In foundation engineering, it is necessary to calculate the bearing capacity of soils. The allowable soil bearing capacity required for foundation design is calculated through various empirical methods using geotechnical parameters such as specific gravity and angle of internal friction. Standard Penetration Test (SPT) values of the soil are used in these calculations. Therefore, soil tests which engineers need, are costly and time-consuming. This study aims to determine the soil bearing capacity of Eskişehir city and present soil bearing capacity maps for shallow foundations. The geotechnical parameters of the soil were obtained from 40 borehole data made in the field. Within the scope of the study, bearing capacity maps were created for 0-5 m depth to provide an overview of the bearing capacity of Eskişehir soil. These maps were made in the Geographic Information System (GIS), which has a database that stores and analyses regular data. In addition, these maps can assist engineers working on shallow foundation design on the site.
{"title":"Using GIS for the allowable soil bearing capacity estimation in Eskişehir city center","authors":"Ebru Ci̇velekler","doi":"10.26833/ijeg.1212584","DOIUrl":"https://doi.org/10.26833/ijeg.1212584","url":null,"abstract":"In foundation engineering, it is necessary to calculate the bearing capacity of soils. The allowable soil bearing capacity required for foundation design is calculated through various empirical methods using geotechnical parameters such as specific gravity and angle of internal friction. Standard Penetration Test (SPT) values of the soil are used in these calculations. Therefore, soil tests which engineers need, are costly and time-consuming. This study aims to determine the soil bearing capacity of Eskişehir city and present soil bearing capacity maps for shallow foundations. The geotechnical parameters of the soil were obtained from 40 borehole data made in the field. Within the scope of the study, bearing capacity maps were created for 0-5 m depth to provide an overview of the bearing capacity of Eskişehir soil. These maps were made in the Geographic Information System (GIS), which has a database that stores and analyses regular data. In addition, these maps can assist engineers working on shallow foundation design on the site.","PeriodicalId":42633,"journal":{"name":"International Journal of Engineering and Geosciences","volume":" ","pages":""},"PeriodicalIF":1.9,"publicationDate":"2023-01-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45718982","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Although the traditional-Precise Point Positioning (PPP) technique, may provide a positioning as precise as the relative positioning technique in long-term observation durations since it has the inability to provide high-precise positioning due to ambiguity problem in short-term observations, the interest in the PPP-AR (Ambiguity Resolution) technique has increased. The main purpose of this study is to investigate the performance of traditional-PPP and PPP-AR techniques for monitoring permanent displacements, considering different observation durations based on different satellite combinations. For this purpose, a displacement simulator that can move precisely in one direction and in the horizontal plane over a small distance was used. 6 different displacements were simulated, and all collected GNSS observations were evaluated with traditional-PPP, PPP-AR, and relative methods. Moreover, these methods were examined by considering the Global Positioning System (GPS), European Global Navigation Satellite System (Galileo), and GPS/Galileo satellite combinations. The findings clearly demonstrate that the superiority of the PPP-AR technique over the traditional-PPP technique in short-term observation durations and emphasize that the contribution of multi-GNSS (Global Navigation Satellite System) combinations to both methods.
{"title":"Investigation of the capability of multi-GNSS PPP-AR method in detecting permanent displacements","authors":"Mert Bezcioglu, Tayyib Ucar, C. O. Yigit","doi":"10.26833/ijeg.1140959","DOIUrl":"https://doi.org/10.26833/ijeg.1140959","url":null,"abstract":"Although the traditional-Precise Point Positioning (PPP) technique, may provide a positioning as precise as the relative positioning technique in long-term observation durations since it has the inability to provide high-precise positioning due to ambiguity problem in short-term observations, the interest in the PPP-AR (Ambiguity Resolution) technique has increased. The main purpose of this study is to investigate the performance of traditional-PPP and PPP-AR techniques for monitoring permanent displacements, considering different observation durations based on different satellite combinations. For this purpose, a displacement simulator that can move precisely in one direction and in the horizontal plane over a small distance was used. 6 different displacements were simulated, and all collected GNSS observations were evaluated with traditional-PPP, PPP-AR, and relative methods. Moreover, these methods were examined by considering the Global Positioning System (GPS), European Global Navigation Satellite System (Galileo), and GPS/Galileo satellite combinations. The findings clearly demonstrate that the superiority of the PPP-AR technique over the traditional-PPP technique in short-term observation durations and emphasize that the contribution of multi-GNSS (Global Navigation Satellite System) combinations to both methods.","PeriodicalId":42633,"journal":{"name":"International Journal of Engineering and Geosciences","volume":"1 1","pages":""},"PeriodicalIF":1.9,"publicationDate":"2022-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41725808","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Disasters such as floods and floods are also encountered on the days when the highest flow is recorded, according to the Annual Maximum Flow (AMF) statistics. The Annual Maximum Flow is the highest flow rate ever recorded in a water year. Wherever this flow happens, it usually results in flooding. Snow melts and unexpected precipitation associated with temperature fluctuations are the two most important factors that create flooding. The deluge that follows kills people and destroys property in communities and agricultural lands. As a result, it's critical to predict the flow that causes flooding and take appropriate precautions to limit the damage. The prediction of the probability of the flood event in advance is very important for the safety of life and property of large masses and agricultural lands. Early warning systems, disaster management plans and minimizing these losses are among the important goals of the country's administration. In this study, It is used in five Current Observation Stations (COS) located in Yeşilırmak Basin in Turkey. By using 8 input data including geographical location, altitude and area information of these stations, AMF data were tried to be estimated for each COS. A total of 240 input data was used in the study. The data period covers the years 1964-2012. Unfortunately, AMF values cannot be monitored for all 5 stations used after 2012.Therefore, the data period was stopped in 2012. In this study, Multilayer Artificial Neural Networks (MANN), Generalized Artificial Neural Networks (GANN), Radial Based Artificial Neural Networks (RBANN) and Multiple Linear Regulation (MLR) methods were used. Input data sets were made into 4 packets and these packages were used respectively in both training and testing stages. Root mean square error (RMSE), mean absolute error (MAE) and correlation coefficient (R) were used as the comparison criteria. The results are as follow; MANN (8 Input) (RMSE = 119.118, MAE = 93.213, R = 0.808), and RBANN (2 Input) (RMSE = 111.559, MAE = 81.114, R = 0.900). These results show that AMF can be predicted with artificial intelligence techniques and can be used as an alternative method.
根据年度最大流量(AMF)统计，洪水和洪水等灾害也会发生在有记录的最高流量的日子里。年最大流量是在一个水年中有记录的最高流量。无论这种水流发生在哪里，通常都会导致洪水泛滥。融雪和与温度波动相关的意外降水是造成洪水的两个最重要因素。随之而来的洪水夺去了生命，摧毁了社区和农田的财产。因此，预测导致洪水的流量并采取适当的预防措施来限制损害是至关重要的。提前预测洪涝灾害的发生概率，对广大人民群众的生命财产安全和农用地安全具有重要意义。早期预警系统、灾害管理计划和尽量减少这些损失是该国政府的重要目标。在本研究中，它被用于位于土耳其Yeşilırmak盆地的五个当前观测站(COS)。利用这些站点的地理位置、海拔和面积信息等8个输入数据，尝试估算每个COS的AMF数据。本研究共使用了240个输入数据。数据期为1964-2012年。不幸的是，无法对2012年以后使用的所有5个台站的AMF值进行监测。因此，数据周期在2012年停止。本研究采用多层人工神经网络(MANN)、广义人工神经网络(GANN)、径向神经网络(RBANN)和多元线性调节(MLR)方法。输入数据集分成4个包，分别用于训练和测试阶段。以均方根误差(RMSE)、平均绝对误差(MAE)和相关系数(R)作为比较标准。研究结果如下:MANN(8个输入)(RMSE = 119.118, MAE = 93.213, R = 0.808)和RBANN(2个输入)(RMSE = 111.559, MAE = 81.114, R = 0.900)。这些结果表明，AMF可以用人工智能技术进行预测，并且可以作为一种替代方法。
{"title":"Modeling of Annual Maximum Flows with Geographic Data Components and Artificial Neural Networks","authors":"Esra Aslı Çubukçu, Vahdettin Demir, M. F. Sevimli","doi":"10.26833/ijeg.1125412","DOIUrl":"https://doi.org/10.26833/ijeg.1125412","url":null,"abstract":"Disasters such as floods and floods are also encountered on the days when the highest flow is recorded, according to the Annual Maximum Flow (AMF) statistics. The Annual Maximum Flow is the highest flow rate ever recorded in a water year. Wherever this flow happens, it usually results in flooding. Snow melts and unexpected precipitation associated with temperature fluctuations are the two most important factors that create flooding. The deluge that follows kills people and destroys property in communities and agricultural lands. As a result, it's critical to predict the flow that causes flooding and take appropriate precautions to limit the damage. The prediction of the probability of the flood event in advance is very important for the safety of life and property of large masses and agricultural lands. Early warning systems, disaster management plans and minimizing these losses are among the important goals of the country's administration. In this study, It is used in five Current Observation Stations (COS) located in Yeşilırmak Basin in Turkey. By using 8 input data including geographical location, altitude and area information of these stations, AMF data were tried to be estimated for each COS. A total of 240 input data was used in the study. The data period covers the years 1964-2012. Unfortunately, AMF values cannot be monitored for all 5 stations used after 2012.Therefore, the data period was stopped in 2012. In this study, Multilayer Artificial Neural Networks (MANN), Generalized Artificial Neural Networks (GANN), Radial Based Artificial Neural Networks (RBANN) and Multiple Linear Regulation (MLR) methods were used. Input data sets were made into 4 packets and these packages were used respectively in both training and testing stages. Root mean square error (RMSE), mean absolute error (MAE) and correlation coefficient (R) were used as the comparison criteria. The results are as follow; MANN (8 Input) (RMSE = 119.118, MAE = 93.213, R = 0.808), and RBANN (2 Input) (RMSE = 111.559, MAE = 81.114, R = 0.900). These results show that AMF can be predicted with artificial intelligence techniques and can be used as an alternative method.","PeriodicalId":42633,"journal":{"name":"International Journal of Engineering and Geosciences","volume":" ","pages":""},"PeriodicalIF":1.9,"publicationDate":"2022-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42181003","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The importance of solar energy as a global energy source is expected to grow. Solar power's future looks bright, especially with an aged and deteriorating energy grid and rising fossil fuel prices. More precise methods for assessment of solar capacity are needed as more homes and companies investigate the possibility of small-scale photovoltaic (PV) solar installations. In this study, a spatial solar energy PV potential assessment method based on the combination of LiDAR (Light Detection and Ranging) datasets and GIS (Geographic Information System) is proposed. The proposed methodology is applied to an area in the capital city of Skopje in N. Macedonia, from where the results of the possible annual energy output of PV systems for the selected rooftops were presented. The results of this study are crucial for financial and urban planning, policy formulation for future energy projects and also allows to analyze different mechanisms to promote PV installations on publicly available rooftops.
{"title":"Assessment of the solar energy potential of rooftops using LiDAR datasets and GIS based approach","authors":"Vancho Adjiski, Gordana Kaplan, Stojanče Mijalkovski","doi":"10.26833/ijeg.1112274","DOIUrl":"https://doi.org/10.26833/ijeg.1112274","url":null,"abstract":"The importance of solar energy as a global energy source is expected to grow. Solar power's future looks bright, especially with an aged and deteriorating energy grid and rising fossil fuel prices. More precise methods for assessment of solar capacity are needed as more homes and companies investigate the possibility of small-scale photovoltaic (PV) solar installations. In this study, a spatial solar energy PV potential assessment method based on the combination of LiDAR (Light Detection and Ranging) datasets and GIS (Geographic Information System) is proposed. The proposed methodology is applied to an area in the capital city of Skopje in N. Macedonia, from where the results of the possible annual energy output of PV systems for the selected rooftops were presented. The results of this study are crucial for financial and urban planning, policy formulation for future energy projects and also allows to analyze different mechanisms to promote PV installations on publicly available rooftops.","PeriodicalId":42633,"journal":{"name":"International Journal of Engineering and Geosciences","volume":" ","pages":""},"PeriodicalIF":1.9,"publicationDate":"2022-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48919061","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The Covid-19 epidemic has adversely affected the world in terms of health, education, economic, tourism, social and psychological. During to the epidemic, different measures were taken to prevent the epidemic, such as travel bans, curfews, stopping in production. These measures have reduced and improved air pollution. Within the scope of this study, the change in air pollution in Kocaeli between 2019 and 2021 was examined monthly. PM10 and SO2 maps were created with inverse distance weighted (IDW) technique using geographic information systems technology (GIS). The year 2020, when Covid-19 measures were taken, was compared with 2019 and 2021. Change maps were created by taking the difference between 2020-2019 and 2021-2020 with GIS technology. As a result of the research, it was determined that the level of air pollution decreased in 2020. On the contrary, in 2021, an increase in air pollution levels was observed. In the study, a decrease was observed in PM10 concentration during the Covid-19 lockdowns, however a decrease was not observed for SO2.
{"title":"Seasonal Analysis and Mapping of Air Pollution (PM10 and SO2) During Covid-19 Lockdown in Kocaeli (Turkey)","authors":"Burak Kotan, A. Erener","doi":"10.26833/ijeg.1111699","DOIUrl":"https://doi.org/10.26833/ijeg.1111699","url":null,"abstract":"The Covid-19 epidemic has adversely affected the world in terms of health, education, economic, tourism, social and psychological. During to the epidemic, different measures were taken to prevent the epidemic, such as travel bans, curfews, stopping in production. These measures have reduced and improved air pollution. Within the scope of this study, the change in air pollution in Kocaeli between 2019 and 2021 was examined monthly. PM10 and SO2 maps were created with inverse distance weighted (IDW) technique using geographic information systems technology (GIS). The year 2020, when Covid-19 measures were taken, was compared with 2019 and 2021. Change maps were created by taking the difference between 2020-2019 and 2021-2020 with GIS technology. As a result of the research, it was determined that the level of air pollution decreased in 2020. On the contrary, in 2021, an increase in air pollution levels was observed. In the study, a decrease was observed in PM10 concentration during the Covid-19 lockdowns, however a decrease was not observed for SO2.","PeriodicalId":42633,"journal":{"name":"International Journal of Engineering and Geosciences","volume":" ","pages":""},"PeriodicalIF":1.9,"publicationDate":"2022-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46386457","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Topographic Wetness Index, also known as the compound topographic index, (TWI) is a topographic indicator that calculates the potential of where water is likely to accumulate during excessive precipitation cycles resulting from abrupt atmospheric anomalies. High index values represent serious potential of water accumulation due to low slope, and the opposite for high slope. As expected from the term, slope, Digital Elevation Model (DEM) datasets play an important role in the calculation of TWI. DEMs are produced utilizing tachometry, GPS benchmarking, UAV, aerial or satellite image capture and LIDAR capabilities. However, no matter how it is generated from, a DEM is as good as the actual ground sampling algorithm, on which the final resolution is based. Using five different DEM resolutions coming from three global and one national presented in two different setting coverages, upper feeder basin of Bozkurt sub-province, Kastamonu, was analyzed emphasizing the urbanized part of the sub-province, which was devastated during the August 11th,2021 flood. Coarser resolution missed the overall precision while the finer resolution captured it nicely. On the flip side, finer resolution excessively fragmented the questioned area while the coarser resolution formed a unity coinciding with the destructed area recorded during the event.
{"title":"The effect of DEM resolution on Topographic Wetness Index calculation and visualization","authors":"A. Altunel","doi":"10.26833/ijeg.1110560","DOIUrl":"https://doi.org/10.26833/ijeg.1110560","url":null,"abstract":"Topographic Wetness Index, also known as the compound topographic index, (TWI) is a topographic indicator that calculates the potential of where water is likely to accumulate during excessive precipitation cycles resulting from abrupt atmospheric anomalies. High index values represent serious potential of water accumulation due to low slope, and the opposite for high slope. As expected from the term, slope, Digital Elevation Model (DEM) datasets play an important role in the calculation of TWI. DEMs are produced utilizing tachometry, GPS benchmarking, UAV, aerial or satellite image capture and LIDAR capabilities. However, no matter how it is generated from, a DEM is as good as the actual ground sampling algorithm, on which the final resolution is based. Using five different DEM resolutions coming from three global and one national presented in two different setting coverages, upper feeder basin of Bozkurt sub-province, Kastamonu, was analyzed emphasizing the urbanized part of the sub-province, which was devastated during the August 11th,2021 flood. Coarser resolution missed the overall precision while the finer resolution captured it nicely. On the flip side, finer resolution excessively fragmented the questioned area while the coarser resolution formed a unity coinciding with the destructed area recorded during the event.","PeriodicalId":42633,"journal":{"name":"International Journal of Engineering and Geosciences","volume":" ","pages":""},"PeriodicalIF":1.9,"publicationDate":"2022-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44301327","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This paper focuses on making a comparing of GNSS/Levelling data and data obtained from global geopotential models. For comparison, geoid undulations obtained by GNSS/Levelling method and geoid undulations obtained from global geopotential models have been used. As global geopotential models, SGG-UGM-2, XGM2019e_2159, GO_CONS_GCF_2_TIM_R6e, ITSG-Grace2018s, EIGEN-GRGS.RL04.MEAN-FIELD, GOCO06s, GO_CONS_GCF_2_TIM_R6, GO_CONS_GCF_2_DIR_R6 global gravity field models are used. The data sets used in the development of the models are altimetry, satellite (e.g., GRACE, GOCE, LAGEOS), ground data (e.g., terrestrial, shipborne and airborne measurements) and topography. The differences between the geoid undulations obtained from the GNSS/Levelling method and the geoid undulations obtained from the global geoid models have been taken. Some statistical criteria for these differences have been calculated. These criteria, such as smallest, biggest, average, standard deviation, Square Mean RMS statistical values of deviations between GNSS/Levelling geoid and global geopotential models, are taken into consideration when comparing the models. According to the comparison, the global gravity field model that best fits the GNSS/Levelling is selected.
{"title":"ASSESSMENT OF RECENT GLOBAL GRAVITY FIELD MODELS BY GNSS/LEVELLING DATA","authors":"N. Yilmaz","doi":"10.26833/ijeg.1070042","DOIUrl":"https://doi.org/10.26833/ijeg.1070042","url":null,"abstract":"This paper focuses on making a comparing of GNSS/Levelling data and data obtained from global geopotential models. For comparison, geoid undulations obtained by GNSS/Levelling method and geoid undulations obtained from global geopotential models have been used. \u0000As global geopotential models, SGG-UGM-2, XGM2019e_2159, GO_CONS_GCF_2_TIM_R6e, ITSG-Grace2018s, EIGEN-GRGS.RL04.MEAN-FIELD, GOCO06s, GO_CONS_GCF_2_TIM_R6, GO_CONS_GCF_2_DIR_R6 global gravity field models are used. The data sets used in the development of the models are altimetry, satellite (e.g., GRACE, GOCE, LAGEOS), ground data (e.g., terrestrial, shipborne and airborne measurements) and topography. The differences between the geoid undulations obtained from the GNSS/Levelling method and the geoid undulations obtained from the global geoid models have been taken. Some statistical criteria for these differences have been calculated. These criteria, such as smallest, biggest, average, standard deviation, Square Mean RMS statistical values of deviations between GNSS/Levelling geoid and global geopotential models, are taken into consideration when comparing the models. According to the comparison, the global gravity field model that best fits the GNSS/Levelling is selected.","PeriodicalId":42633,"journal":{"name":"International Journal of Engineering and Geosciences","volume":" ","pages":""},"PeriodicalIF":1.9,"publicationDate":"2022-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42645976","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Deep Learning algorithms are used by many different disciplines for various purposes, thanks to their ever-developing data processing skills. Convolutional neural network (CNN) are generally developed and used for this integration purpose. On the other hand, the widespread usage of Unmanned Aerial Vehicles (UAV) enables the collection of aerial photographs for Photogrammetric studies. In this study, these two fields were brought together and it was aimed to find the equivalents of the objects detected from the UAV images using deep learning in the global coordinate system and to evaluate their accuracy over these values. For these reasons, v3 and v4 versions of the YOLO algorithm, which prioritizes detecting the midpoint of the detected object, were trained in Google Colab’s virtual machine environment using the prepared data set. The coordinate values read from the orthophoto and the coordinate values of the midpoints of the objects, which were derived according to the estimations made by the YOLO-v3 and YOLO-v4 models, were compared and their spatial accuracy was calculated. Accuracy of 16.8 cm was obtained with the YOLO-v3 and 15.5 cm with the YOLO-v4.
{"title":"Deep learning-based vehicle detection from orthophoto and spatial accuracy analysis","authors":"Muhammed Yahya Bıyık, M. E. Atik, Z. Duran","doi":"10.26833/ijeg.1080624","DOIUrl":"https://doi.org/10.26833/ijeg.1080624","url":null,"abstract":"Deep Learning algorithms are used by many different disciplines for various purposes, thanks to their ever-developing data processing skills. Convolutional neural network (CNN) are generally developed and used for this integration purpose. On the other hand, the widespread usage of Unmanned Aerial Vehicles (UAV) enables the collection of aerial photographs for Photogrammetric studies. In this study, these two fields were brought together and it was aimed to find the equivalents of the objects detected from the UAV images using deep learning in the global coordinate system and to evaluate their accuracy over these values. For these reasons, v3 and v4 versions of the YOLO algorithm, which prioritizes detecting the midpoint of the detected object, were trained in Google Colab’s virtual machine environment using the prepared data set. The coordinate values read from the orthophoto and the coordinate values of the midpoints of the objects, which were derived according to the estimations made by the YOLO-v3 and YOLO-v4 models, were compared and their spatial accuracy was calculated. Accuracy of 16.8 cm was obtained with the YOLO-v3 and 15.5 cm with the YOLO-v4.","PeriodicalId":42633,"journal":{"name":"International Journal of Engineering and Geosciences","volume":" ","pages":""},"PeriodicalIF":1.9,"publicationDate":"2022-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46901793","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Satellite images have been widely used in the production of geospatial information such as land use and land cover mapping, as well as the generation of several thematic layers via image processing. Images acquired by sensors onboard various satellite platforms are influenced by systematic sensor and platform-induced geometry errors. Thus, geometric correction of satellite images is an important step of image pre-processing to extract accurate and reliable locational information. Geometric correction of satellite images obtained from two different satellites, Pleiades 1A (PHR) and SPOT-6, was performed within the scope of this study using empirical models and a physical model. The 2D polynomial model, 3D rational function model with calculated RPCs from GCPs, 3D rational function model with RPCs from satellite, RPC refinement model using GCPs, and Toutin's physical model were used within this scope. Several experiments were carried out to investigate the effects of various parameters on the performance of the geometric correction procedure, such as GCP reference data source, GCP number and distribution, DEM source, spatial resolution, and model. Our results showed that lower RMSE values can be achieved with the model that uses RPC from data providers for PHR and SPOT that is followed by the RPC refinement method for PHR and Toutin method for SPOT. In general, GCPs from the HGM data source and ALOS DEM combination provided better results. Lastly, lower RMSE values, thus better locational accuracies are observed with PHR image except for single test.
{"title":"A comprehensive analysis of different geometric correction methods for Pleiades -1A and Spot-6 satellite images","authors":"Buğrahan Özci̇han, Levent Doğukan Özlü, Mümin İlker Karakap, Halime Sürmeli̇, U. Alganci, Elif Sertel","doi":"10.26833/ijeg.1086861","DOIUrl":"https://doi.org/10.26833/ijeg.1086861","url":null,"abstract":"Satellite images have been widely used in the production of geospatial information such as land use and land cover mapping, as well as the generation of several thematic layers via image processing. Images acquired by sensors onboard various satellite platforms are influenced by systematic sensor and platform-induced geometry errors. Thus, geometric correction of satellite images is an important step of image pre-processing to extract accurate and reliable locational information. Geometric correction of satellite images obtained from two different satellites, Pleiades 1A (PHR) and SPOT-6, was performed within the scope of this study using empirical models and a physical model. The 2D polynomial model, 3D rational function model with calculated RPCs from GCPs, 3D rational function model with RPCs from satellite, RPC refinement model using GCPs, and Toutin's physical model were used within this scope. Several experiments were carried out to investigate the effects of various parameters on the performance of the geometric correction procedure, such as GCP reference data source, GCP number and distribution, DEM source, spatial resolution, and model. Our results showed that lower RMSE values can be achieved with the model that uses RPC from data providers for PHR and SPOT that is followed by the RPC refinement method for PHR and Toutin method for SPOT. In general, GCPs from the HGM data source and ALOS DEM combination provided better results. Lastly, lower RMSE values, thus better locational accuracies are observed with PHR image except for single test.","PeriodicalId":42633,"journal":{"name":"International Journal of Engineering and Geosciences","volume":" ","pages":""},"PeriodicalIF":1.9,"publicationDate":"2022-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44578305","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Glaciers are retreating in the highest mountainous regions of the world as a result of climate change and global warming. This leads to the formation of different types of glacial lakes. These lakes are not only the source of fresh water but it also causes disaster in the form of Glacial Lake Outburst Flood (GLOF). Astore Drainage Basin is located in north eastern mountainous region of Himalayas. This area is prone to GLOFs because of the increasing number of glacial lakes and the growth of existing lakes as a result of global warming. To provide a detailed information about the spatial and temporal information of glacial lakes detailed inventories has been developed for the study area using Landsat images for the year 1989, 1999, 2009 and 2019. Glacial lakes were mapped and identified by using Normalized Different Water Index, Normalized Difference Snow Index and high resolution Google Earth images. It was found from the analysis that the number of the glacial lakes increased from 120 to 128 in a period of thirty years (i.e. from 1989 to 2019). During the study period two lakes disappeared whereas ten new lakes were formed. There were 21 lakes which show area expansion more than 100% representing high susceptibility for GLOF. The results also showed that smaller lakes expanded more rapidly in area than the larger lakes.
{"title":"Genesis and Spatio-Temporal Analysis of Glacial Lakes in the Peri-Glacial Environment of Western Himalayas","authors":"Fareeha Si̇ddi̇que, A. Rahman","doi":"10.26833/ijeg.1097912","DOIUrl":"https://doi.org/10.26833/ijeg.1097912","url":null,"abstract":"Glaciers are retreating in the highest mountainous regions of the world as a result of climate change and global warming. This leads to the formation of different types of glacial lakes. These lakes are not only the source of fresh water but it also causes disaster in the form of Glacial Lake Outburst Flood (GLOF). Astore Drainage Basin is located in north eastern mountainous region of Himalayas. This area is prone to GLOFs because of the increasing number of glacial lakes and the growth of existing lakes as a result of global warming. To provide a detailed information about the spatial and temporal information of glacial lakes detailed inventories has been developed for the study area using Landsat images for the year 1989, 1999, 2009 and 2019. Glacial lakes were mapped and identified by using Normalized Different Water Index, Normalized Difference Snow Index and high resolution Google Earth images. It was found from the analysis that the number of the glacial lakes increased from 120 to 128 in a period of thirty years (i.e. from 1989 to 2019). During the study period two lakes disappeared whereas ten new lakes were formed. There were 21 lakes which show area expansion more than 100% representing high susceptibility for GLOF. The results also showed that smaller lakes expanded more rapidly in area than the larger lakes.","PeriodicalId":42633,"journal":{"name":"International Journal of Engineering and Geosciences","volume":" ","pages":""},"PeriodicalIF":1.9,"publicationDate":"2022-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46129737","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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