ISPRS Annals of the Photogrammetry Remote Sensing and Spatial Information Sciences最新文献_第6页

FACULTY OF GEOMATICS AS THE FOCAL POINT IN GEOMATICS EDUCATION IN SRI LANKA 测绘学院是斯里兰卡测绘教育的重点

ISPRS Annals of the Photogrammetry Remote Sensing and Spatial Information Sciences Pub Date : 2023-05-23 DOI: 10.5194/isprs-annals-x-5-w1-2023-51-2023

H. Prasanna

{"title":"FACULTY OF GEOMATICS AS THE FOCAL POINT IN GEOMATICS EDUCATION IN SRI LANKA","authors":"H. Prasanna","doi":"10.5194/isprs-annals-x-5-w1-2023-51-2023","DOIUrl":"https://doi.org/10.5194/isprs-annals-x-5-w1-2023-51-2023","url":null,"abstract":"Abstract. The Faculty of Geomatics is one of the nine faculties of the Sabaragamuwa University of Sri Lanka. It offers high quality geomatics related degree programmes, and consists of a well-qualified staff and latest technological resources. It delivers highly job oriented programmes and the curriculums of these programmes have been developed to meet various national and international standards and the requirements of professional bodies. As the first time in the history of the Sri Lankan university system, the BSc in Surveying Sciences Degree Programme was introduced by the Faculty of Geomatics established in 2004. This was an essential requirement for the country due to the high demand of surveying industry. Throughout the past decades it is being greatly developed both in infrastructure and human resources. Presently, the faculty has a student population of about 500 with the aim of producing about 150 graduates annually. Geomatics graduates have a variety of rewarding local and global employment prospects as land surveyors, land administrators, site managers, or site engineers in both the public and private sectors, including cadastral, mining, hydrographic surveying, and any other remote sensing and GIS-related disciplines. Graduates with knowledge in the geospatial stream could work as GIS analysts, image analysts, image interpreters, and in other positions in the business. Graduates of the program can take on useful tasks in their field with ease.\u0000","PeriodicalId":30633,"journal":{"name":"ISPRS Annals of the Photogrammetry Remote Sensing and Spatial Information Sciences","volume":"26 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89889315","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}

引用次数: 0

EXPLORATION AND APPLICATION OF "1+2+3" TEACHING MODE USING VIRTUAL REALITY ENVIRONMENTS FOR GEOMATICS UNDERGRADUATE STUDENTS 基于虚拟现实环境的测绘学本科“1+2+3”教学模式探索与应用

ISPRS Annals of the Photogrammetry Remote Sensing and Spatial Information Sciences Pub Date : 2023-05-23 DOI: 10.5194/isprs-annals-x-5-w1-2023-27-2023

E. Gao, Haibo Wang, J. Li, G. Zhou

{"title":"EXPLORATION AND APPLICATION OF \"1+2+3\" TEACHING MODE USING VIRTUAL REALITY ENVIRONMENTS FOR GEOMATICS UNDERGRADUATE STUDENTS","authors":"E. Gao, Haibo Wang, J. Li, G. Zhou","doi":"10.5194/isprs-annals-x-5-w1-2023-27-2023","DOIUrl":"https://doi.org/10.5194/isprs-annals-x-5-w1-2023-27-2023","url":null,"abstract":"Abstract. Since the beginning of this century, mapping and geographic information technology has been deeply integrated with virtual reality, artificial intelligence and other technologies, the mapping industry has entered the \"intelligent mapping era\", how to train innovative and practical mapping talents is urgent. The teaching team takes the cultivation of innovative and practical talents in surveying and mapping as the core goal, and focuses closely on the cultivation of students' innovation ability and large (complex) project implementation ability. Relying on Guangxi Spatial Information and Mapping Virtual Simulation Experimental Teaching Center and Southern Mapping Virtual Simulation Teaching Platform, the team carries out teaching reform and practice. The team proposes the training mode of \"one goal, two synergy, three integration\", which will use VR/AR, GIS and other technologies to teach major projects and scientific research achievements through virtual simulation, and adopt online + offline methods to enhance students' innovative practical ability. Through scientific assessment and incentive mechanism, students' initiative and enthusiasm will be improved, thus solving the problem of difficulty in cultivating students' innovation and practice ability. After years of reform and practice, students' innovation and practical ability have been improved significantly, and they have won dozens of awards such as the silver prize of \"Internet+\" national competition, the second prize of \"Challenge Cup\" national competition, and the national mapping skills competition. The teaching reform methods have been well promoted.\u0000","PeriodicalId":30633,"journal":{"name":"ISPRS Annals of the Photogrammetry Remote Sensing and Spatial Information Sciences","volume":"25 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81848339","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}

引用次数: 0

INTERDISCIPLINARY APPROACH TO CURRICULA DEVELOPMENT IN GEOMATICS EDUCATION: ERASMUS+ LBS2ITS PROJECT 地理信息学教育课程发展的跨学科方法:erasmus + lbs2its项目

ISPRS Annals of the Photogrammetry Remote Sensing and Spatial Information Sciences Pub Date : 2023-05-23 DOI: 10.5194/isprs-annals-x-5-w1-2023-19-2023

J. Gabela, G. Retscher

引用次数: 0

IDENTIFYING SUITABLE LOCATIONS FOR MANGROVE PLANTATION USING GEOSPATIAL INFORMATION SYSTEM AND REMOTE SENSING 利用地理空间信息系统和遥感技术确定适宜的红树林种植地点

ISPRS Annals of the Photogrammetry Remote Sensing and Spatial Information Sciences Pub Date : 2023-01-14 DOI: 10.5194/isprs-annals-x-4-w1-2022-669-2023

R. Sahraei, A. Ghorbanian, Y. Kanani-Sadat, S. Jamali, Saeid Homayouni

{"title":"IDENTIFYING SUITABLE LOCATIONS FOR MANGROVE PLANTATION USING GEOSPATIAL INFORMATION SYSTEM AND REMOTE SENSING","authors":"R. Sahraei, A. Ghorbanian, Y. Kanani-Sadat, S. Jamali, Saeid Homayouni","doi":"10.5194/isprs-annals-x-4-w1-2022-669-2023","DOIUrl":"https://doi.org/10.5194/isprs-annals-x-4-w1-2022-669-2023","url":null,"abstract":"Abstract. Mangroves provide numerous environmental benefits, such as carbon sequestration, water purification, climate change mitigation, and flood and Tsunami impact reduction. Despite these unique advantages, mangroves are threatened by the combined adverse impacts of human activities and climate change. Therefore, it is essential to implement reasonable practices to avoid further degradation of mangroves and provide efficient workflows to increase their extent. Accordingly, better plantation policies are principally required for their conservation and rehabilitation. In this study, we desired to detect suitable locations for mangrove plantation in coastal areas of Hormozgan Province, Iran. We considered a relatively new Multi Criteria Decision Making (MCDM) technique to combine ten criteria derived from remote sensing in a GIS environment. The Best Worst Method (BWM), as an MDCM technique, was implemented to determine the relative importance of each criterion. Afterward, all criteria were aggregated using the Weighted Linear Combination (WLC) method to produce a mangrove plantation suitability map. Statistical measures, including Overall Accuracy (OA = 95%), Kappa Coefficient (KC = 87.9%), and Area Under Curve (AUC = 98.79%), indicated the high applicability of the implemented method for mangrove plantation site allocation. The produced map could give managers a profound insight into finding optimal spots to plant mangroves.\u0000","PeriodicalId":30633,"journal":{"name":"ISPRS Annals of the Photogrammetry Remote Sensing and Spatial Information Sciences","volume":"26 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-01-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74464215","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}

引用次数: 0

GIS-BASED GROUNDWATER POTENTIAL MAPPING USING MACHINE LEARNING MODELS, A CASE STUDY: QOM PROVINCE, IRAN 利用机器学习模型绘制基于gis的地下水潜力图，案例研究:伊朗库姆省

ISPRS Annals of the Photogrammetry Remote Sensing and Spatial Information Sciences Pub Date : 2023-01-14 DOI: 10.5194/isprs-annals-x-4-w1-2022-489-2023

E. Masoudian, P. Pahlavani

{"title":"GIS-BASED GROUNDWATER POTENTIAL MAPPING USING MACHINE LEARNING MODELS, A CASE STUDY: QOM PROVINCE, IRAN","authors":"E. Masoudian, P. Pahlavani","doi":"10.5194/isprs-annals-x-4-w1-2022-489-2023","DOIUrl":"https://doi.org/10.5194/isprs-annals-x-4-w1-2022-489-2023","url":null,"abstract":"Abstract. By considering the increasing trend in water consumption and significant reduction of water resources in most countries of the world, groundwater resources have become very important. The Target of this study is to implement machine learning models to produce a groundwater potential map (GWPM), identify areas with higher water potential, and also identify influencing factors. Therefore, two algorithms including the random forest (RF) and support vector regression (SVR), were performed that according to the literature have a good compatibility with this type of problems, compared to the other models. Of the 351 well points available throughout the study area, 70% (245 well points) were selected as the target for training the models and the rest 30% (106 well points) were used for evaluating the models. In addition, 20 effective information layers were used for modeling. In this study, an effort was made to focus more on data preparation that is one of the most important parts of model development. The variance inflation factor (VIF) and correlation coefficient were applied to identify the dependent variables. Also, feature selection was done to identify the most influential factors. Finally, two groundwater potential map(GWPM)s were created based on these two models. By calculating the area under the curve (AUC) from the receiver operating characteristic (ROC), the prediction accuracy of the two models was calculated. The values for AUC of the two maps produced by the RF and SVR algorithms were 93.4% and 89.7%, respectively. This study improves the knowledge of groundwater potential in the study area which is one of the cities with water scarcity in the country.\u0000","PeriodicalId":30633,"journal":{"name":"ISPRS Annals of the Photogrammetry Remote Sensing and Spatial Information Sciences","volume":"26 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-01-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81531280","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}

引用次数: 1

SEMANTIC SEGMENTATION OF UAV IMAGES BASED ON U-NET IN URBAN AREA 基于u-net的城市无人机图像语义分割

ISPRS Annals of the Photogrammetry Remote Sensing and Spatial Information Sciences Pub Date : 2023-01-14 DOI: 10.5194/isprs-annals-x-4-w1-2022-451-2023

A. Majidizadeh, H. Hasani, M. Jafari

{"title":"SEMANTIC SEGMENTATION OF UAV IMAGES BASED ON U-NET IN URBAN AREA","authors":"A. Majidizadeh, H. Hasani, M. Jafari","doi":"10.5194/isprs-annals-x-4-w1-2022-451-2023","DOIUrl":"https://doi.org/10.5194/isprs-annals-x-4-w1-2022-451-2023","url":null,"abstract":"Abstract. Semantic segmentation of aerial data has been one of the leading researches in the field of photogrammetry, remote sensing, and computer vision in recent years. Many applications, including airborne mapping of urban scenes, object positioning in aerial images, automatic extraction of buildings from remote sensing or high-resolution aerial images, etc., require accurate and efficient segmentation algorithms. According to the high potential of deep learning algorithms in the classification of complex scenes, this paper aims to train a deep learning model to evaluate the semantic segmentation accuracy of UAV-based images in urban areas. The proposed method implements a deep learning framework based on the U-Net encoder-decoder architecture, which extracts and classifies features through layers of convolution, max pooling, activation, and concatenation in an end-to-end process. The obtained results compare with two traditional machine learning models, Random Forest (RF) and Multi-Layer Perceptron (MLP). They rely on two steps that involve extracting features and classifying images. In this study, the experiments are performed on the UAVid2020 semantic segmentation dataset from the ISPRS database. Results show the effectiveness of the proposed deep learning framework, so that the U-Net architecture achieved the best results with 75.15% overall accuracy, compared to RF and MLP algorithms with 52.51% and 54.65% overall accuracy, respectively.\u0000","PeriodicalId":30633,"journal":{"name":"ISPRS Annals of the Photogrammetry Remote Sensing and Spatial Information Sciences","volume":"349 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-01-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79737070","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}

引用次数: 0

POINTNET++ TRANSFER LEARNING FOR TREE EXTRACTION FROM MOBILE LIDAR POINT CLOUDS 从移动激光雷达点云中提取树的pointnet++迁移学习

ISPRS Annals of the Photogrammetry Remote Sensing and Spatial Information Sciences Pub Date : 2023-01-14 DOI: 10.5194/isprs-annals-x-4-w1-2022-721-2023

D. Shokri, M. Zaboli, F. Dolati, S. Homayouni

{"title":"POINTNET++ TRANSFER LEARNING FOR TREE EXTRACTION FROM MOBILE LIDAR POINT CLOUDS","authors":"D. Shokri, M. Zaboli, F. Dolati, S. Homayouni","doi":"10.5194/isprs-annals-x-4-w1-2022-721-2023","DOIUrl":"https://doi.org/10.5194/isprs-annals-x-4-w1-2022-721-2023","url":null,"abstract":"Abstract. Trees are an essential part of the natural and urban environment due to providing crucial benefits such as increasing air quality and wildlife habitats. Therefore, various remote sensing and photogrammetry technologies, including Mobile Laser Scanner (MLS), have been recently introduced for precise 3D tree mapping and modeling. The MLS provides densely 3D LiDAR point clouds from the surrounding, which results in measuring applicable information of trees like stem diameter or elevation. In this paper, a transfer learning procedure on the PointNet++ has been proposed for tree extraction. Initially, two steps of converting the MLS point clouds into same-length smaller sections and eliminating ground points have been conducted to overcome the massive volume of MLS data. The algorithm was tested on four LiDAR datasets ranging from challengeable urban environments containing multiple objects like tall buildings to railway surroundings. F1-Score accuracy was gained at around 93% and 98%, which showed the feasibility and efficiency of the proposed algorithm. Noticeably, the algorithms also measured geometrical information of extracted trees such as 2D coordinate space, height, stem diameter, and 3D boundary tree locations.\u0000","PeriodicalId":30633,"journal":{"name":"ISPRS Annals of the Photogrammetry Remote Sensing and Spatial Information Sciences","volume":"92 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-01-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83355821","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}

引用次数: 0

EXTRINSIC CALIBRATION OF ROTATING 2D LASER RANGE FINDER AND CAMERA USING PHOTOGRAMMETRIC TEST FIELD AND PING PONG BALLS 利用摄影测量试验场和乒乓球对旋转二维激光测距仪和相机进行外部标定

ISPRS Annals of the Photogrammetry Remote Sensing and Spatial Information Sciences Pub Date : 2023-01-14 DOI: 10.5194/isprs-annals-x-4-w1-2022-475-2023

M. Manouchehri, A. Hosseininaveh Ahmadabadian

{"title":"EXTRINSIC CALIBRATION OF ROTATING 2D LASER RANGE FINDER AND CAMERA USING PHOTOGRAMMETRIC TEST FIELD AND PING PONG BALLS","authors":"M. Manouchehri, A. Hosseininaveh Ahmadabadian","doi":"10.5194/isprs-annals-x-4-w1-2022-475-2023","DOIUrl":"https://doi.org/10.5194/isprs-annals-x-4-w1-2022-475-2023","url":null,"abstract":"Abstract. In this article, a method for the extrinsic calibration of a 2D laser range finder and a camera is presented. This technique produces a 3D point cloud from the test field by connecting a laser range finder to a servomotor. In this study, ping balls and standard photogrammetric targets were employed as a test field. Ping pong balls are used because they can be easily recognized in data from laser range finder and camera. To calculate extrinsic calibration parameters between a camera and laser range finder, these balls are employed as control points in the data. The extrinsic calibration of the Laser range finder and camera is carried out using the point cloud created from the test field and the photos captured from the test field. In this method, a sphere is fitted to each ping pong ball's points in the 3D point cloud, and the coordinates of that sphere's center are taken to be the coordinates of that ball. By measuring the distances between various targets in the test field, the scale can be resolved. This approach was compared with another state-of-the-art method. The proposed method is more accurate and stable than the alternative way, taking into account the average inaccuracy of check points.\u0000","PeriodicalId":30633,"journal":{"name":"ISPRS Annals of the Photogrammetry Remote Sensing and Spatial Information Sciences","volume":"4 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-01-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73394408","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}

引用次数: 0

MONOCULAR DEPTH ESTIMATION OF GOOGLE EARTH IMAGES USING CONVOLUTIONAL NEURAL NETWORKS 基于卷积神经网络的谷歌地球图像单目深度估计

ISPRS Annals of the Photogrammetry Remote Sensing and Spatial Information Sciences Pub Date : 2023-01-14 DOI: 10.5194/isprs-annals-x-4-w1-2022-589-2023

M. Najaf, H. Arefi, H. Amini Amirkolaee, B. Farajelahi

{"title":"MONOCULAR DEPTH ESTIMATION OF GOOGLE EARTH IMAGES USING CONVOLUTIONAL NEURAL NETWORKS","authors":"M. Najaf, H. Arefi, H. Amini Amirkolaee, B. Farajelahi","doi":"10.5194/isprs-annals-x-4-w1-2022-589-2023","DOIUrl":"https://doi.org/10.5194/isprs-annals-x-4-w1-2022-589-2023","url":null,"abstract":"Abstract. Depth estimation from images is an important task using scene understanding and reconstruction. Recently, encoder-decoder type fully convolutional architectures have gained great success in the area of depth estimation. Depth extraction from aerial and satellite images is one of the important topics in photogrammetry and remote sensing. This is usually done using image pairs, or more than two images. Solving this problem using a single image is still a challenging problem and has not been completely solved. Several convolutional neural networks have been proposed to extract depth from a single image, which act as encoders and decoders. In this article, we use one of these networks, which has performed well for depth estimation, in order to extract height from aerial and satellite images. Our main goal is to investigate the performance of Google Earth satellite data in order to produce a digital elevation model. At first, we extracted the digital model of the target area using ISPRS benchmark data, then we did the same thing using Google Earth satellite images. The paper presents a convolutional neural network for computing a high-resolution depth map given a single RGB Google Earth image. The results show the proper performance of Google Earth satellite images for height extraction. We achieved values of 2.07 m and 0.36 m for the RMS and REL metrics, respectively, which are very comparable and acceptable to the values of 2.04 m and 0.39 m obtained from the ISPRS benchmark images.\u0000","PeriodicalId":30633,"journal":{"name":"ISPRS Annals of the Photogrammetry Remote Sensing and Spatial Information Sciences","volume":"11 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-01-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84226994","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}

引用次数: 0

PERFORMANCE ANALYSIS OF GEOPOTENTIAL MODELS IN ESTIMATING THE GEOIDAL HEIGHT OVER IRAN 地球势模型在估计伊朗大地水准面高度中的性能分析

ISPRS Annals of the Photogrammetry Remote Sensing and Spatial Information Sciences Pub Date : 2023-01-14 DOI: 10.5194/isprs-annals-x-4-w1-2022-545-2023

M. Mosayebzadeh, R. Karimi, A. Ardalan

引用次数: 0