In traffic monitoring for video analysis systems, vehicle Shadows have a negative effect on their performance. Shadow Detection and removal are essential steps in accurate vehicle Detection. In this paper, a new method is proposed for Shadow Detection using a novel convolution neural network architecture. In the proposed method, the edges of the image are first extracted. Edge extraction reduces calculation, and accelerates the execution of the method. The background of the frame is then removed and the main features are extracted using the ResUNet-a architecture. This architecture consists of two parts: the encoder and the decoder, which detect the Shadow at the decoder output and then remove it. Deep learning is used to detect Shadows, which increases the accuracy of the analysis. The ResUNet-a architecture can learn complex, hierarchical, and appropriate features from the image for accurate feature Detection and discarding the irrelevant Shadow, thereby outperforming conventional filters.The results show that the proposed method provides better performance on NJDOT traffic video, highway-1, and highway-3 datasets than popular Shadow removal methods. Also, the method improves the evaluation criteria such as F-measure and runtime. The F-measure is 94 and 93% for highway-1 and highway-3, respectively.

In image processing many algorithms get disturbed because of Shadow. These problems can be avoided if the location of Shadows is clear. This article presents a new optimized algorithm for extracting Shadows from a single color aerial image. Most of similar works do this by using a suite color space. In this work two RGB and YCbCr color spaces are combined and a powerful ratio map has been created. Furthermore, in a new method, effect of sky blue color is determined that improves the ratio map. Candidate Shadow and non-Shadow regions are separated by applying Otsu’ s thresholding method. Because of ratio map performance it is not necessary to analyze region by region and leads to decrease computation cost. So it can be used in online application. The experimental results demonstrate the advantage of the proposed algorithm.

With the increase in world population، industrialization and improvement in the standard of living، there has been a continuous increase in consumption of energy. In the recent years، a new resource of energy، gas-hydrates، is drawing worldwide attention. Detection and identification of suitable areas of shallow geothermal energy، using remote sensing data is one of the new methods in many applications. In areas of anomalously high heat flow، geothermal systems transfer heat to the Earth’ s surface often forming surface expression such as hot spring، heated ground، and associated mineral deposits. Geothermal systems are increasingly important as sources of renewable energy، or as natural wonders of protected status attracting tourists، and their study is relevant to monitoring deeper magmatic processes. Thermal infrared (TIR) remote sensing provides a unique tool for mapping the surface expressions of geothermal activity as applied to the exploration for new geothermal power resources and long term monitoring studies. Airborne and space borne TIR data supports long-term monitoring of geothermal systems by providing a rapid and repeatable method of inventorying surface geothermal features. In addition، methods for relating the temperatures of surface geothermal phenomena to estimates of near-surface heat loss provide important inputs to the monitoring of geothermal activity and as applied to geothermal resource assessment and modeling. A geothermal resource can be simply defined as a reservoir inside the Earth from which heat can be extracted economically (cost wise less expensive than or comparable with other conventional sources of energy such as hydroelectric power or fossil fuels) and utilized for generating electric power or any other suitable industrial، agricultural or domestic application in the near future. Geothermal resources vary widely from one location to another، depending on the temperature and depth of the resource، the rock chemistry and the abundance of groundwater. Utilization of geothermal resources can broadly be classified into electric power generation and non-electric use. The type of the geothermal resource determines the method of its utilization. This research is based on applications of remote sensing as a decision support system that focused on the exploration of geothermal energy and environmental management. The aim of this study is to identify suitable areas for Shadow geothermal energy Detection by integrating of land surface temperature (LST) anomalies and the energy flows of surface energy balance algorithms for land (SEBAL) algorithm using data LDCM data، ، has been evaluated and analyzed in the North West of Iran. To this end، and because of at least the effect of solar radiation، two examined the scenes of LDCM data was used for dates October 13، 2016. Then، using two single-band algorithms (Radiative Transfer Equation (RTE) and SCJM&S) to calculate the LST and the LST anomaly maps of were identified. In addition، using the SEBAL Algorithm was calculating the amount of net radiation received by the Earth's surface (Rn)، the amount of heat flow between the different layers of soil (G) and the amount of radiation absorbed by the solar surface (Rsolar). By assessing and combining this information layers with the LST anomaly maps the Shadow geothermal prone areas were identified and determined. The results showed that the areas between the cities of Marand and Tasuj as well as between Gator and Khoy cities prone Shadow geothermal areas، the existence of large natural spa in the region، the possibility of geothermal resources increases and this is confirmed. Also، similar results were obtained in areas south of the city of Urmia and west of Oshnavieh. These obtained areas have the maximum distance that the location of energy consumption (in Urmia، Khoy، Marand، Tasuj، Sharafkhaneh and Oshnavieh) equal to 30 km، which is economically justified and it can provide a large part of the clean energy used in industry and cities and brings a healthy environment.

In common background subtraction method usually the Shadow of objects is extracted as the moving objects that cause some errors in the performance of Intelligent Transportation Systems (ITS). In this paper، an effective algorithm based on combination of HSV color space and Principal Component Analysis is proposed. In this algorithm، the candidate Shadow region is detected by using HSV color space. In this step، some part of vehicles may be detected as the moving Shadow. So، to compensate and improve the performance of moving Shadow Detection، principal components analysis algorithm is applied to recognize the automobile object by modeling the automobile based on orthogonal eigen vectors of database. Our proposed algorithm is evaluated on real and operational videos of ITS. The obtained results demonstrate the efficiently and effectiveness of our proposed algorithm in the ITS applications.

Height variation in different urban objects e.g. buildings and trees coincide to occurrence of Shadows in aerial and satellite images. Areas casted by Shadow, appear darker than neighbouring areas in the image and it makes an unwanted contrast to the other brighter areas. This phenomenon attenuate different expectations from remote sensing data. In particular Shadow areas ruins the result of automatic image matching algorithm and in land cover classification cause the misclassified pixels.Detection of overShadow areas is the primary step to deal with this problem. Different strategies have been used to detect Shadow in remote sensing images. To name a few we can consider classification based methods, region-growing methods and different spectral indices. In classification based method, some ground truth from Shadow areas are collected and supervised machine learning algorithms are used to classify Shadow and non-Shadow pixels. Region-growing algorithms use the high contrast between Shadow and bright areas. Spectral indices are made by simple arithmetic equations between spectral bands.There is some deficiencies in the result of previous methods and strategies. In machine learning methods, existence of ground truth information is essential and somehow affect the results. Using region growing and spectral indices usually leads to addition of roads and vegetation to Shadow areas. The result of all this methods are presented in pixel level, labelled Shadow pixels. Wrongly detected Shadow pixels appear as noises in classification map.In high resolution aerial and satellite imagery single pixels are not meaningful independently. This is the outcome of decreasing the ground sampling size of sensors versus natural objects on the earth. The solution to deal with this problem is to integrate similar neighbouring pixels which belong to the same ground object. Object-based image analysis (OBIA) is developed based on this idea, considers image object, created by segmenting the image, as processing unit. The power and possibilities of image objects are less discussed and considered in detecting Shadow areas.In this paper we propose a new object-based framework for Shadow Detection which simultaneously benefits from OBIA, machine learning and spectral indices. Our proposed framework consists of four main steps. First step is the pre-processing of data. In this step spectral bands are pan-sharpened to enhance the spatial accuracy and the panchromatic band is segmented by eCognition Software. In the second step new spectral indices are proposed to overcome the weakness of existing indices in mixing roads and vegetation to Shadow areas. To automate the process of detecting Shadows from index values the Otsu thresholding algorithm is employed.Third step is object-based Shadow Detection. To detect Shadow areas in object level, majority analysis of Shadow pixels in each image object is considered. To solve the ambiguity between vegetated and Shadow objects an extra condition is checked to confirm that an object belongs to Shadow class. This condition uses the mean NDVI value of pixels in each image object. Finally in the fourth step evaluation of produced map is obtained using completeness, correctness and F-measure. In this step the result of Shadow Detection using spectral indices, proposed index, machine learning and proposed method are compared and analysed.GeoEye-1 satellite data comprised 4 spectral bands over Qom city in Iran is used in the experiment.800 Shadow objects are selected manually to evaluate the result. Correctness, completeness and F-measure obtained from confusion matrix of Shadow map are calculated to compare the results. The result of Shadow Detection by spectral indices and SVM and random forest classifiers have been compared to the result of proposed method. Result of our experiments demonstrates the superiority of proposed object-based over the pixel-based method respect to correctness and F-measure for different classifiers. The proposed method succeed to detect Shadow area with 93% correctness and 92% Completeness It is also evident that object-based method have well behaviour on the edge of Shadow areas and perfectly detect Shadows.

The purpose of the current research is to identify and examine the most important sociological theories in relation to the Shadow curriculum, while examining research works and literature, and examine its nature in the perspective of sociological theories. The theoretical framework of this article is based on critical theory and from the methodological point of view, it is a qualitative research in which the data were collected and analyzed in a documentary way. On the one hand, the Shadow curriculum is a tool to achieve goals that cannot be realized through the official curriculum, and designers and policy makers achieve these goals through it. On the other hand, it is a language that students and teachers use to break taboos, to express the problems of school and social life. They want to highlight the concerns of family, social and cultural life. The Shadow curriculum originates from the language, mind, thoughts and hearts of students and teachers. Based on the results of this research, the most important sociological theories that have explained the Shadow curriculum are: Marxism, critical theory, resistance theory, Nietzsche's theory and psychoanalysis

According to myths narrations, Homa (osprey) was an auspicious fowl and prosperity messenger that sho was under the the protection of her, would have been wealthy and prosperous. This fowl is always mentioned by poets because it’s an osprey and didn’t hurt anything.Although Saeb has used these contents in his poetry, but has used literary arrays too, for example irony, allegory and simile the same as poets on his age. He has used norm breaking to present a different personality of osprey. Saeb believed that the reason of instability state of osprey and his disliking to her is because of her dark and transient Shadow. The image of osprey presented by Saeb has made her as a terrestrial and tangible created.Also he believed that the reason of auspiciousness of this myth fowl is because of her humility and the reason of her fame is her satisfactory to some bones. So, Saeb didn’t want to be under the protection of osprey (homa) because he believed that prosperity is an inherent adjective not acquire.He used this myth fowl to describe nature and unimportant issues, that all of them are mentioned above and are analyzed in different accepts.

Automatic topic Detection seems unavoidable in social media analysis due to big text data which their users generate. Clustering-based methods are one of the most important and up-to-date categories in topic Detection. The goal of this research is to have a wide study on this category. Therefore, this paper aims to study the main components of clustering-based-topic-Detection, which are embedding methods, distance metrics, and clustering algorithms. Transfer learning and consequently pretrained language models and word embeddings have been considered in recent years. Regarding the importance of embedding methods, the efficiency of five new embedding methods, from earlier to recent ones, are compared in this paper. To conduct our study, two commonly used distance metrics, in addition to five important clustering algorithms in the field of topic Detection, are implemented by the authors. As COVID-19 has turned into a hot trending topic on social networks in recent years, a dataset including one-month tweets collected with COVID-19-related hashtags is used for this study. More than 7500 experiments are performed to determine tunable parameters. Then all combinations of embedding methods, distance metrics and clustering algorithms (50 combinations) are evaluated using Silhouette metric. Results show that T5 strongly outperforms other embedding methods, cosine distance is weakly better than other distance metrics, and DBSCAN is superior to other clustering algorithms.