bluuebunny/crossref_metadata_2025 huggingface dataset:
HOW WILL CLIMATE CHANGE IMPACT THE STORM MAGNITUDE AND THROUFALL IN SEVERAL FOREST AREAS IN IRAN?
Authors: Attarod P. , Tang Q. , Van Stan II J. T. , Pypker T. G. , Liu X.
Published: October 2019

Abstract. Across all forest systems, the primary driver of throughfall (TF) amount is rainfall amount (Pg) though no work has addressed the sensitivity of the TF response to projected shifts in Pg due to climate change. We assessed how climate change may impact TF sensitivity to variability in Pg for eleven typical forest sites across the main climate types of Iran using a nondimensional relative sensitivity coefficient. The Coupled Model Intercomparison Project phase 5 (CMIP5) HadGEM2-ES product was used under two emission scenarios (Representative Concentration Pathway (RCP) 2.6 and 8.5) to project yearly precipitation and Pg for the measurement sites during 2020–50. There was a strong linear relationship between TF and Pg at all sites [TF = 0.66 (Pg) – 0.30; R2 = 0.91; n = 639]. The sensitivity coefficient ranged from 0.96–5.3 across the eleven forest sites. Large sensitivity coefficient differences were found between small (< mean annual Pg) and large (> mean annual Pg) storms for arid plantations. To buffer expected shifts in storm size due to climate change, it may be suitable to incorporate TF sensitivity when choosing landscaping and urban greening. Shifts in Pg and increased small storm frequency are predicted for 2020–50 per CMIP5 HadGEM2-ES low and high emission scenarios.
INVESTIGATING THE POSSIBILITY OF PREPARING SMALL SCALE SOIL MOISTURE MAP FROM COUPLED SENTINEL-1 AND SENTINEL-2 DATA
Authors: Attarzadeh R. , Amini J.
Published: October 2019

Abstract. With the failure of the radar instrument on NASA's Soil Moisture Active Passive (SMAP) satellite, the Sentinel-1 sensor has been considered as an alternative for replacing the SMAP radar data and restoring the combined radar and radiometer SMC product. A challenging subject to this purpose is the immense discrepancy between the spatial resolution of planned SMAP radar instrument (3 km) and Sentinel-1 data (10 m). In this paper, we investigate the possibility of preparing small scale soil moisture map and its quality from the synergy of Sentinel-1 and Sentinel-2 data using object-based image analysis (OBIA). To reach this goal, the most related features with soil moisture variable extracted from Sentinel-1 and Sentinel-2 data have been used as input layers to multi-resolution segmentation (MRS) algorithm to create image objects. Then the support vector regression (SVR) estimator has been used to calculate the soil moisture value of image objects. Initial evaluations demonstrate that produced soil moisture map obtained acceptable accuracy. In addition, the flexibility of the final product improves on the scale of the soil moisture map regarding the shape and size of image objects. It is also possible to combine this soil moisture product with a Level-3 SMAP SSM product to exploit the advantages of both products. This combination would lead to a small scale soil moisture map with enhanced accuracy and flexible scale.
A CRITICAL REVIEW OF QUALITY ASSESSMENT PROTOCOLS IN PAN-SHARPENING
Authors: Aghapour Maleki S. , Ghassemian H.
Published: October 2019

Abstract. Although vast amounts of pan-sharpening methods have been proposed to date, there has been relatively little published on the topic of qualitative and quantitative assessment of the pan-sharpened multispectral (MS) data. Since a high resolution reference MS image is not available, qualitative and quantitative assessments of the spatially enhanced MS image are a much more challenging task in pan-sharpening. Thomas et al. conducted a critical survey of conventional pan-sharpening methods considering remote sensing physics. In this paper, we study the effects of physical constraints of satellite sensors on conventional quality assessment protocols. The novelty of this work is in investigating the effect of physical constraints on the performance of quality assessment protocols. The most popular protocols, which are analysed here, are quality not requiring a reference (QNR), Wald’s protocol, Zhou’s protocol, Khan’s protocol and Ghassemi’s protocol. In doing so, the strengths and weaknesses of such protocols regarding the physical limitations on satellite sensors are discussed.
COMPARISON OF TRADITIONAL AND MACHINE LEARNING BASE METHODS FOR GROUND POINT CLOUD LABELING
Authors: Ayazi S. M. , Saadat Seresht M.
Published: October 2019

Abstract. Today, a variety of methods have been proposed by researchers to distinguish ground and non-ground points in point cloud data. Most fully automated methods have a common disadvantage which is the lack of proper algorithm response for all areas and levels of the ground, so most of these algorithms have good outcomes in simple landscapes but encounter problems in complex landscapes. Point cloud filtering techniques can be divided into two general rule-based and novel methods. Today, the use of machine learning techniques has improved the results of classification, which has led to significant results, especially when data can be labelled at the presence of training data. In this paper, firstly, altimeter and radiometric features are extracted from the LiDAR data and the point cloud derived from digital photogrammetry. Then, these features are participated in a classification process using SVM learning and random forest methods, and the ground and Non-ground points are classified. The classification results using this method on LiDAR data show a total error of 6.2%, a type I error of 5.4%, and a type II error of 13.2%. The comparison of the proposed method with the results of LASTools software shows a reduction in total error and type I error (while increasing the type II error). This method was also investigated on the dense point cloud obtained from digital photogrammetry and based on this study, the total was 7.2%, the type I error was 6.8%, and the type II error was 10.9%.
EXPLORING THE POTENTIAL OF FULL WAVEFORM AIRBORNE LIDAR FEATURES AND ITS FUSION WITH RGB IMAGE IN CLASSIFICATION OF A SPARSELY FORESTED AREA
Authors: Babadi M. , Sattari M. , Iran Pour S.
Published: October 2019

Abstract. Precise measurements of forest trees is very important in environmental protection. Measuring trees parameters by use of ground- based inventories is time and cost consuming. Employing advanced remote sensing techniques to obtain forest parameters has recently made a great progress step in this research area. Among the information resources of the study field, full waveform LiDAR data have attracted the attention of researchers in the recent years. However, decomposing LiDAR waveforms is one of the challenges in the data processing. In fact, the procedure of waveform decomposition causes some of the useful information in waveforms to be lost. In this study, we aim to investigate the potential use of non-decomposed full waveform LiDAR features and its fusion with optical images in classification of a sparsely forested area. We consider three classes including i) ground, ii) Quercus wislizeni and iii) Quercus douglusii for the classification procedure. In order to compare the results, five different strategies, namely i) RGB image, ii) common LiDAR features, iii) fusion of RGB image and common LiDAR features, iv) LiDAR waveform structural features and v) fusion of RGB image and LiDAR waveform structural features have been utilized for classifying the study area. The results of our study show that classification via using fusion of LiDAR waveform features and RGB image leads to the highest classification accuracy.
AGRICULTURAL LAND CHANGE DETECTING AND FORECASTING USING COMBINATION OF FEEDFORWARD MULTILAYER NEURAL NETWORK, CELLULAR AUTOMATA AND MARKOV CHAIN MODELS
Authors: Babaeian Diva A. , Bigdeli B. , Pahlavani P.
Published: October 2019

Abstract. This paper proposed a methodology for finding changes in agricultural land of Tehran during past years and simulating these changes for future years. The proposed method utilized the spatial GIS-based techniques and Landsat satellite imagery to predict agricultural land map for the future of Tehran. Therefore, a method for finding and predicting changes based on combining the feedforward multilayer perceptron neural network (MLP), cellular automata (CA), and Markov chain model were applied. In this regard, the Landsat images of 2002, 2008, and 2014 were classified by a binary support vector machine classifier into two classes of agricultural and non-agricultural. Then, the potential transition maps were generated by the neural network MLP and extensible areas were obtained by the Markov chain model. Finally, the results of these two steps were combined with the MOLA method and the 2020 and 2025 agricultural maps were predicted. The proposed method obtained the Kappa factor of 89.92% that indicates the high ability of the neural network and the CA–Markov for finding the changes and prediction in the city of Tehran.
MULTIPLE VEHICLES AND PEOPLE TRACKING IN AERIAL IMAGERY USING STACK OF MICRO SINGLE-OBJECT-TRACKING CNNS
Authors: Bahmanyar R. , Azimi S. M. , Reinartz P.
Published: October 2019

Abstract. Geo-referenced real-time vehicle and person tracking in aerial imagery has a variety of applications such as traffic and large-scale event monitoring, disaster management, and also for input into predictive traffic and crowd models. However, object tracking in aerial imagery is still an unsolved challenging problem due to the tiny size of the objects as well as different scales and the limited temporal resolution of geo-referenced datasets. In this work, we propose a new approach based on Convolutional Neural Networks (CNNs) to track multiple vehicles and people in aerial image sequences. As the large number of objects in aerial images can exponentially increase the processing demands in multiple object tracking scenarios, the proposed approach utilizes the stack of micro CNNs, where each micro CNN is responsible for a single-object tracking task. We call our approach Stack of Micro-Single- Object-Tracking CNNs (SMSOT-CNN). More precisely, using a two-stream CNN, we extract a set of features from two consecutive frames for each object, with the given location of the object in the previous frame. Then, we assign each MSOT-CNN the extracted features of each object to predict the object location in the current frame. We train and validate the proposed approach on the vehicle and person sets of the KIT AIS dataset of object tracking in aerial image sequences. Results indicate the accurate and time-efficient tracking of multiple vehicles and people by the proposed approach.
THE ARCHITECTURE OF A STEREO IMAGE BASED SYSTEM TO MEASURE TREE GEOMETRIC PARAMETERS
Authors: Bayat M. , Latifi H. , Hosseininaveh A.
Published: October 2019

Abstract. Stereo photogrammetry enables collecting precise and detailed three-dimensional data of terrestrial objects. The estimation of qualitative and quantitative tree attributes, in particular those related to geometric measures, is crucial for forest management. In this study, a stereo imaging system is designed in order to measure a set of geometric attributes of urban trees such as crown dimensions, height and diameter at multiple height levels. The system consists of two hardware and software components. The hardware comprises two cameras with a specified baseline, two raspberry pi 3 model B+ boards, a GPS, an IMU and a power bank, all embedded in a box. The software includes a connection between the camera and the raspberry pi 3 in each side as well as data transfer to a laptop. The calibration is conducted in laboratory prior to applying the system and leads to achieve a disparity image from a pair of stereo imagery, which is then processed to extract dense point clouds. The system enables measuring basic, yet crucial tree attributes such as height and diameter in near real-time basis. The entire process is conducted by means of drastic libraries in Robot Operating System (ROS). Apart from being convenient and real-time, the system is associated with the potential for timely and precise measurements, which enable comparative analysis against other existing remote measurement systems as well as reference field data.
HYPERSPECTRAL CHANGE DETECTION IN WETLAND AND WATER-BODY AREAS BASED ON MACHINE LEARNING
Authors: Ahangarha M. , Seydi S. T. , Shahhoseini R.
Published: October 2019

Abstract. Wetlands and water-bodies are transitional lands between terrestrial and aquatic ecosystems that give many advantages. The presence of phenomena activities and artificial human activities cause changes on the Earth’s surface. This process causes the changes in land cover type especially in wetlands and water-bodies’ area. For monitoring and assessing resources like wetlands and water-bodies, it is necessary to be aware of these changes. Change detection and attribution of wetlands and water-bodies change over time present more challenges for correctly analysing remote sensing imagery. Hyperspectral images now have potential applications in many scientific areas due to their high spectral resolution and so their good information contents. The aim of this study is to propose a procedure for determining land surface changes within the semi-arid wetland and surrounding upland areas using the new method by combining machine Learning method for detecting change using EO-1 Hyperion satellite hyperspectral imagery. The study area is Shadegan wetlands in the south-west of Iran in Khuzestan province. The most critical water resources of the province are depleted and contain unprecedented levels of toxic waste. In addition, the results show the superiority of the implemented method to extract change map with overall accuracy by a margin of nearly 94% using multi-temporal hyperspectral.
A MOVING OBSTACLES PLANNER ALGORITHM FOR A SIX-WHEELED SURVEYING ROBOT
Authors: Bazhan S. , Hosseininaveh A.
Published: October 2019

Abstract. Nowadays, robotic systems such as ground vehicle robots are mostly used in many industrial and military applications. Therefore, the path planning problem in the robotics domain is very important. Moving Obstacles Planner (MOP) algorithms have got the researchers interests in recent years and some of the most recent ones have been implemented in Robot Operating System (ROS) which is an open source middle wear to work with robots. This paper aims to compare the state-of-the-art MOP algorithms including Rapidly exploring Random Tree (RRT) and those implemented in the ROS navigation stack such as Dynamic Window Approach (DWA) local planner coupled with Dijkstra and A* as global planners on a six-wheeled robot known as MOOR in simulation environment. The results reveal that all of these algorithms have been designed for a square shape footprint robot and thus have limitations for MOOR with a rectangular footprint shape.
INTRODUCING A NOVEL METHOD TO SOLVE SHORTEST PATH PROBLEMS BASED ON STRUCTURE OF NETWORK USING GENETIC ALGORITHM
Authors: Behzadi S. , Kolbadinejad M.
Published: October 2019

Abstract. The shortest path problem is widely applied in transportation, communication and computer networks. It addresses the challenges of determining a path with minimum distance, time or cost from a source to the destination. Network analysis provides strong decision support for users in searching shortest path. A lot of algorithms are designed for solving shortest path but most of them did not considered the conditions of the networks. Genetic Algorithm is a kind of Algorithm that has a lot of efficiency and it can be used for solving many kinds of problems. It also can be used based on the condition of the problems.In this paper, Genetic Algorithm is used for solving shortest path in multistage process planning (MPP) problem. New mutations as well as crossover parameters are defined for each network based on the conditions of them. The results of our experimental demonstrate the effectiveness of the models.
INTEGRATION OF SENTINEL-2 AND LANDSAT-8 DATA FOR SURFACE REFLECTANCE TIME-SERIES ANALYSIS
Authors: Berdou G. , Shrestha S. , Hahn M.
Published: October 2019

Abstract. Integration of Sentinel-2 and Landsat-8 imagery is a key factor to provide earth observation data at a global scale with higher temporal resolution. Integration of data from two sensors is possible with the consistent harmonized data framed in common reference and processing, which can be used for comparing geophysical surface characteristics. This study focuses on the analysis of the atmospheric correction methods available for both Landsat-8 and Sentinel-2 products to convert the top of the atmosphere to the bottom of atmosphere reflectance. Other investigations (De Keukelaere, 2018) carried out similar analyses focusing on data acquired over water, while this study emphasises the analyses over land covers. Two processing algorithms iCOR and Sen2COR are utilized to perform atmospheric corrections, and results are statistically and visually compared. Comparisons based on same images processed with different algorithms show very strong correlation for some classes (urban: 0.99), while correlation values around 0.85 were achieved between images from different sensors.
INVESTIGATING THE EFFECT OF CAPACITY CRITERION ON THE OPTIMAL ALLOCATION OF EMERGENCY FACILITIES IN GIS ENVIRONMENT
Authors: Bolouri S. , Vafaeinejad A. , Alesheikh A. , Aghamohammadi H.
Published: October 2019

Abstract. Location-allocation analysis is one of the most GIS useful analysis, especially in allocating demands to facilities. One of these facilities is the fire stations, which the correct locations and optimal demand allocations to those have most importance. Each facility has a specific capacity that should be considered in locating the facilities and allocating the demand to those. In recent years, the use of unified models in solving allocation problems is too common because these models can solve a variety of problems, but in most of these models, the capacity criterion for facilities has been ignored. The present study tries to investigate the location-allocation problem of the fire stations with the aid of two Tabu and Genetic algorithms with the goal of maximizing the coverage using the (Vector Assignment Ordered Median Problem) VAOMP model, taking into account the capacity criterion and regardless of it. The results of using two algorithms in problem-solving show that the Genetic algorithm produces better quality solutions over a shorter time. Also, considering the capacity criterion that brings the problem closer to real-world space, in the study area, 59,640 demands will not be covered by any station within a 5-minute radius and will be highly vulnerable to potential hazards. Also, by adding 3 stations to the existing stations and re-allocating, the average of allocated demands with the help of Genetic was 93.39% and 92.74% for the Tabu algorithm. So it is necessary to consider the capacity of the facilities for optimal services.
SPATIOTEMPORAL MONITORING OF THERMAL ENVIRONMENT IN ISFAHAN METROPOLITAN AREA
Authors: Bozorgi M. , Nejadkoorki F.
Published: October 2019

Abstract. Urban Heat Island (UHI) phenomenon is regarded as one of the most critical issues in cities caused by unsustainable urban development. UHI has significant effects on air quality and energy consumption of urban buildings and it directly affects life quality the citizens. The current study aims to analyze and evaluate the spatiotemporal changes of thermal environment in Isfahan Metropolitan Area (IMA), Iran, during 1998-2014 time profile. A mono-window algorithm was applied to extract Land Surface Temperature (LST) values from Landsat thermal bands (TM and OLI sensors). Temporal and spatial changes in IMA’s thermal environment were then analyzed using statistical methods, Mann-Kendall analysis and Urban-Heat-Island Ration Index (URI). Results indicated LST in IMA is of an increasing trend and the intensity of this trend is mainly concentrated in northwest and northeast of the city and around Zayandeh-Rood River in the south. This arrangement of UHI distribution is primarily due to compact and high-density patterns of city development in its boundaries, destruction of Zayandeh-Rood riverine ecosystem and its drying up during the last decade as well as excessive consumption of green covers by urban structures. Finally, it was concluded that the URI has witnessed an increasing trend from 0.25 in 1998 to 0.312 in 2014, which is in line with the spatiotemporal dynamics of thermal environment in the study location. The results of this study provide practical planning implications for identifying UHI hotspots in growing cities and optimization of green cover plantations to alleviate UHI effects in urban environments.
DISCRIMINATION AEROSOL FORM CLOUDS USING CATS-ISS LIDAR OBSERVATIONS BASED ON RANDOM FOREST AND SVM ALGORITHMS OVER THE EASTERN PART OF MIDDLE EAST
Authors: Brakhasi F. , Hajeb M. , Fouladinejad F.
Published: October 2019

Abstract. Aerosols and Clouds play an important role in the Earth's environment, climate change and climate models. The Cloud-Aerosol Transport System (CATS) as a lidar remote sensing instrument, from the International Space Station (ISS), provides range-resolved profile measurements of atmospheric aerosols and clouds. Discrimination aerosols from clouds have always been a challenges task in the classification of space-born lidars. In this study, two algorithms including Random Forest (RF) and Support Vector Machine (SVM) were used to tackle the problem in a nighttime lidar data from 18 October 2016 which passes form the western part of Iran. The procedure includes 3 stages preprocessing (improving the signal to noise, generating features, taking training sample), classification (implementing RF and SVM), and postprocessing (correcting misleading classification). Finally, the result of classifications of the two algorithms (RF-SVM) were compared against ground truth samples and Vertical Feature Mask (VFM) of CATS product indicated 0.96–0.94 and 0.88–0.88 respectively. Also, it should be mentioned that a kappa accuracy 0.88 was acquired when we compared VFM against our ground truth samples. Moreover, a visual comparison with Moderate Resolution Imaging Spectroradiometer (MODIS) AOD and RGB products demonstrating that clouds and aerosol can be well detected and discriminated. The experimental results elucidated that the proposed method for classification of space borne lidar observation leads to higher accuracy compared to PDFs based algorithms.
3D SURVEYING AND 3D RECONSTRUCTION OF ARCHITECTURE OF THE ROYAL PARK OF TIRANA
Authors: Corniello L.
Published: October 2019

Abstract. The research proposes the study of the Royal Park of Tirana in Albania, with particular reference both to the vegetation system, such as tree-lined avenues, ornamental gardens and vegetable gardens, and to the architectural elements such as the Villa, the Church, the gardener's house, the generator house and greenhouses. The few iconographic documents consist of the plans of the Grand Park of Tirana in 1957 and 1980, where some large buildings can be identified. The bibliography is relegated to the few citations found in the volumes illustrating the works of the two architects G. Bosio and G. Bertè. For the activities of knowledge of the Royal Park and of the architectural elements, in addition to the classical techniques of direct and indirect survey, we took into account the Ryobi laser instrumentation applied on portable computer support both tablets and smartphone. This digital system allows an immediate view of the relevant data on the photographic image taken by the support, transforming it into a dynamic datum. In the first phase, the relevant activity envisaged a basic knowledge extended to architectural organisms and green systems with a primitive geometric model; subsequently, measurements of architectural details, structures and the digital model were made. Appropriate photographic documentation was produced in addition to bibliographic, archival, and iconographic surveys.
NEW HYBRID PAN-SHARPENING METHOD BASED ON TYPE-1 FUZZY-DWT STRATEGY
Authors: Dadras Javan F. , Mortazavi F. S. , Moradi F. , Toosi A.
Published: October 2019

Abstract. The purpose of image fusion is to combine two images from the same view in order to produce an image with more information and higher quality. In this paper, a panchromatic image with high spatial resolution and a low-resolution multi-spectral image having rich spectral information are fused together to produce a high-resolution multi-spectral image that heritage the characteristics of both initial images. For this purpose, a hybrid pan-sharpening method, called ‘Improved Fuzzy-DWT’ have been proposed based on the modification of the parameters existed in the latest version of Fuzzy-Wavelet algorithm, and then it was compared with some other algorithms such as PCA, Gram-Schmidt, Wavelet, and two of its hybrid derivatives called PCA-Wavelet and IHS-wavelet. The comparison was conducted using DIV, SSIM, SID, CC, DS, and QNR spectral and spatial quality assessment metrics. The obtained results demonstrate that the proposed hybrid algorithm has relatively better performance in comparison with the other mentioned pan-sharpening techniques in terms of both spectral and spatial qualities, regarding it was superior in terms of SID, DIV, SSIM, DS. From the computational cost standpoint, the proposed method has the most running time compared with the other methods.
URBAN VISION DEVELOPMENT IN ORDER TO MONITOR WHEELCHAIR USERS BASED ON THE YOLO ALGORITHM
Authors: Ahmadi A. , Argany M. , Neysani Samany N. , Rasooli M.
Published: October 2019

Abstract. Disability has been one of the most important problems of social communities throughout the ages. As population and urbanization have grown dramatically over recent years, this problem has more and more created the gap between people with disabilities and ordinary people in terms of access to resources, social services and social partnerships. Therefore, this study attempts to demonstrate the ratio of presence of wheelchair users in a community compared to the total population of the same community and evaluate their patterns of presence in different conditions, for example, various weather conditions. For this purpose, we used the You Look Only Once version 3 (YOLOv3) algorithm which is a multilayer deep learning object detection tool to analyze and extract wheelchair users from three different sets of images taken by a camera located in an intersection proximate to a rehabilitation center in Quebec, Canada. The results show that the proportion of wheelchair users in the sample community is 7.4%, while the population with mobility disabilities in Canada is 9.6%.
3D THERMAL MAPPING OF BUILDING ROOFS BASED ON FUSION OF THERMAL AND VISIBLE POINT CLOUDS IN UAV IMAGERY
Authors: Dahaghin M. , Samadzadegan F. , Dadras Javan F.
Published: October 2019

Abstract. Thermography is a robust method for detecting thermal irregularities on the roof of the buildings as one of the main energy dissipation parts. Recently, UAVs are presented to be useful in gathering 3D thermal data of the building roofs. In this topic, the low spatial resolution of thermal imagery is a challenge which leads to a sparse resolution in point clouds. This paper suggests the fusion of visible and thermal point clouds to generate a high-resolution thermal point cloud of the building roofs. For the purpose, camera calibration is performed to obtain internal orientation parameters, and then thermal point clouds and visible point clouds are generated. In the next step, both two point clouds are geo-referenced by control points. To extract building roofs from the visible point cloud, CSF ground filtering is applied, and the vegetation layer is removed by RGBVI index. Afterward, a predefined threshold is applied to the normal vectors in the z-direction in order to separate facets of roofs from the walls. Finally, the visible point cloud of the building roofs and registered thermal point cloud are combined and generate a fused dense point cloud. Results show mean re-projection error of 0.31 pixels for thermal camera calibration and mean absolute distance of 0.2 m for point clouds registration. The final product is a fused point cloud, which its density improves up to twice of the initial thermal point cloud density and it has the spatial accuracy of visible point cloud along with thermal information of the building roofs.
CNN-BASED FEATURE-LEVEL FUSION OF VERY HIGH RESOLUTION AERIAL IMAGERY AND LIDAR DATA
Authors: Daneshtalab S. , Rastiveis H. , Hosseiny B.
Published: October 2019

Abstract. Land-cover classification of Remote Sensing (RS) data in urban area has always been a challenging task due to the complicated relations between different objects. Recently, fusion of aerial imagery and light detection and ranging (LiDAR) data has obtained a great attention in RS communities. Meanwhile, convolutional neural network (CNN) has proven its power in extracting high-level (deep) descriptors to improve RS data classification. In this paper, a CNN-based feature-level framework is proposed to integrate LiDAR data and aerial imagery for object classification in urban area. In our method, after generating low-level descriptors and fusing them in a feature-level fusion by layer-stacking, the proposed framework employs a novel CNN to extract the spectral-spatial features for classification process, which is performed using a fully connected multilayer perceptron network (MLP). The experimental results revealed that the proposed deep fusion model provides about 10% improvement in overall accuracy (OA) in comparison with other conventional feature-level fusion techniques.
ONTOLOGICAL ASSESSMENT AND SIGNIFICANCE OF SEMANTIC LEVELS OF TAGS IN OPENSTREETMAP
Authors: Ahmadian S. , Hakimpour F.
Published: October 2019

Abstract. User-generated contents are developing rapidly through VGI and contributors create the tags through the Web applications in a free mechanism. Semantic Knowledge in VGI like other user-generated contents needs to be combined with other authoritative data sources. One of the main challenges of integration is the semantic heterogeneity of user-generated contents which are describing the geographical objects as POIs. Geographical objects can be described in different semantic levels such as purpose or function. Significance of semantic levels defines the importance of related attributes. Analysis of significance for semantic levels of different POIs can be considered as a base to enhance the semantic quality of VGI. This paper proposes an approach based on the notions of rough set theory to measure the significance of semantic levels of tags which are applied to describe the buildings in OpenStreetMap. The proposed approach is implemented for tags which are applied to describe buildings in OpenStreetMap. Results show the high significance for tags which describing the semantic levels of geographic information constructs and purpose/ function for buildings.
SPATIO-TEMPORAL ASSESSMENT OF MIXED LAND-USE IMPACT ON TRAVEL DEMAND
Authors: Davoudi N. , Taleai M. , Molavi M.
Published: October 2019

Abstract. Urban mobility pattern studies are one of the interesting issues in GIScience which provide appropriate means for urban transportation planning and management. Mobility across the city has a direct relation with the land-use pattern. This paper investigates the spatio-temporal effect of the land-use mix at street level on urban movement. Taxi pick up and drop-off data in Manhattan was chosen as the sample data in this study. Trips are classified into two parts (weekdays and weekends trips) and then the correlation between mixed land-use and number of trips occurred in different time windows in each street segment, is calculated. Results indicate positive impact and moderate correlation between mixed land-use and number of trips. In streets with high Entropy, homogeneous distribution of the number of trips at each time window for the weekend and non-homogeneous trip distribution for employment and commercial and residential areas for weekday trips was observed. Also, in streets with low Entropy, non-homogeneous trip distribution at different time windows for both weekday and weekend were observed upon to dominant land-use.
SPATIAL-SPECTRAL MORPHOLOGICAL FEATURE EXTRACTION FOR HYPERSPECTRAL IMAGES CLASSIFICATION
Authors: Dowlatshah M. , Ghassemian H. , Imani M.
Published: October 2019

Abstract. Remote sensing image classification is a method for labeling pixels to show the Land cover types. The ambiguity in the classification process can be reduced if the spatial dependencies, which exist among the adjacent pixels, are intelligently incorporated into the feature extraction process. One of the methods for spatial feature extraction is applying morphological filters. The basic idea of the morphological filters is comparison of structures within the image with a reference form called structural element. Four types of important morphological filters are included (dilation, erosion, opening, and closing) in this work. Opening morphological filter is used to extract spatial features where this filter is implemented by applying two successive sequences dilation and erosion operators. This filter removes the light areas smaller than the structural element in binary images; and in the gray level images, the areas smaller than the structural element and brighter than the neighboring regions are removed. Differential morphology filters are other important morphological filters, which are also used in this work. In the proposed method, the principal component analysis is used to reduce the data dimensions and an SVM classifier is applied to classify the hyperspectral data. The proposed method provides better classification results than the conventional morphological profile about 2%-5% for the University of Pavia and Pavia Center datasets. The results represent the good performance of the proposed method by using a small number of training samples.
A CHANGE VECTOR ANALYSIS METHOD TO MONITOR DROUGHT USING LANDSAT DATA
Authors: Ebrahimian R. , Alesheikh A.
Published: October 2019

Abstract. Drought has always been a heavy financial burden on a country's economy. To mitigate the impacts of an ongoing drought, spatial information on high risk areas are required. Therefore, the aim of this study is to identify the areas prone to drought in order to minimize the damages through a proper management policy. To achieve this goal, the actual evapotranspiration is considered. The rate of actual evapotranspiration was measured in Najaf Abad using the Surface Energy Balance Algorithm for Land (SEBAL) on Landsat ATM+ images. To present and test the changes in drought, the Change Vector Analysis (CVA) technique was applied to multi-temporal data to compare the differences in the time-trajectory of the tasselled cap intestines and brightness for two time periods 1995 to 2008 – 2008 to 2015. The images were processed using ArcGIS 10.3 and ERDAS Imagine 8.6™ software. The results indicated that the changed area is 9691.41 hectare and the unchanged is 49335.2 hectare between 1995 to 2008. The area of the changed pixels between 2008 and 2015, 3% is higher than the area of the change pixels in 1995 until 2008. In more than 3-quarter of the study area, the value of evapotranspiration has not changed. The proposed method demonstrated immense potentials in monitoring drought change dynamics especially when complemented with field studies.
A NOVEL TIE POINT BASED STRATEGY FOR POINT CLOUD AND IMAGERY DATA FINE REGISTRATION
Authors: Eslami M. , Saadatseresht M.
Published: October 2019

Abstract. Laser scanner generated point cloud and photogrammetric imagery are complimentary data for many applications and services. Misalignment between imagery and point cloud data is a common problem, which causes to inaccurate products and procedures. In this paper, a novel strategy is proposed for coarse to fine registration between close-range imagery and terrestrial laser scanner point cloud data. In such a case, tie points are extracted and matched from photogrammetric imagery and preprocessing is applied on generated tie points to eliminate non-robust ones. At that time, for every tie point, two neighbor pixels are selected and matched in all overlapped images. After that, coarse interior orientation parameters (IOPs) and exterior orientation parameters (EOPs) of the images are employed to reconstruct object space points of the tie point and its two neighbor pixels. Then, corresponding nearest points to the object space photogrammetric points are estimated in the point cloud data. Estimated three point cloud points are used to calculate a plane and its normal vector. Theoretically, every object space tie point should be located on the aforementioned plane, which is used as conditional equation alongside the collinearity equation to fine register the photogrammetric imagery network. Attained root mean square error (RMSE) results on check points, have been shown less than 2.3 pixels, which shows the accuracy, completeness and robustness of the proposed method.
APPLICATION OF B-SPLINE METHOD IN SURFACE FITTING PROBLEM
Authors: Esmaeili F. , Amiri-Simkooei A. , Nafisi V. , Alizadeh Naeini A.
Published: October 2019

Abstract. Fitting a smooth surface on irregular data is a problem in many applications of data analysis. Spline polynomials in different orders have been used for interpolation and approximation in one or two-dimensional space in many researches. These polynomials can be made by different degrees and they have continuous derivative at the boundaries. The advantage of using B-spline basis functions for obtaining spline polynomials is that they impose the continuity constraints in an implicit form and, more importantly, their calculation is much simpler. In this study, we explain the theory of the least squares B-spline method in surface approximation. Furthermore, we present numerical examples to show the efficiency of the method in linear, quadratic and cubic forms and it’s capability in modeling changes in numerical values. This capability can be used in different applications to represent any natural phenomenon which can’t be experienced by humans directly. Lastly, the method’s accuracy and reliability in different orders will be discussed.
ASSESSMENT OF THE EFFECTS OF AIR POLLUTANTS ON BLOOD GLUCOSE CONTROL IN PATIENTS WITH TYPE 2 DIABETES
Authors: Esmaeilzadeh A. , Delavar M. R. , Nasli-Esfahani E.
Published: October 2019

Abstract. Smart care is one of the elements of smart city, which has attracted the attention of many scholars to identify threats to the community's health. Air pollution has a significant contribution to diseases development such as type 2 diabetes, which is a major component of the global disease burden. The objective of this study is to investigate the effect of exposure to air pollutants such as NO2, PM2.5, and PM10 during 2016 on blood glucose control in Type 2 diabetic patients living in Tehran, capital of Iran. In this study, 124 diabetic patients of type 2 and partial correlation, odds ratio, and one-way Analysis of Variance have been considered to determine the effect of pollutants on the control of blood glucose in the patients. The results of this study verified that a significant positive correlation exists between NO2 and blood glucose in women (r = 0.43; p-value < 0.001). There was also a relatively low but significant correlation in the female group between PM2.5 and blood glucose have been identified (r = 0.27; p-value = 0.033). No significant correlation was found between pollutant PM10 and blood glucose in the patients. It is noteworthy that no correlation was found in the men's group for any of the pollutants.
A COMPARATIVE STUDY OF UTILIZING MOBILE-GIS TECHNOLOGY TO COLLECT ONLINE CRIME
Authors: Fadaei H. , Bayazidi M.
Published: October 2019

Abstract. Mobile-Geographic Information System (GIS) is one of the dimensions of these enhancements, which, due to its flexibility, its simplicity with traditional GIS systems, the need for less expertise and low cost, has brought this technology into a variety of areas. Mobile technologies using mobile devices is proving to be a more effective implementation for crime detection and monitoring. Mobile-GIS are operating within a wider range of GIS technologies, wireless technologies, and global positioning technology. The ability to use this system to report crime and control crime in cities has led different countries to use this system for easier and less costly reporting of citizens, and many developed countries use Mobile-GIS technology to do this. And they use this technology depending on their needs. In this research, Mobile-GIS applications in the five developed countries of the world are being studied and their disadvantages and advantages are discussed, and finally, regarding to society safety the possibilities and necessity of police department to develop such programs are discussed. In this study, we are proposing a crime reporting and management model using GIS technologies and review the all possibility and limitation to implementation are confronted in order to mitigate the challenges are faced by most developing countries in reducing the crime rates. A conceptual plan for constructing such a program in the police department has been proposed, which is suggested by using systems like Real-time kinematic (RTK) to improve the user's location to increase accuracy.
INVESTIGATING THE EFFECT OF PSO ALGORITHM ON REDUCING CONTROL POINTS IN CAMERA CALIBRATION
Authors: Fakhri S. A. , Fakhri S. A.
Published: October 2019

Abstract. Over the past decades, non-metric cameras have been utilized in functions with less accuracy requirement or even in precise works with the progress of photogrammetric cameras developed technology. One of the reasons to use these kinds of cameras is due very much to their lower costs in comparison with the metric ones. Since we have always error in measurements, there is no exception in Photogrammetry, which is more in the non-metric cameras than the metric ones. Some of these errors are systematic and the only way to cope with them is to model. So far, many models have been proposed to investigate and modify the behavior of errors. Some of these models are linear and some others are non-linear. The number of parameters in each model is different based on the complexity of error in each image. Since the picture need to be connected to earth to calculate the calibration parameters of the camera, therefore this connection is generally made through points known as the ground control points, and more of these control points are needed in the complex models with more sophisticated calculations. Using a method to reduce the need for less control points and achieving a suitable accuracy is beneficial due to the high cost and time-consuming process of preparing the control points. One of the methods that could be used in solving the calibration equations is the Particle Swarm Optimization (PSO) algorithm. Images of a few targets are captured in this research by a non-metric camera and the collinearity equations are used by adding further correction terms in order to calibrate the camera. The results of PSO method are compared with the classical mathematical methods in each step by reducing the number of control points, which indicated that the performance of using the PSO algorithm is better than the conventional proration methods in reducing the number of points and could be utilized in projects suffering from the lack of control points.
ROAD CRACK DETECTION USING GAUSSIAN/PREWITT FILTER
Authors: Fakhri S. A. , Fakhri S. A. , Saadatseresht M.
Published: October 2019

Abstract. Crack is one of the main problems of roads that can reduce the quality of the road or put it in danger in terms of safety. These cracks are needed to be identified first in order to be investigated and followed up. So detecting cracks is one of the most important parts of the road maintenance procedures, which has been considered in recent years. On the other hand, traditional manual methods of crack detection are very time-consuming and dangerous. It is timely because human resources must have thorough and accurate road visits and examine their qualitative status. For this reason, the automatic methods are utilized instead of these methods to increase the speed and reliability of the crack analysis in intelligent transport systems. A simple method is used in this research to detect the crack. Based on the proposed method, a softening filter is applied first on the image to reduce the noise, and then an edge detection filter is applied to the image. Generally, the noise still exists in the image after applying these filters. A window is used here that scans all the image and calculates the average standard deviation for all the pixels in each window, and removes the noise by considering a range. Then the process of removing noise is done with more stringency by reducing the search window in each iteration. Finally, this method was compared with one of the most prominent and modern methods of detecting cracks using a random forest method and the results indicated that despite the simplicity and the speed of the existing method in this study, it has an acceptable performance compared to the manual and random forest methods.
IMPACT OF POLARIMETRIC SAR SPECKLE REDUCTION ON CLASSIFICATION OF AGRICULTURE LANDS
Authors: Farhadiani R. , Homayouni S. , Safari A.
Published: October 2019

Abstract. Presence of speckle in the Polarimetric Synthetic Aperture Radar (PolSAR) images could decrease the performance of information extraction applications such as classification, segmentation, change detection, etc. Hence, an essential pre-processing step named de-speckling is needed to suppress this granular noise-like phenomenon from the PolSAR images. In this paper, a comparison study is conducted between several new PolSAR speckle reduction methods such as POSSC, PNGF, and ANLM. For this comparison, a 4-look L-band AIRSAR NASA/JPL PolSAR dataset that obtained over an agriculture land from Flevoland, Netherlands, was employed. The de-speckling assessment was completed based on some no-reference quantitative indicators. All the de-speckling methods were evaluated in terms of speckle reduction form homogeneous areas, details, and radiometric preservation, and retaining the polarimetric information. Furthermore, the impact of PolSAR de-speckling on classification was evaluated. For this purpose, Support Vector Machine (SVM) classifier was used to classify H/A/Alpha decomposition. Experimental results showed that the ANLM method was better to suppress the speckle, followed by the PNGF method. Also, the classification results showed that a proper PolSAR de-speckling could effectively increase the classification accuracy. The improvement of the Overall Accuracy based on de-speckling using the ANLM method was approximately 22% and 13% higher than the POSSC and PNGF methods, respectively.