Pub Date : 2024-06-11DOI: 10.5194/isprs-archives-xlviii-2-2024-443-2024
Kaizhi Yang, Alper Yilmaz
Abstract. The prevalence of surveillance cameras in public places has led to an extremely pressing need for effective position and crowd monitoring, as well as anomaly detection. This paper tends to exhibit an incorporated approach that combines state-of-the-art computer vision techniques for comprehensive crowd surveillance. The main features of our approach are summarized into four steps: (a) Object detection and tracking; (b) Geometric rectification for positioning; (c) Motion extraction; and (d) Anomaly detection. First, this uses YOLOv5's Convolutional Neural Network (CNN) model in making efficient detection of objects, focusing on spotting individuals within crowded scenes. After detection, a strong mechanism for tracking is established with the help of the DeepSORT algorithm, which can track the person across frames. It must gain the people's position in the video frame and analyze motion data with the guarantee of capture of camera-scene geometry. Each frame thus gets converted from the 3D perspective to a 2D bird's eye view within the surveillance video, giving a guarantee of capture of the geometry of a camera scene. Motion anomaly detection is addressed through statistical methods, with Kernel Density Estimation (KDE) being employed to identify deviations from normal motion patterns. Extensive experiments conducted on different online crowd scene video datasets validate the effectiveness of the proposed anomaly detection mechanism. Overall, this integrated approach proposes a promising solution to crowd surveillance, further development of object detection, tracking, and anomaly analysis for monitoring public spaces.
{"title":"Crowd Scene Anomaly Detection in Online Videos","authors":"Kaizhi Yang, Alper Yilmaz","doi":"10.5194/isprs-archives-xlviii-2-2024-443-2024","DOIUrl":"https://doi.org/10.5194/isprs-archives-xlviii-2-2024-443-2024","url":null,"abstract":"Abstract. The prevalence of surveillance cameras in public places has led to an extremely pressing need for effective position and crowd monitoring, as well as anomaly detection. This paper tends to exhibit an incorporated approach that combines state-of-the-art computer vision techniques for comprehensive crowd surveillance. The main features of our approach are summarized into four steps: (a) Object detection and tracking; (b) Geometric rectification for positioning; (c) Motion extraction; and (d) Anomaly detection. First, this uses YOLOv5's Convolutional Neural Network (CNN) model in making efficient detection of objects, focusing on spotting individuals within crowded scenes. After detection, a strong mechanism for tracking is established with the help of the DeepSORT algorithm, which can track the person across frames. It must gain the people's position in the video frame and analyze motion data with the guarantee of capture of camera-scene geometry. Each frame thus gets converted from the 3D perspective to a 2D bird's eye view within the surveillance video, giving a guarantee of capture of the geometry of a camera scene. Motion anomaly detection is addressed through statistical methods, with Kernel Density Estimation (KDE) being employed to identify deviations from normal motion patterns. Extensive experiments conducted on different online crowd scene video datasets validate the effectiveness of the proposed anomaly detection mechanism. Overall, this integrated approach proposes a promising solution to crowd surveillance, further development of object detection, tracking, and anomaly analysis for monitoring public spaces.\u0000","PeriodicalId":505918,"journal":{"name":"The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences","volume":"4 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141360793","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}
Pub Date : 2024-06-11DOI: 10.5194/isprs-archives-xlviii-2-2024-25-2024
O. Bayrak, Zhenyu Ma, E. M. Farella, F. Remondino, M. Uzar
Abstract. Cityscapes contain a variety of objects, each with a particular role in urban administration and development. With the rapid growth and implementation of 3D imaging technology, urban areas are increasingly surveyed with high-resolution point clouds. This technical advancement extensively improves our ability to capture and analyse urban environments and their small objects. Deep learning algorithms for point cloud data have shown considerable capacity in 3D object classification but still face problems with generally under-represented objects (such as light poles or chimneys). This paper introduces the ESTATE dataset (https://github.com/3DOM-FBK/ESTATE), which combines available datasets of various sensors, densities, regions, and object types. It includes 13 classes featuring intensity and/or colour attributes. Tests using ESTATE demonstrate that the dataset improves the classification performance of deep learning techniques and could be a game-changer to advance in the 3D classification of urban objects.
{"title":"ESTATE: A Large Dataset of Under-Represented Urban Objects for 3D Point Cloud Classification","authors":"O. Bayrak, Zhenyu Ma, E. M. Farella, F. Remondino, M. Uzar","doi":"10.5194/isprs-archives-xlviii-2-2024-25-2024","DOIUrl":"https://doi.org/10.5194/isprs-archives-xlviii-2-2024-25-2024","url":null,"abstract":"Abstract. Cityscapes contain a variety of objects, each with a particular role in urban administration and development. With the rapid growth and implementation of 3D imaging technology, urban areas are increasingly surveyed with high-resolution point clouds. This technical advancement extensively improves our ability to capture and analyse urban environments and their small objects. Deep learning algorithms for point cloud data have shown considerable capacity in 3D object classification but still face problems with generally under-represented objects (such as light poles or chimneys). This paper introduces the ESTATE dataset (https://github.com/3DOM-FBK/ESTATE), which combines available datasets of various sensors, densities, regions, and object types. It includes 13 classes featuring intensity and/or colour attributes. Tests using ESTATE demonstrate that the dataset improves the classification performance of deep learning techniques and could be a game-changer to advance in the 3D classification of urban objects.\u0000","PeriodicalId":505918,"journal":{"name":"The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences","volume":"14 12","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141356381","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}
Pub Date : 2024-06-11DOI: 10.5194/isprs-archives-xlviii-2-2024-187-2024
Jiyong Kwag, C. Toth
Abstract. Autonomous driving offers benefits such as congestion mitigation, increased productivity through the reallocation of driving time, and decreased energy waste. However, achieving Level 4 and 5 autonomous driving remains a significant challenge for both academia and industry. Among the various modules of autonomous driving, High-Definition (HD) maps have become a crucial component due to their high precision in map elements, enabling accurate localization, scene interpretation, navigation, vehicle control and motion forecasting of trajectory of surrounding objects. Several map providers, including TomTom, HERE, Waymo, and NVIDIA, create HD maps for their specific purposes. However, most HD map datasets are not publicly available for individual researchers and companies to investigate the current trends in HD map generation. Furthermore, recent survey papers on HD map generation have tended to focus only on specific aspects, such as road topology or boundary extraction, rather than considering the overall end-to-end HD map generation process. Therefore, we begin with a brief definition, standards, and functionality of HD maps, followed by an exploration of different types of HD maps, including offline and online variants, highlighting their respective advantages and disadvantages. Finally, we will discuss the most recent end-to-end HD map generation architectures, along with various types of open-source HD map datasets and compare their performances.
{"title":"A Review on End-to-End High-Definition Map Generation","authors":"Jiyong Kwag, C. Toth","doi":"10.5194/isprs-archives-xlviii-2-2024-187-2024","DOIUrl":"https://doi.org/10.5194/isprs-archives-xlviii-2-2024-187-2024","url":null,"abstract":"Abstract. Autonomous driving offers benefits such as congestion mitigation, increased productivity through the reallocation of driving time, and decreased energy waste. However, achieving Level 4 and 5 autonomous driving remains a significant challenge for both academia and industry. Among the various modules of autonomous driving, High-Definition (HD) maps have become a crucial component due to their high precision in map elements, enabling accurate localization, scene interpretation, navigation, vehicle control and motion forecasting of trajectory of surrounding objects. Several map providers, including TomTom, HERE, Waymo, and NVIDIA, create HD maps for their specific purposes. However, most HD map datasets are not publicly available for individual researchers and companies to investigate the current trends in HD map generation. Furthermore, recent survey papers on HD map generation have tended to focus only on specific aspects, such as road topology or boundary extraction, rather than considering the overall end-to-end HD map generation process. Therefore, we begin with a brief definition, standards, and functionality of HD maps, followed by an exploration of different types of HD maps, including offline and online variants, highlighting their respective advantages and disadvantages. Finally, we will discuss the most recent end-to-end HD map generation architectures, along with various types of open-source HD map datasets and compare their performances.\u0000","PeriodicalId":505918,"journal":{"name":"The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences","volume":"95 22","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141359279","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}
Pub Date : 2024-06-11DOI: 10.5194/isprs-archives-xlviii-2-2024-301-2024
Abdul Nurunnabi, Felicia Teferle, D. Laefer, Meida Chen, Mir Masoom Ali
Abstract. A precise tree structure that represents the distribution of tree stem, branches, and leaves is crucial for accurately capturing the full representation of a tree. Light Detection and Ranging (LiDAR)-based three-dimensional (3D) point clouds (PCs) capture the geometry of scanned objects including forests stands and individual trees. PCs are irregular, unstructured, often noisy, and contaminated by outliers. Researchers have struggled to develop methods to separate leaves and wood without losing the tree geometry. This paper proposes a solution that employs only the spatial coordinates (x, y, z) of the PC. The new algorithm works as a filtering approach, utilizing multi-scale neighborhood-based geometric features (GFs) e.g., linearity, planarity, and verticality to classify linear (wood) and non-linear (leaf) points. This involves finding potential wood points and coupling them with an octree-based segmentation to develop a tree architecture. The main contributions of this paper are (i) investigating the potential of different GFs to split linear and non-linear points, (ii) introducing a novel method that pointwise classifies leaf and wood points, and (iii) developing a precise 3D tree structure. The performance of the new algorithm has been demonstrated through terrestrial laser scanning PCs. For a Scots pine tree, the new method classifies leaf and wood points with an overall accuracy of 97.9%.
摘要要准确捕捉树木的全貌，就必须有一个精确的树形结构来表示树干、树枝和树叶的分布。基于光探测和测距（LiDAR）的三维（3D）点云（PCs）可以捕捉扫描对象的几何形状，包括林分和单棵树木。点云不规则、无结构、经常有噪声并受到异常值的污染。研究人员一直在努力开发既能分离树叶和木材，又不会丢失树木几何形状的方法。本文提出了一种仅使用 PC 空间坐标（x、y、z）的解决方案。新算法作为一种过滤方法，利用基于多尺度邻域的几何特征（GFs），如线性、平面度和垂直度，对线性点（木头）和非线性点（树叶）进行分类。这涉及到寻找潜在的木点，并将它们与基于八度分割的方法结合起来，从而开发出一种树形结构。本文的主要贡献在于：(i) 研究了不同 GF 分割线性点和非线性点的潜力；(ii) 引入了一种新方法，对树叶点和树林点进行点分类；(iii) 开发了一种精确的三维树结构。新算法的性能已通过地面激光扫描 PC 进行了验证。对于一棵苏格兰松树，新方法对树叶和木材点进行分类的总体准确率为 97.9%。
{"title":"Development of a Precise Tree Structure from LiDAR Point Clouds","authors":"Abdul Nurunnabi, Felicia Teferle, D. Laefer, Meida Chen, Mir Masoom Ali","doi":"10.5194/isprs-archives-xlviii-2-2024-301-2024","DOIUrl":"https://doi.org/10.5194/isprs-archives-xlviii-2-2024-301-2024","url":null,"abstract":"Abstract. A precise tree structure that represents the distribution of tree stem, branches, and leaves is crucial for accurately capturing the full representation of a tree. Light Detection and Ranging (LiDAR)-based three-dimensional (3D) point clouds (PCs) capture the geometry of scanned objects including forests stands and individual trees. PCs are irregular, unstructured, often noisy, and contaminated by outliers. Researchers have struggled to develop methods to separate leaves and wood without losing the tree geometry. This paper proposes a solution that employs only the spatial coordinates (x, y, z) of the PC. The new algorithm works as a filtering approach, utilizing multi-scale neighborhood-based geometric features (GFs) e.g., linearity, planarity, and verticality to classify linear (wood) and non-linear (leaf) points. This involves finding potential wood points and coupling them with an octree-based segmentation to develop a tree architecture. The main contributions of this paper are (i) investigating the potential of different GFs to split linear and non-linear points, (ii) introducing a novel method that pointwise classifies leaf and wood points, and (iii) developing a precise 3D tree structure. The performance of the new algorithm has been demonstrated through terrestrial laser scanning PCs. For a Scots pine tree, the new method classifies leaf and wood points with an overall accuracy of 97.9%.\u0000","PeriodicalId":505918,"journal":{"name":"The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences","volume":"9 11","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141355825","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}
Pub Date : 2024-06-11DOI: 10.5194/isprs-archives-xlviii-2-2024-387-2024
Priyanka Singh, S. Saran
Abstract. Vector-borne diseases pose a significant threat to human health, particularly in regions vulnerable to climate change. Among these diseases, malaria, caused by the parasite Plasmodium falciparum and transmitted through the Anopheles mosquito, remains a major global health concern, particularly in sub-Saharan Africa. This study explores the use of machine learning techniques to identify and predict the impact of climate change on the transmission dynamics of P. falciparum malaria in Africa.The research utilizes a combination of climate data, epidemiological records, and machine learning algorithms to analyze historical patterns and project future trends in malaria transmission. Key climate variables such as temperature, precipitation, humidity, and vegetation cover are integrated into predictive models to assess their influence on the abundance and distribution of mosquito vectors and the parasite's lifecycle. Through the application of machine learning models such as Maximum Entropy, this study aims to uncover complex relationships between climatic factors and malaria transmission dynamics. By training these models on historical data, they can accurately predict future scenarios under various climate change scenarios. The findings of this research will provide valuable insights into the potential impact of climate change on the spatial and temporal distribution of P. falciparum malaria in Africa. Such insights are crucial for designing targeted interventions and adaptation strategies to mitigate the anticipated rise in malaria cases and associated morbidity and mortality in the region. Moreover, the methodology developed in this study can serve as a framework for assessing and addressing the impact of climate change on other vector-borne diseases globally.
{"title":"Identifying and predicting climate change impact on vector-borne disease using machine learning: Case study of Plasmodium falciparum from Africa","authors":"Priyanka Singh, S. Saran","doi":"10.5194/isprs-archives-xlviii-2-2024-387-2024","DOIUrl":"https://doi.org/10.5194/isprs-archives-xlviii-2-2024-387-2024","url":null,"abstract":"Abstract. Vector-borne diseases pose a significant threat to human health, particularly in regions vulnerable to climate change. Among these diseases, malaria, caused by the parasite Plasmodium falciparum and transmitted through the Anopheles mosquito, remains a major global health concern, particularly in sub-Saharan Africa. This study explores the use of machine learning techniques to identify and predict the impact of climate change on the transmission dynamics of P. falciparum malaria in Africa.The research utilizes a combination of climate data, epidemiological records, and machine learning algorithms to analyze historical patterns and project future trends in malaria transmission. Key climate variables such as temperature, precipitation, humidity, and vegetation cover are integrated into predictive models to assess their influence on the abundance and distribution of mosquito vectors and the parasite's lifecycle. Through the application of machine learning models such as Maximum Entropy, this study aims to uncover complex relationships between climatic factors and malaria transmission dynamics. By training these models on historical data, they can accurately predict future scenarios under various climate change scenarios. The findings of this research will provide valuable insights into the potential impact of climate change on the spatial and temporal distribution of P. falciparum malaria in Africa. Such insights are crucial for designing targeted interventions and adaptation strategies to mitigate the anticipated rise in malaria cases and associated morbidity and mortality in the region. Moreover, the methodology developed in this study can serve as a framework for assessing and addressing the impact of climate change on other vector-borne diseases globally.\u0000","PeriodicalId":505918,"journal":{"name":"The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences","volume":"67 8","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141360113","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}
Pub Date : 2024-06-11DOI: 10.5194/isprs-archives-xlviii-2-2024-129-2024
Jack Henharen, P. Helmholz
Abstract. There is increasing adoption of cost-effective nonmetric camera-equipped unmanned aerial vehicles due to the perceived benefits of timesaving, ease of use, and the accuracy of the digital elevation models that can be produced using structure from motion software. The introduction of systematic elevation errors, doming and bowing, has been evidenced by several authors, and various methods have been identified to reduce these errors. This paper aims to analyse the impact of flight plans on these systematic errors using the especially challenging case of a corridor survey. Two sites were flown for the survey using a DJI Zenmuse. The first site, a car park, was utilised for on-the-job pre-calibration of the camera and consisted of several orbit flights and a double grip flight. Subsequently, an adjacent road (a corridor survey overall 428 m long) was also surveyed at 60 m and 80 m heights using varying flight configurations. This study confirms that pre-calibrating the camera's IOPs significantly reduces the root mean squared elevation error (from 0.268 m to 0.034 m) compared to self-calibrated IOPs using the corridor flights. The impact of flight design on elevation errors confirms a single flight path's risk and the benefits of two or more flight paths, including a point-of-interest orbit flight.
{"title":"Investigation into Camera Calibration Flight Paths for UAV-Based Corridor Surveys","authors":"Jack Henharen, P. Helmholz","doi":"10.5194/isprs-archives-xlviii-2-2024-129-2024","DOIUrl":"https://doi.org/10.5194/isprs-archives-xlviii-2-2024-129-2024","url":null,"abstract":"Abstract. There is increasing adoption of cost-effective nonmetric camera-equipped unmanned aerial vehicles due to the perceived benefits of timesaving, ease of use, and the accuracy of the digital elevation models that can be produced using structure from motion software. The introduction of systematic elevation errors, doming and bowing, has been evidenced by several authors, and various methods have been identified to reduce these errors. This paper aims to analyse the impact of flight plans on these systematic errors using the especially challenging case of a corridor survey. Two sites were flown for the survey using a DJI Zenmuse. The first site, a car park, was utilised for on-the-job pre-calibration of the camera and consisted of several orbit flights and a double grip flight. Subsequently, an adjacent road (a corridor survey overall 428 m long) was also surveyed at 60 m and 80 m heights using varying flight configurations. This study confirms that pre-calibrating the camera's IOPs significantly reduces the root mean squared elevation error (from 0.268 m to 0.034 m) compared to self-calibrated IOPs using the corridor flights. The impact of flight design on elevation errors confirms a single flight path's risk and the benefits of two or more flight paths, including a point-of-interest orbit flight.\u0000","PeriodicalId":505918,"journal":{"name":"The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences","volume":"78 11","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141357745","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}
Pub Date : 2024-06-11DOI: 10.5194/isprs-archives-xlviii-2-2024-401-2024
Alessandra Spadaro, M. Piras, N. Grasso, P. Lollino, Alessandro Parisi, Daniele Giordan
Abstract. Accurate cave surveying is crucial for understanding their genesis, current state, and potential hazards, especially in challenging environments marked by limited accessibility and poor visibility. This study applies geomatics techniques, including Terrestrial Laser Scanning (TLS), SLAM-based Mobile Mapping Systems (MMS), and digital photogrammetry, to create three-dimensional models of artificial caves in Gravina in Puglia, Apulia region, southern Italy. The research aims to assess these methodologies' accuracy, reliability, and performance for structural monitoring and hazard assessment. Despite challenges such as rough conditions, limited accessibility and poor visibility, the study reveals promising insights into the capabilities of these techniques for efficient surveying in complex underground environments. While highlighting the potential of MMS for cost-effective and rapid data acquisition, digital photogrammetry using spherical cameras also emerges as a viable alternative, offering comprehensive data collection capabilities with minimal capture time. Further research is warranted to optimize these techniques for enhanced hazard assessment and structural monitoring in challenging underground environments.
摘要准确的洞穴勘测对于了解洞穴的起源、现状和潜在危险至关重要，尤其是在交通不便、能见度低的挑战性环境中。本研究采用地面激光扫描（TLS）、基于 SLAM 的移动测绘系统（MMS）和数字摄影测量等地理信息技术，在意大利南部阿普利亚大区普利亚的格拉维纳创建人工洞穴的三维模型。研究旨在评估这些方法在结构监测和危险评估方面的准确性、可靠性和性能。尽管存在条件恶劣、交通不便和能见度低等挑战，但研究揭示了这些技术在复杂的地下环境中进行高效勘测的能力，令人充满希望。在强调 MMS 在成本效益和快速数据采集方面的潜力的同时，使用球形相机的数字摄影测量也成为一种可行的替代方法，它能以最短的采集时间提供全面的数据采集能力。有必要开展进一步研究，以优化这些技术，从而在具有挑战性的地下环境中加强危险评估和结构监测。
{"title":"Three-dimensional modelling of artificial caves for geomechanical analysis","authors":"Alessandra Spadaro, M. Piras, N. Grasso, P. Lollino, Alessandro Parisi, Daniele Giordan","doi":"10.5194/isprs-archives-xlviii-2-2024-401-2024","DOIUrl":"https://doi.org/10.5194/isprs-archives-xlviii-2-2024-401-2024","url":null,"abstract":"Abstract. Accurate cave surveying is crucial for understanding their genesis, current state, and potential hazards, especially in challenging environments marked by limited accessibility and poor visibility. This study applies geomatics techniques, including Terrestrial Laser Scanning (TLS), SLAM-based Mobile Mapping Systems (MMS), and digital photogrammetry, to create three-dimensional models of artificial caves in Gravina in Puglia, Apulia region, southern Italy. The research aims to assess these methodologies' accuracy, reliability, and performance for structural monitoring and hazard assessment. Despite challenges such as rough conditions, limited accessibility and poor visibility, the study reveals promising insights into the capabilities of these techniques for efficient surveying in complex underground environments. While highlighting the potential of MMS for cost-effective and rapid data acquisition, digital photogrammetry using spherical cameras also emerges as a viable alternative, offering comprehensive data collection capabilities with minimal capture time. Further research is warranted to optimize these techniques for enhanced hazard assessment and structural monitoring in challenging underground environments.\u0000","PeriodicalId":505918,"journal":{"name":"The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences","volume":"32 23","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141355588","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}
Pub Date : 2024-06-11DOI: 10.5194/isprs-archives-xlviii-2-2024-465-2024
Miao Zhang, Tao Shen, Liang Huo, Yucai Li, Wenfei Shen
Abstract. Urban buildings are an important part of urban morphology, and building height has an important impact on the three-dimensional spatial morphology of cities. At present, research on the two-dimensional morphology of cities is relatively abundant, but there are fewer studies on the characteristics of the three-dimensional undulating morphology of cities and their spatial distribution patterns, and thus knowledge of the degree of utilization of the airspace above the city and its developmental pattern is still relatively lacking.Based on the multi-scale urban agglomeration, the landscape pattern is converted from two-dimensional to three-dimensional, and the overall spatial differentiation can be analyzed more intuitively. The purpose of this paper is to explore the three-dimensional spatial differentiation law of urban architectural landscape pattern. Through in-depth spatial analysis of the architectural landscape in urban areas, it is found that the landscape patterns in different areas show significant differences, and the interrelationship between them and the urban landscape pattern is revealed through the study of three-dimensional characteristics of urban buildings, such as height, density, form and layout. It is found that the different characteristics of urban buildings have a significant impact on the landscape pattern, which leads to the differentiation of urban space. This study provides more comprehensive spatial information for urban planning, contributes to a better understanding of the complexity of urban architectural landscapes, and provides a scientific basis for future urban design and planning.
{"title":"A Study on Spatial Differentiation of Landscape Pattern Based on Three-Dimensional Morphology of Urban Buildings","authors":"Miao Zhang, Tao Shen, Liang Huo, Yucai Li, Wenfei Shen","doi":"10.5194/isprs-archives-xlviii-2-2024-465-2024","DOIUrl":"https://doi.org/10.5194/isprs-archives-xlviii-2-2024-465-2024","url":null,"abstract":"Abstract. Urban buildings are an important part of urban morphology, and building height has an important impact on the three-dimensional spatial morphology of cities. At present, research on the two-dimensional morphology of cities is relatively abundant, but there are fewer studies on the characteristics of the three-dimensional undulating morphology of cities and their spatial distribution patterns, and thus knowledge of the degree of utilization of the airspace above the city and its developmental pattern is still relatively lacking.Based on the multi-scale urban agglomeration, the landscape pattern is converted from two-dimensional to three-dimensional, and the overall spatial differentiation can be analyzed more intuitively. The purpose of this paper is to explore the three-dimensional spatial differentiation law of urban architectural landscape pattern. Through in-depth spatial analysis of the architectural landscape in urban areas, it is found that the landscape patterns in different areas show significant differences, and the interrelationship between them and the urban landscape pattern is revealed through the study of three-dimensional characteristics of urban buildings, such as height, density, form and layout. It is found that the different characteristics of urban buildings have a significant impact on the landscape pattern, which leads to the differentiation of urban space. This study provides more comprehensive spatial information for urban planning, contributes to a better understanding of the complexity of urban architectural landscapes, and provides a scientific basis for future urban design and planning.\u0000","PeriodicalId":505918,"journal":{"name":"The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences","volume":"89 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141359676","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}
Pub Date : 2024-06-11DOI: 10.5194/isprs-archives-xlviii-2-2024-233-2024
F. Matrone, Francesca Gallitto, A. Lingua, P. Maschio
Abstract. Following the enormous technological developments of LiDAR (Light Detection And Ranging) sensors, it is currently easier to find them commercially in the UA (Uncrewed Aerial Systems) sector. In particular, with the Zenmuse L1 by DJI (Dà-Jiāng Innovations) the market has grown globally, mainly due to the compactness of the product that is easily compatible with UAS. The L1 sensor can record up to three returns of the emanating signal, so it can acquire a larger amount of points, such as those below the vegetation. Therefore, in addition to the geometric information of the points, the Zenmuse L1 point clouds also provide other information, such as the number of echo returns from 1 to 3. This data could be exploited to improve the automatic extraction of the digital terrain model (DTM) from the point clouds, hopefully leading to the avoidance of manual correction. This research aims to focus on evaluating whether the addition of the return number feature can affect the identification of the ground points through different computational methods and can improve the time efficiency of state-of-the-art algorithms.
{"title":"Exploitation of the Number of Return Echoes for DTM Extraction from Point Clouds Acquired by LiDAR UAS DJI Zenmuse L1","authors":"F. Matrone, Francesca Gallitto, A. Lingua, P. Maschio","doi":"10.5194/isprs-archives-xlviii-2-2024-233-2024","DOIUrl":"https://doi.org/10.5194/isprs-archives-xlviii-2-2024-233-2024","url":null,"abstract":"Abstract. Following the enormous technological developments of LiDAR (Light Detection And Ranging) sensors, it is currently easier to find them commercially in the UA (Uncrewed Aerial Systems) sector. In particular, with the Zenmuse L1 by DJI (Dà-Jiāng Innovations) the market has grown globally, mainly due to the compactness of the product that is easily compatible with UAS. The L1 sensor can record up to three returns of the emanating signal, so it can acquire a larger amount of points, such as those below the vegetation. Therefore, in addition to the geometric information of the points, the Zenmuse L1 point clouds also provide other information, such as the number of echo returns from 1 to 3. This data could be exploited to improve the automatic extraction of the digital terrain model (DTM) from the point clouds, hopefully leading to the avoidance of manual correction. This research aims to focus on evaluating whether the addition of the return number feature can affect the identification of the ground points through different computational methods and can improve the time efficiency of state-of-the-art algorithms.\u0000","PeriodicalId":505918,"journal":{"name":"The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences","volume":"87 22","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141359569","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}
Pub Date : 2024-06-11DOI: 10.5194/isprs-archives-xlviii-2-2024-449-2024
S. Yoon, Taejung Kim
Abstract. To maintain the efficiency of UAV (unmanned aerial vehicle) remote sensing, rapid image stitching is essential to make multiple UAV images into a seamless mosaic image. Relief displacement introduces variations in object appearance for each image, causing mismatch errors at mosaic seamlines. Traditional approaches involve orthorectifying images using DSMs (digital surface models). While these approaches allow for accurate image stitching, they do not cope with the advantages of UAVs due to their time consumption. In contrast, fast image stitching techniques that do not use orthorectification are well suited for UAV image processing. Related researches have attempted to optimize seamlines to eliminate the errors caused by relief displacement without the use of DSMs. We propose to utilize a TIN (triangular irregular network) of tiepoints to effectively eliminate errors caused by relief displacement while maintaining the fast speed of image stitching. In this study, a TIN is constructed based image tiepoints whose ground coordinates have been obtained through bundle adjustment. The edges of the TIN are used to generate seamlines for image stitching, and the facets of the TIN are used to select minimal images for image stitching and to optimize seamlines. Image stitching results of our proposed method had small error of 1–2 pixels and the processing time of less than 10 minutes for 97 UAV images. This study showed that the proposed method could stitch multiple images while maintaining stable quality using only geometric clues of a TIN of tiepoints.
{"title":"Triangulated Irregular Network based Seamline Determination for Fast Image Stitching of Multiple UAV Images","authors":"S. Yoon, Taejung Kim","doi":"10.5194/isprs-archives-xlviii-2-2024-449-2024","DOIUrl":"https://doi.org/10.5194/isprs-archives-xlviii-2-2024-449-2024","url":null,"abstract":"Abstract. To maintain the efficiency of UAV (unmanned aerial vehicle) remote sensing, rapid image stitching is essential to make multiple UAV images into a seamless mosaic image. Relief displacement introduces variations in object appearance for each image, causing mismatch errors at mosaic seamlines. Traditional approaches involve orthorectifying images using DSMs (digital surface models). While these approaches allow for accurate image stitching, they do not cope with the advantages of UAVs due to their time consumption. In contrast, fast image stitching techniques that do not use orthorectification are well suited for UAV image processing. Related researches have attempted to optimize seamlines to eliminate the errors caused by relief displacement without the use of DSMs. We propose to utilize a TIN (triangular irregular network) of tiepoints to effectively eliminate errors caused by relief displacement while maintaining the fast speed of image stitching. In this study, a TIN is constructed based image tiepoints whose ground coordinates have been obtained through bundle adjustment. The edges of the TIN are used to generate seamlines for image stitching, and the facets of the TIN are used to select minimal images for image stitching and to optimize seamlines. Image stitching results of our proposed method had small error of 1–2 pixels and the processing time of less than 10 minutes for 97 UAV images. This study showed that the proposed method could stitch multiple images while maintaining stable quality using only geometric clues of a TIN of tiepoints.\u0000","PeriodicalId":505918,"journal":{"name":"The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences","volume":"19 18","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141356626","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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