, thus creating new conditions for anthropological data analysis. Traditional anthropological manual point measurements can be substituted by analysis of accurate textured 3D models, which allow to retrieve more information about studied object and easily to share data for independent analysis. The paper presents the deep learning technique for anthropological landmarks identification and accurate 3D measurements. Photogrammetric methods and their practical implementation in the automatic system for accurate digital 3D reconstruction of anthropological objects are described.
D. Murashov, Y. Obukhov, I. Kershner, M. Sinkin
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, pp 163-166; doi:10.5194/isprs-archives-xliv-2-w1-2021-163-2021

The work is devoted to the study of the frequency features of the optical flow obtained from the video record of long-term video-electroencephalographic (video-EEG) monitoring data of patients with epilepsy. It is necessary to obtain features to recognize epileptic seizures and differentiate them from non-epileptic events. We propose to analyze the periodograms of the smoothed optical flow calculated from the fragments of the patient's video recordings. We use Welch's method to obtain periodograms. The values of the power spectral density of the optical flow at the selected frequencies will be used as features. Using the clustering algorithm, four groups of events were identified in video recordings.
I. G. Khanykov, V. A. Nenashev
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, pp 107-111; doi:10.5194/isprs-archives-xliv-2-w1-2021-107-2021

The issues of image fusion in a two-position small-size radar on-board operational monitoring system are considered. The aim of present research is to develop a method for fusion of images of the land surface based on data obtained from a multi-sensor spatially distributed on-board location system implemented on the basis of a UAV. The method of combining different-angle location images is implemented iteratively. At each iteration, there is a search for pairs of points of the contour of the isolated areas. Areas are highlighted using two image segmentation methods. The final result is a proposed method for information fusion from a two-position on-board small-sized radar system and an optical location system. The implemented method of fusion makes it possible to increase the information content, quality and reliability of information about the observed underlying surfaces and the physical objects detected on them. The practical significance of the results obtained lies in the formation of integral information in real time in the interests of environmental reconnaissance, monitoring in hard-to-reach and dangerous places for human life, as well as in order to promptly prevent natural and man-made emergencies.
E. F. Berra,
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, pp 267-272; doi:10.5194/isprs-archives-xlii-3-w12-2020-267-2020

Interest in Unnamed Aerial Vehicle (UAV)-sourced data and Structure-from-Motion (SfM) and Multi-View-Stereo (MVS) photogrammetry has seen a dramatic expansion over the last decade, revolutionizing the fields of aerial remote sensing and mapping. This literature review provides a summary overview on the recent developments and applications of light-weight UAVs and on the widely-accepted SfM - MVS approach. Firstly, the advantages and limitations of UAV remote sensing systems are discussed, followed by an identification of the different UAV and miniaturised sensor models applied to numerous disciplines, showing the range of systems and sensor types utilised recently. Afterwards, a concise list of advantages and challenges of UAV SfM-MVS is provided and discussed. Overall, the accuracy and quality of the SfM-MVS-derived products (e.g. orthomosaics, digital surface model) depends on the quality of the UAV data set, characteristics of the study area and processing tools used. Continued development and investigation are necessary to better determine the quality, precision and accuracy of UAV SfM-MVS derived outputs.
S. A. Santos, N. V. Ribeiro
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, pp 525-528; doi:10.5194/isprs-archives-xlii-3-w12-2020-525-2020

The Território Quilombola Kalunga de Goiás (TKG) was established by law nº. 11,409/GO, and was federally regularized as territory in 2009. Its total area covers three Municipalities of Goiás: Monte Alegre de Goiás, Teresina de Goiás and Cavalcante. There live remaining communities of quilombolas. The present work aims to evaluate the recurrence of fires in this Territory and in its surroundings, doing relationships between land use and land cover and burned areas. For this purpose, were used data generated from satellite images, made available in public databases, and procedures in GIS. During the annualized time, the frequency of fires was moderate. Grassland had the largest proportion of burned areas.
O. T. Amoo, M. D. V. Nakin, A. Abayomi, H. O. Ojugbele, A. W. Salami
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, pp 45-51; doi:10.5194/isprs-archives-xliv-4-w3-2020-45-2020

Water shortages are a chronic and severe problem in South Africa. Allocation of this limited water resources, environmental quality, and policies for sustainable water use are issues of increasing concern that require accurate and timely information to evolve strategies for dynamic natural resources management. Specifically, this paper is aimed to assist the planning, restoring and to rationally allocate the water resources in any river basin in resolving the current water stresses in many parts of South Africa, by using integrated knowledge from simulation and integrated river basin management approach. The developed system dynamic (SD) allocation system was used to investigates the extent to which the framework is ‘sustainable’ in the medium and long terms in evaluating existing and future water allocation among conflicting users at Mkomazi River Basin (MRB), KwaZulu-Natal Province, South Africa The invented SD framework confirms agricultural water use as the highest demand when compared with other users. The optimal sustainability performance index (0.25) of the system at 70% dependable flow shows an integrated scenario that combines rainfall variation with improved irrigation water use efficiency as a suitable framework plan. The study uses integrated knowledge from simulation and integrated river basin management approach as a feasible method to assist the planning, restoring and to rationally allocate the water resources in any river basin with similar attributes to the study area in resolving the current water stresses in many parts of the country. Water resources managers would find these tools beneficial in understanding the complex nature of water resources allocation and in determining priorities area which required prompt attention and intervention.
K. Ahkouk, M. Machkour, K. Majhadi, R. Mama
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, pp 7-11; doi:10.5194/isprs-archives-xliv-4-w3-2020-7-2020

Sequence to sequence models have been widely used in the recent years in the different tasks of Natural Language processing. In particular, the concept has been deeply adopted to treat the problem of translating human language questions to SQL. In this context, many studies suggest the use of sequence to sequence approaches for predicting the target SQL queries using the different available datasets. In this paper, we put the light on another way to resolve natural language processing tasks, especially the Natural Language to SQL one using the method of sketch-based decoding which is based on a sketch with holes that the model incrementally tries to fill. We present the pros and cons of each approach and how a sketch-based model can outperform the already existing solutions in order to predict the wanted SQL queries and to generate to unseen input pairs in different contexts and cross-domain datasets, and finally we discuss the test results of the already proposed models using the exact matching scores and the errors propagation and the time required for the training as metrics.
C. Basri, A. ElKhadimi
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, pp 121-128; doi:10.5194/isprs-archives-xliv-4-w3-2020-121-2020

The advancement of Internet of things (IoT) has revolutionized the field of telecommunication opening the door for interesting applications such as smart cities, resources management, logistics and transportation, wearables and connected healthcare. The emergence of IoT in multiple sectors has enabled the requirement for an accurate real time location information. Location-based services are actually, due to development of networks, sensors, wireless communications and machine learning algorithms, able to collect and transmit data in order to determine the target positions, and support the needs imposed by several applications and use cases. The performance of an indoor positioning system in IoT networks depends on the technical implementation, network architecture, the deployed technology, techniques and algorithms of positioning. This paper highlights the importance of indoor localization in internet of things applications, gives a comprehensive review of indoor positioning techniques and methods implemented in IoT networks, and provides a detailed analysis on recent advances in this field.
G. Ikrissi, T. Mazri
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, pp 255-261; doi:10.5194/isprs-archives-xliv-4-w3-2020-255-2020

The smart campus is a sustainable and well-connected environment that aims to improve experience, efficiency and education. It uses a variety of interconnected components, smart applications and networked technologies to facilitate communication, make more efficient use of resources, improve performance, security and quality of campus services. However, as with many other smart environments, the smart campus is vulnerable to many security issues and threats that make it face many security-related challenges that limit its development. In our paper, we intend to provide an overview of smart campuses by highlighting the main applications and technologies used in this environment, presenting several vulnerabilities and susceptible attacks that affect data and information security in the smart campus. Moreover, we discuss the major challenges of smart campus and we conclude by overviewing some current security solutions to deal with campus security issues.
W. N. F. W. A. Basir, U. Ujang, Z. Majid, S. Azri, T. L. Choon
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, pp 107-116; doi:10.5194/isprs-archives-xliv-4-w3-2020-107-2020

From days to days, management of construction project has been improved during life-cycle project, starting from planning until maintenance. This happen cause of the advantages in implementation technology of Building Information Modeling (BIM) and Geographic Information System (GIS) in supporting construction project. Few years ago, enhancement in term of BIM and GIS that provides an additional extension for the purpose of information management is very interesting. With the advantages that been provided by BIM and GIS, information of construction project can be adapted into real situation of the construction site which be helpful during the life-cycle of building construction. BIM and GIS is a different platform which contains their own advantages that support construction project. In order to bring the most effectiveness in management of construction project, integration between BIM and GIS becomes an important task to support the design phase until operational phase which include the facility management and maintenance. Although this integration can support the building information management, the software that used in integration process is still having limitations and differences in fulfilling the needs of users. For that reasons, data consistency needs to be studied in order to develop the best practices of integration application. The purpose of this paper is to investigate the data consistency during the integration process. From the investigation, it showed that there are some data inconsistency occurs in IFC platform after conversion process. Through this paper, the comparison of the geometric and semantic data before and after translation process will be examined.
S. F. K. Anuar, A. A. M. Nasir, S. Azri, U. Ujang, Z. Majid, M. González Cuétara, G. De Miguel Retortillo
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, pp 61-69; doi:10.5194/isprs-archives-xliv-4-w3-2020-61-2020

In recent years, there has been an increase in development of urbanization in the world. Nowadays, all communities in the world are concerned about current technological developments especially in terms of development and management that can facilitate their daily life. In urbanization, smart city is one of modernization changes that improves the infrastructure management, convenience and efficient for the life of citizens. Moreover, 3D asset management is one of the approach of smart city development. Asset management using the 3D concept has been witnessing a welcoming approach due to its high efficiency in organising multiple assets. 3D geometric extraction offers a perfect aid in recording information of an asset such as buildings. The model is derived from the reality techniques where the exterior surfaces of an object are captured in high resolution through the means of special equipment such as airborne imagery. From here, point clouds are generated where the sets of points based on the external surfaces of an object are present. Pre-processing of point clouds should be done in order to perform the 3D modelling. In dealing with point clouds, segmentations are used to investigate the structure of the object with information regarding to different level of sections. The boon behind this segmentation process is to identify different features that is available for the object. In this research, the aim is to analyse the different methodology and algorithm available to segment the point cloud data. Comparison between the results will be made to identify the advantages and disadvantages of the results for the use of asset management.
I. Elachkar, H. Ouzif, H. Labriji
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, pp 203-207; doi:10.5194/isprs-archives-xliv-4-w3-2020-203-2020

The user profile is a very important tool in several fields such as recommendation systems, customization systems etc., it is used to narrow the number of data or results provided for a specific user, also to minimize the cost and the time of processing of multiple systems. Whatever the user profile model used, it’s updating and enrichment is a very essential step in the information research process in order to obtain more interesting and satisfactory results, which lead the information systems to develop several techniques aiming to enrich them based especially on similarity methods between user profiles. The similarity methods are used for several tasks such as the detection of duplicate profiles in online social network, also to answer the problem of cold start, and to predict users who can become friends as well as their future intentions, etc. In this paper, we propose a new approach to express the similarity between users profiles by developing a structural similarity measure to calculate the similarity between user profiles based on SimRank measure or similarity ,and the properties of bipartite graphs, in order to take advantage of the information provided by the relational structure between user profiles and their interests, our method is characterized by the similarity propagation between graph's nodes over iterations from source nodes to their successors, so our method finds profiles similar to the query profile, whether the links are direct or indirect between profiles.
A. Awad, H. Ali, S. K. M. Abujayyab, I. R. Karas, D. R. S. Sumunar
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, pp 81-84; doi:10.5194/isprs-archives-xliv-4-w3-2020-81-2020

The massive disasters that arise by nature and humanity are significantly leads to several losses in lives and infrastructures. Disasters such as chemical explosions, flash floods and volcanoes. The high level of preparedness from the governments and administration authorities and ambulance services can significantly reduce the losses in lives. The aim of this paper is to measure the spatial readiness of ambulance facilities for natural disasters using GIS networks analysis. The measurement performed based on three standards, the area covered by the ambulance service, speed of service and the proportion to the population. ArcGIS spatial analysis and network analysis tools employed to develop the coverage maps of the three measured standards. According to the analysis, 94.4% from the study area appeared within the standard distance (20 km) from the ambulance stations, while 91% from the study area appeared within the time response standard (15 minutes) from the ambulance stations. The study area has a deficit of 256,714 people and needs 5 additional ambulances to achieve the demographic standard. The main recommendation of this study is to apply this methodology regularly in the study area to avoid any weakness before the disasters and to increase the level of preparedness.
S. A. M. Ariff, S. Azri, U. Ujang, A. A. M. Nasir, N. Ahmad Fuad, H. Karim
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, pp 71-79; doi:10.5194/isprs-archives-xliv-4-w3-2020-71-2020

The current trends of 3D scanning technologies allow us to acquire accurate 3D data of large-scale environment efficiently. The 3D data of large-scale environments is essential when generating 3D model is for the visualization of smart cities. For the seamless visualization of 3D model, large data size will be used during the 3D data acquisition. However, the processing time for large data size is time consuming and requires suitable hardware specification. In this study, different hardware capability in processing large data of 3D point cloud for mesh generation is investigated. Light Detection and Ranging (LiDAR) Airborne and Mobile Mapping System (MMS) are used as data input and processed using Bentley ContextCapture software. The study is conducted in Malaysia, specifically in Wilayah Persekutuan Kuala Lumpur and Selangor with the size of 49 km2. Several analyses have been performed to analyse the software and hardware specification based on the 3D mesh model generated. From the finding, we have suggested the most suitable hardware specification for 3D mesh model generation.
H. Bayraktar, D. Y. Bayar, B. Kara, G. Bilgin
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, pp 137-142; doi:10.5194/isprs-archives-xliv-4-w3-2020-137-2020

Cities are facing numerous challenges because of the unprecedented growth of population all over the world. In this context, smart city stands out as a viable option to improve quality of life. Smart city, with its ability to transform the information into economic, social and environmental benefits, offers acquisitions in the fields of sustainable development, competitiveness and environmental sustainability. However, the cost of implementing and maintaining smart city applications on a large scale reveals the necessity to choose the right smart city application at the beginning of smart city transformation. In order to determine which smart city application should be used in smart city domain, the current situation and needs of the city should be analysed effectively. Maturity assessment can be used as a tool to understand the existing conditions of a city. In this study, Turkey's smart city approach will be addressed and Smart City Maturity Assessment Model of Turkey will be introduced with the preparation and implementation process. Consequently, the impact of the Smart City Maturity Assessment Model on selection of smart city applications will be discussed with the result of maturity assessment which is implemented on 4 cities of Turkey.
Z. Nassr, N. Sael, F. Benabbou
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, pp 323-330; doi:10.5194/isprs-archives-xliv-4-w3-2020-323-2020

Sentiment Analysis concerns the analysis of ideas, emotions, evaluations, values, attitudes and feelings about products, services, companies, individuals, tasks, events, titles and their characteristics. With the increase in applications on the Internet and social networks, Sentiment Analysis has become more crucial in the field of text mining research and has since been used to explore users’ opinions on various products or topics discussed on the Internet. Developments in the fields of Natural Language Processing and Computational Linguistics have contributed positively to Sentiment Analysis studies, especially for sentiments written in non-structured or semi-structured languages. In this paper, we present a literature review on the pre-processing task on the field of sentiment analysis and an analytical and comparative study of different researches conducted in Arabic social networks. This study allowed as concluding that several works have dealt with the generation of stop words dictionary. In this context, two approaches are adopted: first, the manual one, which gives rise to a limited list, and second, the automatic, where the list of stop words is extracted from social networks based on defined rules. For stemming two, algorithms have been proposed to isolate prefixes and suffixes from words in dialects. However, few works have been interested in dialects directly without translation. The Moroccan dialect in particular is considered as the 5th dialect studied among Arabic dialects after Jordanian, Egyptian, Tunisian and Algerian dialects. Despite the significant lack in studies carried out on Arabic dialects, we were able to extract several conclusions about the difficulties and challenges encountered through this comparative study, as well as the possible ways and tracks to study in any dialects sentiment analysis pre-processing solution.
B. Prima, M. Bouhorma
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, pp 343-349; doi:10.5194/isprs-archives-xliv-4-w3-2020-343-2020

In this paper, we propose a malware classification framework using transfer learning based on existing Deep Learning models that have been pre-trained on massive image datasets. In recent years there has been a significant increase in the number and variety of malwares, which amplifies the need to improve automatic detection and classification of the malwares. Nowadays, neural network methodology has reached a level that may exceed the limits of previous machine learning methods, such as Hidden Markov Models and Support Vector Machines (SVM). As a result, convolutional neural networks (CNNs) have shown superior performance compared to traditional learning techniques, specifically in tasks such as image classification. Motivated by this success, we propose a CNN-based architecture for malware classification. The malicious binary files are represented as grayscale images and a deep neural network is trained by freezing the pre-trained VGG16 layers on the ImageNet dataset and adapting the last fully connected layer to the malware family classification. Our evaluation results show that our approach is able to achieve an average of 98% accuracy for the MALIMG dataset.
B. Sebbar, A. Moumni, A. Lahrouni
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, pp 365-373; doi:10.5194/isprs-archives-xliv-4-w3-2020-365-2020

Great effort has been recently employed for the development of a modern and competitive agriculture in Morocco, growth in the agricultural sector is determined largely through the realization of thousands of new projects, and the support of the smallholder farmers at a national scale. Modernization of irrigation systems, and enlargement of the extent and spatial distribution of irrigated areas holds the key to increase annual productions. In this context, we established a unique procedure for monitoring the agricultural surfaces not fully exploited in terms of potential and production, in the semi-arid zone of the Haouz plain, central Morocco. We derived Normalized Difference Vegetation Index (NDVI) time series from Sentinel-2 (S2) and Landsat 8 (L8) high spatial resolution satellite images from 2016 to 2018. Seasonal phenological changes and land-cover dynamics, in addition to elevation models and landscape slopes, helped determine periods and thresholds suitable for classes separability, and establish a set of rules to be implemented in a Decision tree classifier model for a detailed land-cover mapping of the last three years. The agricultural zone was successfully separated from mountains and hills, and the derived maps of the three years yielded satisfying result with an OAthat reached above 91% for quite detailed landscape-type information. The outputs of this work hold promise to provide valuable information for planners, decision-makers and regional offices, to help smallholder farmers. Although this approach has been developed at regional-scale, it holds the potential to be adapted to larger scales, with the appropriate selection of land-cover types, and carful adjustment in the threshold values.
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