Airplane detection from satellite imagery is a challenging task due to the complex backgrounds in the images and differences in data acquisition conditions caused by the sensor geometry and atmospheric effects. Deep learning methods provide reliable and accurate solutions for automatic detection of airplanes; however, huge amount of training data is required to obtain promising results. In this study, we create a novel airplane detection dataset called High Resolution Planes (HRPlanes) by using images from Google Earth (GE) and labeling the bounding box of each plane on the images. HRPlanes include GE images of several different airports across the world to represent a variety of landscape, seasonal and satellite geometry conditions obtained from different satellites. We evaluated our dataset with two widely used object detection methods namely YOLOv4 and Faster R-CNN. Our preliminary results show that the proposed dataset can be a valuable data source and benchmark data set for future applications. Moreover, proposed architectures and results of this study could be used for transfer learning of different datasets and models for airplane detection.
{"title":"A benchmark dataset for deep learning-based airplane detection: HRPlanes","authors":"Tolga BAKIRMAN, Elif SERTEL","doi":"10.26833/ijeg.1107890","DOIUrl":"https://doi.org/10.26833/ijeg.1107890","url":null,"abstract":"Airplane detection from satellite imagery is a challenging task due to the complex backgrounds in the images and differences in data acquisition conditions caused by the sensor geometry and atmospheric effects. Deep learning methods provide reliable and accurate solutions for automatic detection of airplanes; however, huge amount of training data is required to obtain promising results. In this study, we create a novel airplane detection dataset called High Resolution Planes (HRPlanes) by using images from Google Earth (GE) and labeling the bounding box of each plane on the images. HRPlanes include GE images of several different airports across the world to represent a variety of landscape, seasonal and satellite geometry conditions obtained from different satellites. We evaluated our dataset with two widely used object detection methods namely YOLOv4 and Faster R-CNN. Our preliminary results show that the proposed dataset can be a valuable data source and benchmark data set for future applications. Moreover, proposed architectures and results of this study could be used for transfer learning of different datasets and models for airplane detection.","PeriodicalId":42633,"journal":{"name":"International Journal of Engineering and Geosciences","volume":"56 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136183470","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}
Accurate and timely availability of LiDAR data is vital in some cases. To facilitate monitoring of any environmental changes, LiDAR systems can be designed, and carried by UAV platforms that can take off without major preparation. In this study, the methodology of the real-time LiDAR mapping system was developed in the laboratory. The designed system shortens the target-based flight planning and post-flight data processing. In this system, the data is taken instantly and thus the change in the mapping area can be detected quickly. The simulation system, produce 3D point cloud, and data was stored in a database for later analysis. The 3D visualization of the data obtained from our developed UAV-LiDAR system was carried out with a platform-independent interface designed as web-based. The X3D file format used in the study to produce 3D point data provide an infrastructure for AI and ML-based systems in identifying urban objects in systems containing big data such as LiDAR.
{"title":"Methodology of real-time 3D point cloud mapping with UAV lidar","authors":"Levent CANDAN, Elif KAÇAR","doi":"10.26833/ijeg.1178260","DOIUrl":"https://doi.org/10.26833/ijeg.1178260","url":null,"abstract":"Accurate and timely availability of LiDAR data is vital in some cases. To facilitate monitoring of any environmental changes, LiDAR systems can be designed, and carried by UAV platforms that can take off without major preparation. In this study, the methodology of the real-time LiDAR mapping system was developed in the laboratory. The designed system shortens the target-based flight planning and post-flight data processing. In this system, the data is taken instantly and thus the change in the mapping area can be detected quickly. The simulation system, produce 3D point cloud, and data was stored in a database for later analysis. The 3D visualization of the data obtained from our developed UAV-LiDAR system was carried out with a platform-independent interface designed as web-based. The X3D file format used in the study to produce 3D point data provide an infrastructure for AI and ML-based systems in identifying urban objects in systems containing big data such as LiDAR.","PeriodicalId":42633,"journal":{"name":"International Journal of Engineering and Geosciences","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136183695","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 observation of the juxtaposition of formal and informal urban settlements in the commonwealth and sub-Saharan developing countries is been trending as a common mundane phenomenon in emerging and fast-growing cities. In Dar es Salaam for example, one of the largest, high-density, and populous businesses cities in Tanzania; dichotomy of informal and formal land rights is ubiquitous in peri-urban areas and its urban vicinities where land evolves from village to urban. The dichotomy of urban settlements occurs when the public authorities do not satisfactorily provide public services which are customarily attributed to poor governance and policies formulated to guide market forces, urban management, and growth. Different strategies and approaches have been applied by the government for at least providing proper infrastructure; however, most of the approaches are not well successful and deliver the expected results following high cost of implementation. To understand the spatial dynamics of urban typology, population density and land cover maps of Dar es Salaam were used to comprehend the developmental characteristics of Dar es Salaam urban land transformation and change detection of built-up area. According to the analysis of the maps, rapid urbanization and dramatical growth in built-up area especially between 1990-2000 years were easily observed.
{"title":"Proliferation of Dichotomy Urban Settlements Plight: Land Governance And Sustainable Built Environment In Tanzania","authors":"Amanı Uisso, Sibel CANAZ SEVGEN, Prof.dr. Harun Tanrivermi̇ş","doi":"10.26833/ijeg.1149133","DOIUrl":"https://doi.org/10.26833/ijeg.1149133","url":null,"abstract":"The observation of the juxtaposition of formal and informal urban settlements in the commonwealth and sub-Saharan developing countries is been trending as a common mundane phenomenon in emerging and fast-growing cities. In Dar es Salaam for example, one of the largest, high-density, and populous businesses cities in Tanzania; dichotomy of informal and formal land rights is ubiquitous in peri-urban areas and its urban vicinities where land evolves from village to urban. The dichotomy of urban settlements occurs when the public authorities do not satisfactorily provide public services which are customarily attributed to poor governance and policies formulated to guide market forces, urban management, and growth. Different strategies and approaches have been applied by the government for at least providing proper infrastructure; however, most of the approaches are not well successful and deliver the expected results following high cost of implementation. To understand the spatial dynamics of urban typology, population density and land cover maps of Dar es Salaam were used to comprehend the developmental characteristics of Dar es Salaam urban land transformation and change detection of built-up area. According to the analysis of the maps, rapid urbanization and dramatical growth in built-up area especially between 1990-2000 years were easily observed.","PeriodicalId":42633,"journal":{"name":"International Journal of Engineering and Geosciences","volume":" ","pages":""},"PeriodicalIF":1.9,"publicationDate":"2023-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46653575","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}
Mostafa Mahdavi̇fard, Sara KAVİANİ AHANGAR, B. Feizizadeh, Khalil Valizadeh Kamran, S. Karimzadeh
Mangrove forests are considered one of the most complex and dynamic ecosystems facing various challenges due to anthropogenic disturbance and climate change. The excessive harvesting and land-use change in areas covered by mangrove ecosystems are critical threats for these forests. Therefore, the continuous and regular monitoring of these forests is essential. Fortunately, remote sensing data has made it possible to regularly and frequently monitor this type of forest. This study has two goals. Firstly, it combines optical data of Landsat- 8 and Sentinel-2 with Sentinel-1 radar data to improve land cover mapping accuracy. Secondly, it aims to evaluate the SVM machine learning algorithms and random forest to detection and differentiate forest cover from other land types in the Google Earth Engine system. The results show that the support vector machine (SVM) algorithm in the S2 + S1 dataset with a kappa coefficient of 0.94 performs significantly better than when used in the L8 + S1 combination dataset with a kappa coefficient of 0.88. On the other hand, the kappa coefficients of 0.89 and 0.85 were estimated for the random forest algorithm in S2 + S1 and L8 + S1 datasets. This again indicates the superiority of Sentinel-2 and Sentinel-1 datasets over Landsat- 8 and Sentinel-1 datasets. In general, the support vector machine (SVM) algorithm yielded better results than the RF random forest algorithm in optical and radar datasets. The results showed that the use of the Google Earth engine system and machine learning algorithms accelerates the process of mapping mangrove forests and even change detection.
{"title":"Spatio-Temporal monitoring of Qeshm mangrove forests through machine learning classification of SAR and Optical images on Google Earth Engine","authors":"Mostafa Mahdavi̇fard, Sara KAVİANİ AHANGAR, B. Feizizadeh, Khalil Valizadeh Kamran, S. Karimzadeh","doi":"10.26833/ijeg.1118542","DOIUrl":"https://doi.org/10.26833/ijeg.1118542","url":null,"abstract":"Mangrove forests are considered one of the most complex and dynamic ecosystems facing various challenges due to anthropogenic disturbance and climate change. The excessive harvesting and land-use change in areas covered by mangrove ecosystems are critical threats for these forests. Therefore, the continuous and regular monitoring of these forests is essential. Fortunately, remote sensing data has made it possible to regularly and frequently monitor this type of forest. This study has two goals. Firstly, it combines optical data of Landsat- 8 and Sentinel-2 with Sentinel-1 radar data to improve land cover mapping accuracy. Secondly, it aims to evaluate the SVM machine learning algorithms and random forest to detection and differentiate forest cover from other land types in the Google Earth Engine system. The results show that the support vector machine (SVM) algorithm in the S2 + S1 dataset with a kappa coefficient of 0.94 performs significantly better than when used in the L8 + S1 combination dataset with a kappa coefficient of 0.88. On the other hand, the kappa coefficients of 0.89 and 0.85 were estimated for the random forest algorithm in S2 + S1 and L8 + S1 datasets. This again indicates the superiority of Sentinel-2 and Sentinel-1 datasets over Landsat- 8 and Sentinel-1 datasets. In general, the support vector machine (SVM) algorithm yielded better results than the RF random forest algorithm in optical and radar datasets. The results showed that the use of the Google Earth engine system and machine learning algorithms accelerates the process of mapping mangrove forests and even change detection.","PeriodicalId":42633,"journal":{"name":"International Journal of Engineering and Geosciences","volume":" ","pages":""},"PeriodicalIF":1.9,"publicationDate":"2023-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44575572","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}
Communication between citizen and government has gained a new dimension with the developing information communication Technologies (ICT). The advantage of this technology in terms of both time and cost in service delivery has accelerated the use of these technologies in public administration. With this change, the concept of e-government took its place in the literature and started to be used in many countries. In particular, many public institutions both in the world and Turkey since the 2000s, began to provide many services in different areas such as health, safety, tax, education through e-government. Land management is one of these areas. The Land Registry and Cadastre Directorate, which operates under the Ministry of Environment and Urbanization, and many other public institutions have started to provide many services through e-government within the scope of land management. Thus, the foundations of the transition to e-land administration began to be laid. In this study, it is aimed to determine the necessary strategies for transition to e-land administration considering the role of e-government platform in the provision of public services relating to land management in Turkey. Therefore, the current e-service structure related to land management has been analyzed with the SWOT technique. Thus, the strengths and weaknesses of the existing structure were revealed, and the opportunities and threats faced by this structure were identified. As a result of the analysis, various suggestions were made for the institutional policies that should be implemented in transition to e-land administration.
{"title":"Transformation from e-Government to e-land administration in Turkey: A SWOT-based assessment analysis","authors":"Z. Polat","doi":"10.26833/ijeg.1152715","DOIUrl":"https://doi.org/10.26833/ijeg.1152715","url":null,"abstract":"Communication between citizen and government has gained a new dimension with the developing information communication Technologies (ICT). The advantage of this technology in terms of both time and cost in service delivery has accelerated the use of these technologies in public administration. With this change, the concept of e-government took its place in the literature and started to be used in many countries. In particular, many public institutions both in the world and Turkey since the 2000s, began to provide many services in different areas such as health, safety, tax, education through e-government. Land management is one of these areas. The Land Registry and Cadastre Directorate, which operates under the Ministry of Environment and Urbanization, and many other public institutions have started to provide many services through e-government within the scope of land management. Thus, the foundations of the transition to e-land administration began to be laid. \u0000In this study, it is aimed to determine the necessary strategies for transition to e-land administration considering the role of e-government platform in the provision of public services relating to land management in Turkey. Therefore, the current e-service structure related to land management has been analyzed with the SWOT technique. Thus, the strengths and weaknesses of the existing structure were revealed, and the opportunities and threats faced by this structure were identified. As a result of the analysis, various suggestions were made for the institutional policies that should be implemented in transition to e-land administration.","PeriodicalId":42633,"journal":{"name":"International Journal of Engineering and Geosciences","volume":" ","pages":""},"PeriodicalIF":1.9,"publicationDate":"2023-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47383917","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}
Karst Rocky Desertification (KRD) is the reduction of vegetative productivity of this land with the release of bedrock as a result of the full or partial transportation of the fertile soil through natural processes and human activities in karst landscapes. The purpose of this study is to reveal the effectiveness of Remote Sensing methods in monitoring, mapping, and evaluating KRD. Landsat 8 OLI images were used to carry out these procedures. In monitoring this process, Karst Bare Rock Index (KBRI), Normalized Difference Rock Index (NDRI), Carbonate Rock Index 2 (CRI2), Normalized Difference Build-Up Index (NDBI), Normalized Difference Vegetation Index (NDVI), Dimidiate Pixel Model (DPM), Multi Endmember Spectral Mixture Analysis (MESMA) and Support Vector Machine (SVM) were used from the spectral indices. In order to evaluate the results obtained, KRD was divided into 4 basic classes such as none, mild, moderate, and severe. According to these classification levels, it was determined that SVM method had the highest accuracy. For this reason, it was concluded that the SVM method can be used effectively in determining KRD. In the study, it was concluded that the KRD strengthens as one goes from south to north and from west to east in the research area. This study points out KRD is one of the effective land problems in the Mediterranean region, Turkey.
{"title":"Monitoring and Classification of Karst Rocky Desertification with Landsat 8 OLI Images Using Spectral Indices, Multi-Endmember Spectral Mixture Analysis and Support Vector Machine","authors":"Çağan Alevkayali, Onur Yayla, Yıldırım Atayeter","doi":"10.26833/ijeg.1149738","DOIUrl":"https://doi.org/10.26833/ijeg.1149738","url":null,"abstract":"Karst Rocky Desertification (KRD) is the reduction of vegetative productivity of this land with the release of bedrock as a result of the full or partial transportation of the fertile soil through natural processes and human activities in karst landscapes. The purpose of this study is to reveal the effectiveness of Remote Sensing methods in monitoring, mapping, and evaluating KRD. Landsat 8 OLI images were used to carry out these procedures. In monitoring this process, Karst Bare Rock Index (KBRI), Normalized Difference Rock Index (NDRI), Carbonate Rock Index 2 (CRI2), Normalized Difference Build-Up Index (NDBI), Normalized Difference Vegetation Index (NDVI), Dimidiate Pixel Model (DPM), Multi Endmember Spectral Mixture Analysis (MESMA) and Support Vector Machine (SVM) were used from the spectral indices. In order to evaluate the results obtained, KRD was divided into 4 basic classes such as none, mild, moderate, and severe. According to these classification levels, it was determined that SVM method had the highest accuracy. For this reason, it was concluded that the SVM method can be used effectively in determining KRD. In the study, it was concluded that the KRD strengthens as one goes from south to north and from west to east in the research area. This study points out KRD is one of the effective land problems in the Mediterranean region, Turkey.","PeriodicalId":42633,"journal":{"name":"International Journal of Engineering and Geosciences","volume":"1 1","pages":""},"PeriodicalIF":1.9,"publicationDate":"2023-03-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41691781","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}
Detailed knowledge regarding the availability of potential groundwater sources is a prerequisite for the sustainable development of cities and towns in a planned manner. The present study is carried out to identify the potential groundwater sources for the growth of towns and cities around Virudhunagar district, India by integrated geospatial techniques and analytical hierarchy method. The groundwater potential zones are divided into four groups: low, medium, high, and very high. It is observed that 18.41% and 34.1% fall under the low and medium zones, respectively. The high and very high groundwater potential zone encompasses an area of 11.95% and 35.23% of the total area respectively. Finally, the identified groundwater potential zones are validated with reported potential yield data of various wells which shows a good correlation. The findings of this study will assist urban planners and decision-makers in better planning and development of future cities and towns.
{"title":"Identification of Groundwater Potential for Urban Development Using Geospatial Techniques and Analytical Hierarchy Process","authors":"Rajaveni SUNDARA PANDİAN, S. U, P. K, L. R","doi":"10.26833/ijeg.1190998","DOIUrl":"https://doi.org/10.26833/ijeg.1190998","url":null,"abstract":"Detailed knowledge regarding the availability of potential groundwater sources is a prerequisite for the sustainable development of cities and towns in a planned manner. The present study is carried out to identify the potential groundwater sources for the growth of towns and cities around Virudhunagar district, India by integrated geospatial techniques and analytical hierarchy method. The groundwater potential zones are divided into four groups: low, medium, high, and very high. It is observed that 18.41% and 34.1% fall under the low and medium zones, respectively. The high and very high groundwater potential zone encompasses an area of 11.95% and 35.23% of the total area respectively. Finally, the identified groundwater potential zones are validated with reported potential yield data of various wells which shows a good correlation. The findings of this study will assist urban planners and decision-makers in better planning and development of future cities and towns.","PeriodicalId":42633,"journal":{"name":"International Journal of Engineering and Geosciences","volume":" ","pages":""},"PeriodicalIF":1.9,"publicationDate":"2023-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48897846","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 study aims to reveal suitable places where floating photovoltaic-solar power plants (FPV-SPPs) can be installed on the dam surface using the possibilities of remote sensing (RS) and geographical information science (GISc) technologies. Past satellite images from Landsat and Sentinel platforms allow researchers to analyse shoreline changes in the dam surface. Shoreline extraction is a crucial process for the FPV-SPP to stay afloat despite external constraints. In this study, changes in dam water levels were determined by classifying 20-year satellite images and analysing a 32-year global surface water dynamics dataset. The water surface area was calculated as 1,562.40 ha using the random forest (RF) algorithm and the normalized differences water index (NDWI) on Google Earth Engine (GEE) cloud platform. In addition, solar analysis was carried out with GISc using annual solar radiation maps shuttle radar topography mission (SRTM) data, which directly affects the energy production of FPV-SPPs. It has been calculated that the solar radiation on the water surface varies between 1,554 kWh/m2-year and 1,875 kWh/m2-year. These calculated values were divided into five different classes, and it was observed that 88.5% of the dam surface had a very high level of solar radiation compared to other areas. Higher efficiency will be obtained from the FPV-SPP to be installed in this region compared to the systems to be installed in other regions. It has been observed that the radiation values in other parts of the water surface are lower due to topographic shading. These analyses revealed energy zones with high production potential, thereby easing the decision-making process for investors planning to establish FPV-SPPs.
{"title":"Determination of the appropriate zone on dam surface for floating photovoltaic system installation using RS and GISc technologies","authors":"Osman Salih YILMAZ, Fatih GÜLGEN, Ali Murat ATEŞ","doi":"10.26833/ijeg.1052556","DOIUrl":"https://doi.org/10.26833/ijeg.1052556","url":null,"abstract":"This study aims to reveal suitable places where floating photovoltaic-solar power plants (FPV-SPPs) can be installed on the dam surface using the possibilities of remote sensing (RS) and geographical information science (GISc) technologies. Past satellite images from Landsat and Sentinel platforms allow researchers to analyse shoreline changes in the dam surface. Shoreline extraction is a crucial process for the FPV-SPP to stay afloat despite external constraints. In this study, changes in dam water levels were determined by classifying 20-year satellite images and analysing a 32-year global surface water dynamics dataset. The water surface area was calculated as 1,562.40 ha using the random forest (RF) algorithm and the normalized differences water index (NDWI) on Google Earth Engine (GEE) cloud platform. In addition, solar analysis was carried out with GISc using annual solar radiation maps shuttle radar topography mission (SRTM) data, which directly affects the energy production of FPV-SPPs. It has been calculated that the solar radiation on the water surface varies between 1,554 kWh/m2-year and 1,875 kWh/m2-year. These calculated values were divided into five different classes, and it was observed that 88.5% of the dam surface had a very high level of solar radiation compared to other areas. Higher efficiency will be obtained from the FPV-SPP to be installed in this region compared to the systems to be installed in other regions. It has been observed that the radiation values in other parts of the water surface are lower due to topographic shading. These analyses revealed energy zones with high production potential, thereby easing the decision-making process for investors planning to establish FPV-SPPs.","PeriodicalId":42633,"journal":{"name":"International Journal of Engineering and Geosciences","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135538197","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 study aimed to investigate the performance and sensitivity of 3D photogrammetric models generated without GCPs (ground control points). To determine whether the models with no GCPs retained accuracy in all terrain types as well as under varying climate or meteorological conditions, two separate studies were conducted in two areas with different characteristics (elevation, slope, topography, and meteorological differences). The study areas were initially modelled with GCPs and were later modelled without GCPs. Furthermore, some of the dimensions and areas within the modelled regions were measured using terrestrial techniques (with GPS/GNSS) for accuracy analyses. After regional modelling was conducted with and without GCPs, different territories with different slopes and geometric shapes were selected. Various length, area and volume measurements were carried out over the selected territories using both models (generated with and without GCPs). The datasets obtained from the measurement results were compared, and the measurements obtained using the models produced with GCPs were accepted as the true values. The length measurement results provided various levels of success. The first study area exhibited very promising length measurement results, with a relative error less than 1% and an RMSE (root mean square error) of 0.139 m. In the case of the area measurements, in the first study area (Sivas), a minimum relative error of 0.04% and a maximum relative error of 1.05% with an RMSE of 1.264 m² were obtained. In the second study areas (Artvin), a minimum relative error of 0.56% and a maximum relative error of 5.27% with an RMSE of 1.76 m² were achieved. Finally, in the case of the volume measurements, for the first study area (Sivas), a minimum relative error of 0.8% and a maximum relative error of 6.8% as well as an RMSE of 2.301 m³ were calculated. For the second study area (Artvin), the minimum relative error of the volume measurements was 0.502%, and the maximum relative error was 2.01%, with an RMSE of 7.061 m³.
{"title":"Investigating the length, area and volume measurement accuracy of UAV-Based oblique photogrammetry models produced with and without ground control points","authors":"Erdem Emin MARAŞ, Noman NASERY","doi":"10.26833/ijeg.1017176","DOIUrl":"https://doi.org/10.26833/ijeg.1017176","url":null,"abstract":"This study aimed to investigate the performance and sensitivity of 3D photogrammetric models generated without GCPs (ground control points). To determine whether the models with no GCPs retained accuracy in all terrain types as well as under varying climate or meteorological conditions, two separate studies were conducted in two areas with different characteristics (elevation, slope, topography, and meteorological differences). The study areas were initially modelled with GCPs and were later modelled without GCPs. Furthermore, some of the dimensions and areas within the modelled regions were measured using terrestrial techniques (with GPS/GNSS) for accuracy analyses. After regional modelling was conducted with and without GCPs, different territories with different slopes and geometric shapes were selected. Various length, area and volume measurements were carried out over the selected territories using both models (generated with and without GCPs). The datasets obtained from the measurement results were compared, and the measurements obtained using the models produced with GCPs were accepted as the true values. The length measurement results provided various levels of success. The first study area exhibited very promising length measurement results, with a relative error less than 1% and an RMSE (root mean square error) of 0.139 m. In the case of the area measurements, in the first study area (Sivas), a minimum relative error of 0.04% and a maximum relative error of 1.05% with an RMSE of 1.264 m² were obtained. In the second study areas (Artvin), a minimum relative error of 0.56% and a maximum relative error of 5.27% with an RMSE of 1.76 m² were achieved. Finally, in the case of the volume measurements, for the first study area (Sivas), a minimum relative error of 0.8% and a maximum relative error of 6.8% as well as an RMSE of 2.301 m³ were calculated. For the second study area (Artvin), the minimum relative error of the volume measurements was 0.502%, and the maximum relative error was 2.01%, with an RMSE of 7.061 m³.","PeriodicalId":42633,"journal":{"name":"International Journal of Engineering and Geosciences","volume":"66 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135582544","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}
Manti Patil, A. Saha, S. Pingale, D. Rathore, V. Goyal
An investigation of soil and water resources is essential to determine the future scenario of water management and water resources to attain food and water security. The improper management of watersheds results in a huge amount of sediment loss and surface runoff. Therefore, the present study was carried out to estimate the surface runoff and soil erosion using the Soil Conservation Service Curve Number (SCS-CN) method and Revised Universal Soil Loss Equation (RUSLE), respectively. These have been estimated using geospatial technologies for the ungauged Mandri river watershed from the Kanker district of Chhattisgarh State in India. The runoff potential zones, which are defined by the area's impermeable surfaces for a given quantity of precipitation were identified based on curve numbers at the sub-watershed levels. The results showed that the average volume of runoff generated throughout the 16-years period was 14.37 million cubic meters (mM3). While average annual soil loss was found to be 17.23 tons/ha/year. Most of the eroded area was found to be around the major stream in a drainage system of Mandri River and on higher slopes of the terrain in the watershed. This study revealed that surface runoff and soil erosion are primary issues, which adversely affected the soil and water resources in this watershed. Therefore, suitable water harvesting sites and structures can be constructed based on the potential runoff zone and severity of soil erosion to conserve the soil and water in the watershed.
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