bluuebunny/crossref_metadata_2025 huggingface dataset:
PREDICTING SUGARCANE YIELDS IN KHUZESTAN USING A LARGE TIME-SERIES OF REMOTE SENSING IMAGERY REGION
Authors: Khosravirad M. , Omid M. , Sarmadian F. , Hosseinpour S.
Published: October 2019

Abstract. This study aimed to evaluate the power of various vegetation indices for sugarcane yield modelling in Shoeibeyeh area in Khuzestan province of Iran. Seven indices were extracted from satellite images and were then converted to seven days' time-series via interpolation. To eliminate noise from the time-series data, all of them were reconstructed using the Savitzky-Golay algorithm. Thus seven different time-series of vegetation indices were obtained. The growth profile was drawn via averaging of NDVI time-series data and was divided into three growth intervals. Then the accumulative values of vegetation indices related to first and second periods of growth (from 2004 to 2016 extracted from time-series data) were evaluated by simple linear regression models against the average observed yields efficiency. The result showed the accumulative IAVI (γ = 1.4) vegetation index relative to first period of growth with R2 = 0.66 and RMSE = 3.78 ton/ha and the accumulative NDI vegetation index relative to second period of growth with R2 = 0.66 and RMSE = 3.79 ton/ha and the accumulative NDI vegetation index relative to sum of the first and the second growth periods with R2 = 0.78 and RMSE = 3.09 ton/ha had good agreement with sugarcane stem yield efficiency at the middle of growth and before harvesting season.
COMPARISON OF DECOMPOSITION METHODS OVER AGRICULTURAL FIELDS USING THE UAVSAR POLARIMETRIC SYNTHETIC APERTURE RADAR
Authors: Kiana E. , Homayouni S. , Sharifi M. A. , Farid-Rohani M. R.
Published: October 2019

Abstract. This paper investigates and compares the potential of five model-based polarimetric decompositions, namely those developed by Eigenvector-based decomposition (Van Zyl), Model-based decomposition (Freeman-Durden three-component decomposition and Yamaguchi four-component decomposition), An & Yang3 and An & Yang4 for crop biomass detection over agricultural fields covered by various crops. The time series of Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) data and the ground truth of soil and vegetation characteristics collected during the Soil Moisture Active Passive (SMAP) Validation Experiment in 2012 (SMAPVEX12) were used to compare the five decomposition methods with related to the scattering mechanisms and the biomass retrieval performances. The results show that the performance of each decomposition method for biomass retrieval depends on the crop types and the crop phenological stages. Finally, an overall biomass underestimation was observed from the five decompositions, and the highest regression value of 99% was obtained from Freeman decomposition as a result of the enhanced volume scattering. Indeed, Freeman-Durden model provided the best results.
THE APPLICABILITY OF DUAL POLARIZED SENTINEL-1 SAR DATA FOR DETECTION OF FLOODED AREAS IN POL-E DOKHTAR, LORESTAN, IRAN
Authors: Kianfar N.
Published: October 2019

Abstract. The main source for acquiring enough information about the areas of flood for the purpose of quick mapping is Microwave remote sensing because of its capability of imaging even in serious conditions of weather. Sentinel-1 with HV polarization is way more practical than VV polarization for detecting the flooded area due to its sensivity to short ripples over the surface of the flood. Despite this privilege, at low incidence angles, the same difficulty is observed in HV polarization. In this research, the main focus is on extracting the flood inundation with dual polarized sentinel-1 data in high and low incidence angles. The extraction of backscatter values for flood surface in VV and also HV polarizations had been done. The backscatter from floodwater is high at low incidence angles and sometimes low at high incidence angles as is illustrated by Sentinel-1 HV polarized data. However, the alteration is marginal at those high and low incidence angles with using Sentinel-1 VV polarized data. A measurement between Sentinel-1 VV & HV datasets and Optical Sentinel-2 had been done. The extension of flood is higher when we use Sentinel-2 as opposed to VV & HV and also the flood extent from VV at low incidence angles is way lesser. Another observation is that even at diverse incidence angles, the combination of flood mapping from VV & HV have the ability to compare with optical data much better.
ROAD RECOGNITION BASED ON DECISION LEVEL FUSION OF SAR AND OPTIC DATA
Authors: Lazari Zare M. , Tabib Mahmoudi F.
Published: October 2019

Abstract. Road recognition and extraction based on remotely sensed data is efficient and applicable in much urban management studies. In this research, the capabilities of SPOT and SAR images are investigated for road recognition. Spectral and textural similarities between roads and other urban objects such as building’s roofs many cause some difficulties in road recognition based on SPOT image. On the other hand, SAR images are good for small road recognition but, may have some difficulties for detecting roads among vegetation. The proposed method in this paper is a decision level fusion of SPOT and SAR classification results in order to modify extracted road regions. This method has three main steps; 1) texture feature extraction from each of the SPOT and SAR images, 2) classifying each of the SPOT and SAR images based on SVM classifier, 3) decision level fusion of classification results in order to reduce road recognition difficulties and having optimum road regions. Performing the capabilities of the proposed decision level fusion algorithm for road recognition can improve the quality of the classification for about 21%.
ASSESSMENT OF LOW-COST PARTICULATE MATTER SENSORS
Authors: Lehmann K. , Minhans A. , Fajari M. K. , Hahn M.
Published: October 2019

Abstract. The effect of particulate matter is increasingly gaining significance due to its harmful effects on human and urban ecosystems. In view of it, many communities worldwide are collecting air quality data privately to influence their policy makers to make stricter provisions for reducing harmful emissions and thereby improving their quality of life. Likewise, in many German cities, a community of air quality monitors which rely on low-cost PM Sensors is gathering momentum. Such communities possess privately-owned & low-cost air quality monitoring devices that claim to accurately measure PM concentrations and are openly accessible via internet. One such initiative is an air quality monitoring network viz. “luftdaten.info”, which contains of more than 300 low-cost sensors that consistently obtains PM data, colloquially referred as fine dust, in the city of Stuttgart as well as its surrounding districts. Besides, eight stations are continuously monitoring PM concentration in Stuttgart; these are operated by the State Environmental Agency (LuBW- Landesanstalt für Umwelt Baden-Württemberg). Stuttgart University of Applied Sciences (HFT) has currently installed 7 low-cost PM sensors to monitor and study PM concentration in one of its projects. This study endeavors to relate PM 2.5 and PM 10.0 using low-cost sensors. It intends to investigate the reliability of the measured PM concentration using such low-costs sensors once these are placed horizontally and vertically apart and comparing the measures of the 7 sensors. Another objective is to compare the PM concentration measurements with a meteorological station operated by the State of Baden-Wuerttemberg in the vicinity. A correlation analysis is performed to develop understanding of relationships of PM concentration with meteorological parameters, viz. with respect to ambient temperature, air pressure, humidity, wind speed and wind direction. Furthermore, it attempts to develop a regression model using above listed meteorological parameters. Finally, deficiencies in the measurement of low-costs and its placement effects are commented. Further suggestions are made for improving the data capturing and analytical procedures while using low-cost sensors.
CHANGES IN EVAPOTRANSPIRATION AND THE POTENTIAL DRIVERS IN ASIAN ARID REGIONS DURING 2003 TO 2017
Authors: Liu X. , Tang Q.
Published: October 2019

Abstract. Evapotranspiration (ET) has changed greatly across the world in the past decades. There are various drivers that may affect the changes in ET. Understanding the changes in ET and the potential drivers is of great importance to water resources management in the arid regions. In this study, we examine the trends in ET using satellite-based data in the Asian arid regions from 2003 to 2017. Trends in different ET components, i.e., the evaporation from bare soil and transpiration from vegetation, are estimated to examine their respective contributions to the changes in ET. The results show that the evaporation from bare soil is the main component of ET and has contributed larger to the changes in ET from 2003 to 2017. Statistically significant increasing trend is mostly found in Southwest Pakistan, Northwest India, Southeast Iran, many areas in Northwest China, and some parts in South Kazakhstan. The relations between ET and the global sea surface temperature (SST) are also examined. It suggests that the interannual variation of ET is closely related to annual SST anomalies in most areas, especially in Kazakhstan, Turkmenistan, Northwest China, Southwest Pakistan, and some parts of eastern Iran. This analysis provides useful information for water resources management and planning with respect to forest and agricultural development in the Asian arid regions.
SINGLE-IMAGE DEHAZING ON AERIAL IMAGERY USING CONVOLUTIONAL NEURAL NETWORKS
Authors: Madadikhaljan M. , Bahmanyar R. , Azimi S. M. , Reinartz P. , Sörgel U.
Published: October 2019

Abstract. Haze contains floating particles in the air which can result in image quality degradation and visibility reduction in airborne data. Haze removal task has several applications in image enhancement and can improve the performance of automatic image analysis systems, namely object detection and segmentation. Unlike rich haze removal literature in ground imagery, there is a lack of methods specifically designed for aerial imagery, considering the fact that there is a characteristic difference between the aerial imagery domain and ground one. In this paper, we propose a method to dehaze aerial images using Convolutional Neural Networks (CNNs). Currently, there is no available data for dehazing methods in aerial imagery. To address this issue, we have created a syntheticallyhazed aerial image dataset to train the neural network on aerial hazy image dataset. We train All-in-One dehazing network (AODNet) as the base approach on hazy aerial images and compare the performance of our proposed approach against the classical model. We have tested our model on natural as well as the synthetically-hazed aerial images. Both qualitative and quantitative results of the adapted network show an improvement in dehazing results. We show that the adapted AOD-Net on our aerial image test set increases PSNR and SSim by 2.2% and 9%, respectively.
SURVEY OF LIGHT POLLUTION OF ARAK CITY BY USING OF DMSP AND SUONI-NPP SATELLITE IMAGERY
Authors: Maghsoodi M. O. , Kazemi A. , Hedayati Aghmashadi A. , Gili M. R.
Published: October 2019

Abstract. Today, the use of remote sensing techniques and satellite images of night lights has immensely assisted in investigating this kind of pollution. The objectives of this research is to survey the light pollution of the city of Arak in Iran for the years 1997 and 2009, by utilizing satellite images of DMSP night lights and for the years 2013 through 2018, by taking advantage of Soumi-NPP satellite images of night lights and exploring its connection to the city’s electricity power consumption for street lighting. The night light images of the DMSP and NPP satellites, depending on the degree of light intensity, were each, in segregation, categorized into five classes of light pollution levels, ranging from very high to very low levels. Next, light pollution maps were extracted from classified images and then the connection of the ‘high and very high’ classes of light pollution with electric power consumption for street or road lighting was surveyed in these years. Results of the study indicates that the amount of light pollution in these years was in relevance with road or street lighting, as in the years when the amount of electricity consumption for road lighting was high and its role in connection with light pollution was also in great percentages; and in years with a decrease in electricity consumption for road lighting, lower percentages were displayed.
PHOTOMETRIC STEREO ASSISTED DRAWING OF ARCHITECTURAL RELIEFS
Authors: Abzal A. , Saadatseresht M. , Varshosaz M.
Published: October 2019

Abstract. Geometric documentation is one of the most important parts of a documentary report. Despite the advances made in the field of line drawing of ancient relief surfaces, in most cases, human operator interaction is unavoidable. In this paper, an algorithm for the semiautomatic line drawing of relief surfaces has been developed. In the proposed method, photometric stereo normals are used as a highresolution and low-noise data for the automatic extraction of surface edges. The normals are computed in 2D image space and also the fringe projection scanner is used for geometric correction of 2D image based drawings. Therefore, the drawings are converted to a metric map for geometric documentation reports. The results show that the efficiency of the proposed method, which has managed to correctly draw about more than 99% of the edge lines of an ancient relief. Also all of the drawn lines are completely coincided to the relief edges on its orthophoto image.
EVALUATION OF SPATIAL JUSTICE IN ACCESSIBILITY OF URBAN FACILITIES: A CASE STUDY OF ACCESSIBILITY OF PUBLIC PARKS IN DISTRICT # 11 OF TEHRAN, IRAN
Authors: Mahmoudi S. , Jelokhani-Niaraki M. R. , Argany M.
Published: October 2019

Abstract. The green spaces and urban parks play a critical role to improve the quality of life of citizens. In order to meet the principles of the justice-based city and spatial justice in the distribution of public services, it is necessary to evaluate the accessibility to parks in different locations and pay more attention to the places with a low level of accessibility. This study evaluates the spatial justice or inequalities in accessibility to urban parks in District # 11 of Tehran, Iran using a set of spatial indices and GIS tools. Indices used in this study are Covering, Minimum distance, Average distance, Proximity, Two-step floating catchment area and Gravity-based two-step floating catchment area. The results indicate that the level of accessibility to mini and neighborhood parks are almost similar and below the average level. The slight differences in the results are related to the differences in the assumptions and logics of methods. Moreover, the results show that the Coverage, 2SFCA and GB2SFCA (with inappropriate distance decay coefficient) methods face limitations when the accessibility for the community parks are calculated. For example, the Coverage method does not take into account the area of the park for measuring the accessibility level, in turn, this leads to inaccurate results. Overall, the findings show better accessibility to community parks than mini and neighborhood parks. This implies that the municipalities need to increase the number of local mini and neighborhood parks across the city.
EXTRACTING POINT OF INTERESTS FROM MOVEMENT DATA USING KERNEL DENSITY AND WEIGHTED K-MEANS
Authors: Malekzadeh M. , Javanmard R. , Karimipour F.
Published: October 2019

Abstract. Development in spatial data acquisition techniques, facilitate the process of analyzing movement characteristics and removed the lack of spatial data challenge. Annually, an enormous amount of spatial data are produced, and interpretation of this volume of data has become a major challenge. In this study, the movement data of 157 users in Geneva, Switzerland, were used and attempted to analyze their movement patterns. After the pre-processing stage, in order to investigate the dense areas, Kernel Density is calculated for each point for its neighborhood. The size of each cell of the output raster is approximately 100 meter. Afterward, in order to find the point of interests in the Geneva city, Weighted K-means is used for clustering of the raster. The kernel value of each cell is considered as the weight of the cell. Subsequently, the centroid of each final cluster has reflected the point of interest. As a final point, with the intention of assessing the results, the land use of the area is compared to each point of interest. Eventually, an interpretation is given.
INTEGRATION OF MOBILE GIS AND LINKED DATA TECHNOLOGY FOR SPATIO-TEMPORAL TRANSPORT DATA MODEL
Authors: Margan B. , Hakimpour F.
Published: October 2019

Abstract. Linked Data is available data on the web in a standard format that is useful for content inspection and insights deriving from data through semantic queries. Querying and Exploring spatial and temporal features of various data sources will be facilitated by using Linked Data. In this paper, an application is presented for linking transport data on the web. Data from Google Maps API and OpenStreetMap linked and published on the web. Spatio-Temporal queries were executed over linked transport data and resulted in network and traffic information in accordance with the user’s position. The client-side of this application contains a web and a mobile application which presents a user interface to access network and traffic information according to the user’s position. The results of the experiment show that by using the intrinsic potential of Linked Data we have tackled the challenges of using heterogeneous data sources and have provided desirable information that could be used for discovering new patterns. The mobile GIS application enables assessing the profits of mentioned technologies through an easy and user-friendly way.
INSAR TIME SERIES INVESTIGATION OF LAND SURFACE DEFORMATION IN AZAR OIL FIELD
Authors: Mirzaii Z. , Hasanlou M. , Samieie-Esfahany S. , Rojhani M. , Ajourlou P.
Published: October 2019

Abstract. Time-series interferometric synthetic aperture radar (InSAR) has developed as an influential method to measure various surface deformations. One of the generations of time-series InSAR methodologies is Persistent Scatterer Interferometry (PSI) that focuses on targets with a high correlation over time. In this study, we have measured the surface deformation in Azar Oil Field utilizing time series analysis. Azar Oil Field is one of Iran's oil fields. This oil field is located in the east of the city of Mehran, Ilam province. The reservoir of this oil field is shared by Iraq oil field whose name is Badra where oil extraction started in 201409. While Iran started oil exploration in 201709, Iraq has maximized its oil exploration ever since. The subsidence is mainly observed in the vicinity of the oil field. The Stanford Method for Persistent Scatterers (StaMPS) package has been employed to process 20 descending ENVISAT-ASAR images collected between 2003 and 2009, as well as 50 descending Sentinel-1A satellite images collected between 2014 and 2019. Sentinel-1 images bring new improvements due to their wide coverage and high revisiting time, which allows us to make a wide area processing. Due to the high depth of oil wells (4,300 meters), as well as the stone type of the region’s bed in some areas, we needed to calculate the magnitude of subsidence. The results show the maximum displacement rate in this area is 18 mm between 2014 and 2019 in the radar line of sight direction, but no subsidence took place between 2003 and 2009 .The results of the study confirm typical patterns of subsidence induced by oil extraction. Also, since 2017, with the onset of Iran’s oil extraction and the intensification of Iraq's oil exploration, subsidence has taken place with a steeper slope. The displacement of the area before and after this date is modelled with two lines.
AUTOMATIC VEHICLE RECOGNITION FOR URBAN TRAFFIC MANAGEMENT
Authors: Mohammadi M. , Tabib Mahmoudi F. , Hedayatifard M.
Published: October 2019

Abstract. Automatic vehicle recognition has an important role for many applications such as supervision, traffic management and rescue tasks. The ability of online supervision on the distribution of vehicles in urban environments prevents traffic, which in turn reduces air pollution and noise. However, this is extremely challenging due to the small size of vehicles, their different types and orientations, and the visual similarity to some other objects in very high resolution images. In this paper, an automatic vehicle recognition algorithm is proposed based on very high spatial resolution aerial images. In the first step of the proposed method, by generating the image pyramid, the candidate regions of the vehicles are recognized. Then, performing reverse pyramid, decision level fusion of the vehicle candidates and the land use/cover classification results of the original image resolution are performed in order to modify recognized vehicle regions. For evaluating the performance of the proposed method in this study, Ultracam aerial imagery with spatial resolution of 11 cm and 3 spectral bands have been used. Comparing the obtained vehicle recognition results from the proposed decision fusion algorithm with some manually selected vehicle regions confirm the accuracy of about %80. Moreover, the %78.87 and 0.71 are respectively the values for overall accuracy and Kappa coefficient of the obtained land use/cover classification map from decision fusion algorithm.
NEURAL NETWORK METHOD FOR DROUGHT MODELING USING SATELLITE DATA
Authors: Mokhtari R. , Akhoondzadeh M.
Published: October 2019

Abstract. Drought is one of the natural crises in each region. Drought has a direct relationship with vegetation. Various factors affect vegetation. The relationship between these factors and vegetation can be expressed using methods of machine learning algorithms. Nowadays, using remote sensing images can be used to measure the factors affecting vegetation and investigate this phenomenon with high precision. In this research, vegetation and various factors affecting this factor, which can be measured using satellite imagery, are selected. The factors include land surface temperature (LST), evapotranspiration (ET), snow cover, rainfall, soil moisture that which are derived from the active and passive sensors of satellite sensors as the products of land surface temperature (LST), snow cover and vegetation, using images of products of the MODIS sensor and rainfall using the images of the TRMM satellite and soil moisture using the images of the SMOS satellite during a period from June 2010 to the end of 2018 for the central region of Iran has received and after that, primary processing was performed on these images. The vegetation index (NDVI) is modeled using artificial neural network algorithm for monthly periods. method have been able to achieve model with desirable accuracy. The average accuracy was RMSE = 0.048 and R2 = 0.867.
EXAMINING ASSOCIATIONS OF THE SOCIOECONOMIC CHARACTERISTICS WITH THE NUMBER OF GEO-TAGGED TWEETS IN CENSUS BLOCK LEVEL (CASE STUDY: BOSTON)
Authors: Molavi Gonabadi M. , Mojtabaee P. , Taleai M.
Published: October 2019

Abstract. In this study, the aim is to help uncover some facts about who the Twitter users really are. To this purpose, geotagged twitter data from the city of Boston together with socio-economic data were used. In the first step, tweets in each census block were counted and using the Getis-Ord Gi* index the hotspots and coldspots of tweet locations were extracted. Then, a multiple linear regression was employed, having the number of tweets as the response variable and the population data, age, education, occupation and income as the explanatory variables. Hence, more insight into the relationship between the number of tweets and some socio-economic factors is obtained. Results show that the central parts of Boston are the hotspot and the southern areas are the coldspot locations with regard to tweet numbers. The regression results imply that the number of tweets shared by users in an area is related to the income, the number of people having a university degree and the number of people having each type of job in that spatial unit. The results achieved in this paper could lead to a better vision and understanding in analyzing the tweeter users’ behavior in any area of research and application.
SIMULTANEOUS REGISTRATION AND INTEGRATION OF TWO SEQUENTIAL VELODYNE POINT CLOUDS USING VOXEL-BASED LEAST SQUARE ADJUSTMENT
Authors: Moradi L. , Saadatseresht M.
Published: October 2019

Abstract. In this paper, a model for simultaneous registration and 3D modelling of Velodyne VLP 32e laser scanner point clouds based on least square adjustment methods was developed. Considering that the most of proposed methods for registration of point clouds which obtained by mobile mapping systems have applications in navigation and visualization. They usually do not pay enough attention to geometric accuracy, error propagation, and weights analysis. In addition, in these methods, some point correspondence solutions are used which increase the computation time and decrease the accuracy. Therefore, the purpose of this paper is to develop a model based on the least square adjustment and focus on the weight of the plane parameters which created by a robust least square fitting algorithm. It also simultaneously creates a 3D environmental model and registers point clouds. To do this, it utilizes both point cloud voxelization and differential planes techniques. The result illustrates the high capability of the proposed solution with the optimum weight of plane parameters to 100, and average distance between two scans can reach to below 10 mm.In addition, the best voxel size was 10 cm which is twice of point cloud resolutions.
PHOTOGRAMMETRIC SOLUTIONS FOR 3D MODELING OF CULTURAL HERITAGE SITES IN REMOTE AREAS
Authors: Mostafavi A. , Scaioni M. , Yordanov V.
Published: October 2019

Abstract. The realistic possibility of using non-metric digital cameras to achieve reliable 3D models has eased the application of photogrammetry in different domains. Documentation, conservation and dissemination of the Cultural Heritage (CH) can be obtained and implemented through virtual copies and replicas. Structure-from-Motion (SfM) photogrammetry has widely proven its impressive potential for image-based 3D reconstruction resulting in great 3D point clouds’ acquisitions but at minimal cost. Images from Unmanned Aerial Vehicles (UAVs) can be also processed within SfM pipeline to obtain point cloud of Cultural Heritage sites in remote regions. Both aerial and terrestrial images can be integrated to obtain a more complete 3D. In this paper, the application of SfM photogrammetry for surveying of the Ziggurat Chogha Zanbil in Iran is presented. Here point clouds have been derived from oblique and nadir photos captured from UAV as well as terrestrial photos. The obtained four point clouds have been compared on the basis of different techniques to highlight differences among them.
DIGITAL SOIL MAPPING WITH REGRESSION TREE CLASSIFICATION APPROACHES BY RS AND GEOMORPHOMETRY COVARIATE IN THE QAZVIN PLAIN, IRAN
Authors: Mousavi S. R. , Sarmadian F. , Rahmani A. , Khamoshi S. E.
Published: October 2019

Abstract. Digital soil mapping applies soil attributes, Remote sensing and Geomorphometrics indices to estimate soil types and properties at unobserved locations. This study carried out in order to comparison two data mining algorithms such as Random Forest (RF) and Boosting Regression tree (BRT) and two features selection principal component analysis (PCA) and variance inflation factor (VIF) for predicting soil taxonomy class at great group and subgroup levels. A total of 61 soil profile observation based on stratified random determined and digged in area with approximately 16660 hectares.19 RS indices and geomorphometrics covariates derivated from Landsate-8 imagery and DEM with 30 meters’ resolution in ERDAS IMAGINE 2014 and SAGA GIS version 7.0 software’s. Also to run four Data mining algorithms scenarios (PCA-RF, VIF-RF, PCA-BRT, VIF-BRT) from “Randomforest” and “C.5” packages were used in R studio software. 80% and 20% from soil profiles were applied for calibrating and validating. The results showed that in PCA and VIF approaches, eight covariates such as (Relative slope position, diffuse insolation, modified catchment, normalized height, RVI, Standard height, TWI, Valley depth) and six covariates (NDVI, DVI, Catchment area, DEM, Salinity index, Standard height) were selected. The validation results based on overall accuracy and kappa index for scenarios at great group level indicated that 88,93,62, 54 and 75,83,51,45 percentages and for subgroup level had 70, 77, 54, 47 and 60, 71, 43, 37 percentages, respectively. Generally, VIF-RF had accuracy rather than from other scenarios at two categorical level in this study area.
AIR POLLUTION ESTIMATION USING AEROSOL OPTICAL THICKNESS BY OLI IMAGES IN TEHRAN
Authors: Mozafari S. M. , Hasanlou M. , Arefi H.
Published: October 2019

Abstract. Air pollution is one of the main problems in human and environmental health in big cities and in developing countries. The precise monitoring and predicting of air quality and the assessing of the amount of contaminants will reduce the risks to human and the environment health. The particulate materials in the atmosphere are divided into two PM2.5 (particulate materials with a diameter of less than 2.5 μm) and PM10 (particulate materials with a diameter of less than 10 μm) groups, the main contributor to the air pollution associated with these pollutants. In this study, an experimental relationship is established between in-situ values of PM2.5 and PM10 with satellite images and a high-precision air pollution model is produced. Also, the impact of some urban parameters such as vegetation on air pollution have been the objectives of this research. Using four year course (From the beginning of 2015 until the end of 2018 from Tehran) from Landsat-8, OLI images and receiving ground data, at the same time, air pollution rates in different parts of Tehran have been investigated. For this purpose, 23 air quality control stations in Tehran have been used. The study suggests that the study of atmospheric reflectance from Landsat-8, OLI is a good alternative to monitoring the quality of air on Earth. The feasibility of the proposed algorithms was investigated based on the correlation coefficient (R) and root mean-square error (RMSE) and normalize root mean-square error (NRMSE) compared with the PM2.5 and PM10 in-situ measurement data. A choice of our proposed multispectral model was founded on the highest value R and lowest value of the RMSE and so and lowest value of NRMSE with PM10 in-situ data. The outcomes of this research showed that visible bands of Landsat 8 OLI were capable of calculating PM2.5 and PM10 concentration to an acceptable level of accuracy.
GROUNDWATER VULNERABILITY MAPPING USING A MODIFIED-DRASTIC MODEL IN AJABSHIR PLAIN, SOUTHEAST COAST OF URMIA LAKE, IRAN
Authors: Nakhostinrouhi M. , Rezaei Moghaddam M. H.
Published: October 2019

Abstract. Groundwater resources play an important role not only in providing drinking water but also in irrigation, industry and power generation. In general, groundwater is a part of the water cycle in nature that can be collected by wells, qanats, drains, or natural springs. In this research, the potential of groundwater vulnerability in Ajabshir plain, located in the Southwest of East Azerbaijan Province and Southeast of the Urmia Lake, Iran, is investigated using 7 hydrogeological parameters as well as land-use criterion. Depth to water map is provided using 26 boreholes. Twenty-seven drilling points are also used in generating aquifer media and impact of vadose zone maps. After providing and ranking all layers, they are multiplied by appropriate weights and overlaid to produce vulnerability map. Modified-DRASTIC model is applied to achieve the aim. According to the results, an approximately large part of the aquifer (29 percent), mostly located in the west of the plain, is covered with moderate vulnerability class. Spearman correlation coefficient is calculated 0.63 between the vulnerability and land use maps.
AN AGENT-BASED MODELLING FOR RIDE SHARING OPTIMIZATION USING A* ALGORITHM AND CLUSTERING APPROACH
Authors: Naseri Gorgoon M. , Davoodi M. , Davoodi M. , Motieyan H.
Published: October 2019

Abstract. Today, city management is one of the great challenges facing the world. The growth of population, industries, and services is in urgent need of transportation on a large scale. Meanwhile, transportation has great importance in urban management. Therefore, it is necessary to solve the traffic problem with scientific methods and reduce the traffic load of cities. An interesting way to reduce urban travels is using 2,3, or 4 people from one car that it is known as “Ride Sharing”. In this research, the NetLogo software is used to simulate travel sharing scenarios. The three considered parameters are the number of passengers, the acceptable travel sharing radius, and the acceptable waiting time. The proposed algorithm uses a clustering method to find the best candidates to share a ride. Several scenarios were performed to evaluate numerical results. The number of passengers was 100, and 500, the radius of the trip was 1,000 and 2,000 meters, and the waiting time was 10 and 20 minutes. So, 8 experiments were carried out. The least amount of travel sharing was observed in the first scenario (100 passengers, 1000 m travel sharing radius and 10 minutes waiting time), in which 2% of single trips dropped out. The most sharing trips were in the final scenario (500 passengers, 2000 meters radius and 20 minutes waiting time), which saw a decrease of 36.4% of single trips. So, it can be said that sharing a trip can reduce traffic in cities and consequently reduce urban costs and either air pollution or noise pollution.
FLOOD MAPPING AND PERMANENT WATER BODIES CHANGE DETECTION USING SENTINEL SAR DATA
Authors: Nasirzadehdizaji R. , Akyuz D. E. , Cakir Z.
Published: October 2019

Abstract. Producing flood maps that can be carried out quickly for disaster management applications is essential to reduce the human and socio-economic losses. In addition, mapping and change detection of water bodies as an essential natural resource is imperative for robust operations and sustainable management. Synthetic Aperture Radar (SAR) sensors with long wavelengths have a high potential for delineating the extent of the flooded areas and providing timely and accurate maps of surface water for risk mitigation and disaster or sustainable management. In this study, multi-temporal Sentinel-1 C-band SAR images were utilized to investigate the performance of the sensor backscatter image on permanent water bodies monitoring and flooded areas mapping. Lake Urmia as a permanent water system and two floods in Golestan and Khuzestan provinces of Iran have been investigated. The backscatter values of an image acquired before the event that is referred as an Archive image and another one after the event as a Crisis image are analysed. As a preliminary result, it is concluded that with overlaying of the two bands from Archive and Crisis images and creating a color composite image, the permanent water bodies have a uniformly dark return due to the very low backscatter in both images. The flooded areas and changes in water level show relatively higher backscatter in the Crisis image, whereas the other land cover features indicate very high backscatter values with tones of grey. Therefore, Sentinel-1 SAR data provides beneficial information on flood risk management and change detection.
UNCERTAINTY MODELLING OF CITIZEN-CENTERED GROUP DECISION MAKING USING FUZZY-VIKOR CASE STUDY: SITE SELECTION OF HEALTHCARE SERVICES
Authors: Neisani Samani Z. , Alesheikh A. A.
Published: October 2019

Abstract. The participation of citizens in decision-making processes is one of the main concerns in urban planning. People’s contributions increase the usability and efficiency of urban facilities. Hospitals and healthcare services are among the most important public facilities that citizens require. This paper aims to improve an approach that could locate the hospitals according to the citizens’ preferences. Decision-making process in this situation should consider the uncertainties exist in any steps of decisions-making. In this regard, this paper applied Fuzzy-VIKOR method that is appropriate to model such kind of uncertainty. The proposed method was accomplished in Districts 6 of Tehran province. The achieved results were compared with each other in two different scenario (using expert knowledge and citizens’ satisfaction). The comparison of the results showed that the more suitable distribution and density of proposed sites for hospitals must be observed if the citizens’ perspectives were considered. Also, the proposed sites with experts follow urban planning principals rather than the second case.
USING A PULSE INTEGRATION TECHNIQUE FOR IMPROVEMENT OF RECEIVED SIGNAL POWER IN GROUND BASED SAR SYSTEM
Authors: Nekoee M. , Amini J.
Published: October 2019

Abstract. Today, radar imaging with synthetic aperture has obtained an important place in radar remote sensing, in such a way that it is utilized in the variety of space borne, airborne and ground platforms to extract information from different desired surfaces. Among these, Ground Based Synthetic Aperture Radars (GB-SAR) has numerous applications such as space system calibration, change detection, target detection, surface displacement determination and condition monitoring. In general, in GB-SAR systems, given that the power of the transmitted signal is low, the received signal is also of low power, and subsequently the presence of target in the image is of lower probability. Therefore, in this research, a method is proposed to increase the received signal power based on pulse integration by a non-Coherent method which results in an increase in the Signal to Noise Ratio (SNR), thereby increasing the focusing power on the target. In this way, a large number of samples can be received in the data collection section and the gain can be therefore increased by integrating the number of received pulses. According to the −3.22 dB Improvement of focusing evaluation criterion (PSLR), it is shown in this research that the accuracy of the target detection is increased. Finally, the effect of the number of pulses integrated on the noise presence and accuracy of the target detection is described.
REDUCING THE TIME TO GET EMERGENCY ASSISTANCE FOR ACCIDENT VEHICLES ON THE ROAD THROUGH AN INTELLIGENT TRANSPORTATION SYSTEM
Authors: Nourbakhsh A. H. , Delavar M. R. , Jadidi M. , Moshiri B.
Published: October 2019

Abstract. Intelligent Transportation Systems (ITS) is one of the main components of a smart city. ITS have several purposes including the increase of the safety and comfort of the passengers and the reduction of the road accidents. ITS can enhance safety in three modes before, within and after the collision by preventing accident via assistive system, sensing the collision situation and calculating the time of the collision and providing the emergency response in a timely manner. The main objective of this paper is related to the smart transportation services which can be provided at the time of the collision and after the accident. After the accident, it takes several minutes to hours for the person to contact the emergency department. If an accident takes place for a vehicle in a remote area, this time increases and that may cause the loss of life. In addition, determination of the exact location of the accident is difficult by the emergency centres. That leads to the possibility of erroneous responder act in dispatching the rescue team from the nearest hospital. A new assistive intelligent system is designed in this regard that includes both software and hardware units. Hardware unit is used as an On-Board Unit (OBU), which consists of GPS, GPRS and gyroscope modules. Once OBU detects the accident, a notification system designed and connected to OBU will sent an alarm to the server. The distance to the nearest emergency center is calculated using Dijkstra algorithm. Then the server sends a request for assistance to the nearest emergency centre. The proposed system is developed and tested at local laboratory conditions. The results show that this system can reduce Ambulance Arrival Time (AAT). The preliminary results and architecture of the system have been presented. The inclination angle determined by the proposed system along with the car position identified by the installed GPS sensor assists the crash/accident warning part of the system to send a help request to the nearest road emergency centre. These results verified that the probability of having a remote and smart car crash/accident decision support system using the proposed system has been improved compared to that of the existing systems.
MAPPING BATHYMETRY BY LANDSAT DATA IN SHALLOW COASTAL ENVIRONMENT (CASE STUDY: THE SOUTHEAST OF THE CASPIAN SEA)
Authors: Amini L. , Kakroodi A. A.
Published: October 2019

Abstract. Compared to traditional methods, remote sensing (RS) technique is an efficient, fast, low-priced and effective solution in bathymetry. In this study, a linear equation was applied between field and reflectance data to achieve bathymetry map. Landsat-8 is characterized by additional visible band compared with previous Landsat bands on coastal zone application, therefore, we apply two regions of spectrum to compare the accuracy of bathymetry, Bands 1, 3, 4 and bands 2, 3 and 4 respectively. Furthermore, Logarithmic transformation also applied on both spectral regions to have bathymetry map. The coefficient obtained from reflectance and filed data was applied on Tm bands to retrieve the bathymetry of Gomishan lagoon in 2000. The results show that the second spectral region, Bands 2, 3 and 4, is highly correlated with filed data and more accurate (R2 = 0.94, RMSE = 0.35). Moreover, there is a relatively high accuracy between the retrieval of Gomishan bathymetry and the report of recent studies.
MEMS BASED BRIDGE MONITORING SUPPORTED BY IMAGE-ASSISTED TOTAL STATION
Authors: Omidalizarandi M. , Neumann I. , Kemkes E. , Kargoll B. , Diener D. , Rüffer J. , Paffenholz J.-A.
Published: October 2019

Abstract. In this study, the feasibility of Micro-Electro-Mechanical System (MEMS) accelerometers and an image-assisted total station (IATS) for short- and long-term deformation monitoring of bridge structures is investigated. The MEMS sensors of type BNO055 from Bosch as part of a geo-sensor network are mounted at different positions of the bridge structure. In order to degrade the impact of systematic errors on the acceleration measurements, the deterministic calibration parameters are determined for fixed positions using a KUKA youBot in a climate chamber over certain temperature ranges. The measured acceleration data, with a sampling frequency of 100 Hz, yields accurate estimates of the modal parameters over short time intervals but suffer from accuracy degradation for absolute position estimates with time. To overcome this problem, video frames of a passive target, attached in the vicinity of one of the MEMS sensors, are captured from an embedded on-axis telescope camera of the IATS of type Leica Nova MS50 MultiStation with a practical sampling frequency of 10 Hz. To identify the modal parameters such as eigenfrequencies and modal damping for both acceleration and displacement time series, a damped harmonic oscillation model is employed together with an autoregressive (AR) model of coloured measurement noise. The AR model is solved by means of a generalized expectation maximization (GEM) algorithm. Subsequently, the estimated model parameters from the IATS are used for coordinate updates of the MEMS sensor within a Kalman filter approach. The experiment was performed for a synthetic bridge and the analysis shows an accuracy level of sub-millimetre for amplitudes and much better than 0.1 Hz for the frequencies.
AERIAL POINT CLOUD CLASSIFICATION WITH DEEP LEARNING AND MACHINE LEARNING ALGORITHMS
Authors: Özdemir E. , Remondino F. , Golkar A.
Published: October 2019

Abstract. With recent advances in technology, 3D point clouds are getting more and more frequently requested and used, not only for visualization needs but also e.g. by public administrations for urban planning and management. 3D point clouds are also a very frequent source for generating 3D city models which became recently more available for many applications, such as urban development plans, energy evaluation, navigation, visibility analysis and numerous other GIS studies. While the main data sources remained the same (namely aerial photogrammetry and LiDAR), the way these city models are generated have been evolving towards automation with different approaches. As most of these approaches are based on point clouds with proper semantic classes, our aim is to classify aerial point clouds into meaningful semantic classes, e.g. ground level objects (GLO, including roads and pavements), vegetation, buildings’ facades and buildings’ roofs. In this study we tested and evaluated various machine learning algorithms for classification, including three deep learning algorithms and one machine learning algorithm. In the experiments, several hand-crafted geometric features depending on the dataset are used and, unconventionally, these geometric features are used also for deep learning.
OPTIMIZATION OF SUGARCANE HARVEST USING REMOTE SENSING
Authors: Rahimi Jamnani M. , Liaghat A. , Mirzaei F.
Published: October 2019

Abstract. In Iranian sugarcane agro-industries, the harvest time is estimated by sucrose content. Measuring the sucrose content in the juice (pol) during sugarcane harvest season will help users and farmers to achieve the best time for sugarcane harvest, which is important in accurate agricultural management. In harvest season, the pol percent is measured weekly by the destructive method through sampling of different areas of representative farms. In the this method, all fields are not sampled due to the plurality of fields and the need for expenditure and workforce, and the measured samples do not represent the entire area of a field. The aim of this paper is to find an optimal model for determine best harvest time for four sugarcane varieties using satellite vegetation indices, and also to obtain a zoning map which represents the areas ready for harvest during a harvest season in order to achieve maximum sucrose content. The results showed that, compared with NDVI and GVI, GNDVI represented higher correlation with pol (R2=0.885). The optimum values of GNDVI were found to be between 0.5 and 0.55, which indicated the areas with highest sucrose concentration. In addition, the zoning map was presented that makes it possible to separate spatially the areas ready for harvest in each field and they were also showed that central areas of farms ripened (reach maximum sugar content) sooner than sideways.
3D CHANGE DETECTION IN URBAN AREAS BASED ON DCNN USING A SINGLE IMAGE
Authors: Amini Amirkolaee H. , Arefi H.
Published: October 2019

Abstract. In this paper, a novel approach is proposed for 3D change detection in urban areas using only a single satellite images. To this purpose, a dense convolutional neural network (DCNN) is utilized in order to estimate a digital surface model (DSM) from a single image. In this regard, a densely connected convolutional network is employed for feature extraction and an upsampling method based on dilated convolution is employed for estimating the height values. The proposed DCNN is trained using satellite and Light Detection and Ranging (LiDAR) data which are provided in 2012 from Isfahan, Iran. Subsequently, the trained network is utilized in order to estimate DSM of a single satellite image that is provided in 2006. Finally, the changed areas are detected by subtracting the estimated DSMs. Evaluating the accuracy of the detected changed areas indicates 66.59, 72.90 and 67.90 for correctness, completeness, and kappa, respectively.