Pub Date : 2024-07-25DOI: 10.5194/isprs-archives-xlviii-4-w11-2024-189-2024
L. Díaz-Vilariño, J. Balado
{"title":"The 19th 3D GeoInfo Conference: Preface Archives","authors":"L. Díaz-Vilariño, J. Balado","doi":"10.5194/isprs-archives-xlviii-4-w11-2024-189-2024","DOIUrl":"https://doi.org/10.5194/isprs-archives-xlviii-4-w11-2024-189-2024","url":null,"abstract":"<jats:p>\u0000 </jats:p>","PeriodicalId":505918,"journal":{"name":"The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences","volume":"6 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141803370","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-2-2024-393-2024
Shuang Song, Luca Morelli, Xinyi Wu, Rongjun Qin, H. Albanwan, F. Remondino
Abstract. Tie-point matching of off-track stereo images is a very challenging task, which can impact bias compensation and digital surface model (DSM) generation. Compared to in-track stereo images, off-track stereo images are more complex primarily due to the radiometric differences caused by sun illumination, sensor responses, atmospheric conditions, and seasonal land cover variations, and secondly due to the longer baseline and larger intersection angle. These challenges significantly limit the use of the vast number of images in satellite archives for automated geometric processing and mapping. Recent advances in deep learning (DL) based matching show promising results against images with diverse illuminations, viewing angles and scales through learning examples. This paper evaluates the potentials of addressing the tie point matching problems in off-track satellite stereo images. Specifically, we focus on stereo pairs that failed or underperformed in classic matching algorithms (i.e., SIFT (scale invariant feature transform)), and evaluate the DL-based tie points matchers by its resulting geometric accuracy in relative orientation, and the generated DSM. The experiments are carried out using 40 off-track satellite stereo pairs from four different regions around the world. We conclude that DL-based methods provide a significant higher success rate in matching challenging multi-temporal stereo pairs, even if their matching accuracy is slightly lower than classic algorithms.
{"title":"Evaluating Learning-based Tie Point Matching for Geometric Processing of Off-Track Satellite Stereo","authors":"Shuang Song, Luca Morelli, Xinyi Wu, Rongjun Qin, H. Albanwan, F. Remondino","doi":"10.5194/isprs-archives-xlviii-2-2024-393-2024","DOIUrl":"https://doi.org/10.5194/isprs-archives-xlviii-2-2024-393-2024","url":null,"abstract":"Abstract. Tie-point matching of off-track stereo images is a very challenging task, which can impact bias compensation and digital surface model (DSM) generation. Compared to in-track stereo images, off-track stereo images are more complex primarily due to the radiometric differences caused by sun illumination, sensor responses, atmospheric conditions, and seasonal land cover variations, and secondly due to the longer baseline and larger intersection angle. These challenges significantly limit the use of the vast number of images in satellite archives for automated geometric processing and mapping. Recent advances in deep learning (DL) based matching show promising results against images with diverse illuminations, viewing angles and scales through learning examples. This paper evaluates the potentials of addressing the tie point matching problems in off-track satellite stereo images. Specifically, we focus on stereo pairs that failed or underperformed in classic matching algorithms (i.e., SIFT (scale invariant feature transform)), and evaluate the DL-based tie points matchers by its resulting geometric accuracy in relative orientation, and the generated DSM. The experiments are carried out using 40 off-track satellite stereo pairs from four different regions around the world. We conclude that DL-based methods provide a significant higher success rate in matching challenging multi-temporal stereo pairs, even if their matching accuracy is slightly lower than classic algorithms.\u0000","PeriodicalId":505918,"journal":{"name":"The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences","volume":"38 18","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141355561","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-2-2024-173-2024
Michael Karnes, Jacob Riffel, Alper Yilmaz
Abstract. Visual navigation has recently seen significant developments with the rise in autonomous navigation. Keypoint-based mapping and localization has served as a reliable localization method for many applications, but the push to run more applications on less expensive hardware becomes extremely limiting. In this paper, we present a novel approach for visual geolocalization and navigation that improves landmark detection reliability while reducing reference map complexity. Similar to prior techniques, we use the process of point based matching schemes to solve for the image-to-map transform. The critical difference is that we use object detection to identify key-regions instead of keypoints. During an initial flight key-regions are mapped into an identity dictionary with their geolocations and few-shot learning encoded descriptors. Then on subsequent flights, key-regions are detected and matched using the identity dictionary for re-identification. Using the identified vehicles as key-regions, the results show that the proposed key-region based localization produces GPS like localization while maintaining a higher resilience to image noise compared to keypoint-based techniques.
{"title":"Key-Region-Based UAV Visual Navigation","authors":"Michael Karnes, Jacob Riffel, Alper Yilmaz","doi":"10.5194/isprs-archives-xlviii-2-2024-173-2024","DOIUrl":"https://doi.org/10.5194/isprs-archives-xlviii-2-2024-173-2024","url":null,"abstract":"Abstract. Visual navigation has recently seen significant developments with the rise in autonomous navigation. Keypoint-based mapping and localization has served as a reliable localization method for many applications, but the push to run more applications on less expensive hardware becomes extremely limiting. In this paper, we present a novel approach for visual geolocalization and navigation that improves landmark detection reliability while reducing reference map complexity. Similar to prior techniques, we use the process of point based matching schemes to solve for the image-to-map transform. The critical difference is that we use object detection to identify key-regions instead of keypoints. During an initial flight key-regions are mapped into an identity dictionary with their geolocations and few-shot learning encoded descriptors. Then on subsequent flights, key-regions are detected and matched using the identity dictionary for re-identification. Using the identified vehicles as key-regions, the results show that the proposed key-region based localization produces GPS like localization while maintaining a higher resilience to image noise compared to keypoint-based techniques.\u0000","PeriodicalId":505918,"journal":{"name":"The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences","volume":"12 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141356603","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-2-2024-115-2024
Michael Grömer, E. Nocerino, A. Calantropio, F. Menna, Ansgar Dreier, Lukas Winiwarter, Gottfried Mandlburger
Abstract. The article presents a practical method that combines low-cost camera systems with remotely operated vehicles (ROVs) to accomplish a comprehensive but economically feasible underwater survey of large hydropower infrastructures. Typically, inspecting reservoirs entails draining them off to allow for visual inspections, which are time-intensive, pose risks to operators' safety and are associated with generation losses. In this regard, ROVs are a much safer and more efficient alternative to traditional methods. The study was conducted at the Pack reservoir in Austria, where a reference framework was set up using terrestrial laser scanning and checkerboard markings for the above-water components. A ROV equipped with a GoPro camera and lighting system for the underwater recordings has been employed. Via a close-range photogrammetric approach, it was possible to generate 3D point clouds of the submerged infrastructure with a survey-grade accuracy level. Various strategies were explored to perform bundle block adjustment (BBA), among these were strategies where ground control points (GCPs) were used, strategies without the use of GCPs but pre-calibrated initial camera parameters and strategies with a combination of using both GCPs and pre-calibrated camera parameters in the BBA. The deployment of an inspection technique using low-cost sensors that can generate highly detailed three-dimensional models of submerged infrastructure areas is presented and discussed, allowing easy detection and localization for maintenance inspection, all while being cost-effective. The paper strengthens the suggestion of best practices that optimize camera settings, considering the effect of electronic image stabilization, suggesting its avoidance, and using advanced calibration methods.
{"title":"High-detail and low-cost underwater inspection of large-scale hydropower dams","authors":"Michael Grömer, E. Nocerino, A. Calantropio, F. Menna, Ansgar Dreier, Lukas Winiwarter, Gottfried Mandlburger","doi":"10.5194/isprs-archives-xlviii-2-2024-115-2024","DOIUrl":"https://doi.org/10.5194/isprs-archives-xlviii-2-2024-115-2024","url":null,"abstract":"Abstract. The article presents a practical method that combines low-cost camera systems with remotely operated vehicles (ROVs) to accomplish a comprehensive but economically feasible underwater survey of large hydropower infrastructures. Typically, inspecting reservoirs entails draining them off to allow for visual inspections, which are time-intensive, pose risks to operators' safety and are associated with generation losses. In this regard, ROVs are a much safer and more efficient alternative to traditional methods. The study was conducted at the Pack reservoir in Austria, where a reference framework was set up using terrestrial laser scanning and checkerboard markings for the above-water components. A ROV equipped with a GoPro camera and lighting system for the underwater recordings has been employed. Via a close-range photogrammetric approach, it was possible to generate 3D point clouds of the submerged infrastructure with a survey-grade accuracy level. Various strategies were explored to perform bundle block adjustment (BBA), among these were strategies where ground control points (GCPs) were used, strategies without the use of GCPs but pre-calibrated initial camera parameters and strategies with a combination of using both GCPs and pre-calibrated camera parameters in the BBA. The deployment of an inspection technique using low-cost sensors that can generate highly detailed three-dimensional models of submerged infrastructure areas is presented and discussed, allowing easy detection and localization for maintenance inspection, all while being cost-effective. The paper strengthens the suggestion of best practices that optimize camera settings, considering the effect of electronic image stabilization, suggesting its avoidance, and using advanced calibration methods.\u0000","PeriodicalId":505918,"journal":{"name":"The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences","volume":"14 8","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141356385","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-2-2024-195-2024
Nan Li, M. Truong, Roland Schwarz, M. Pfennigbauer, A. Ullrich
Abstract. The processing of bathymetric LiDAR waveforms is an important task, as it provides range and radiometric information to determine the precise location of water surface and bottom, and other characteristics like amplitude. The exponential waveform decomposition proved to be an effective algorithm for bathymetric LiDAR waveforms processing, however, it heavily relies on the high-quality initial estimates of the model parameters. This paper proposes to make use of deep learning to obtain the initial values directly from the input received waveforms without any hand-crafted features and prior-knowledges. Additionally, to provide training samples, we presents a method to create the synthetic bathymetric LiDAR waveforms by simulating of the backscatter cross function returned from water bodies. Two networks with different sensitivities of weak signals were trained by these synthetic waveforms, and used to estimate the initial values of the model parameters, a least square optimization follows up to obtain the final waveform decomposition result. This deep learning assisted exponential waveform decomposition method is applied to the real waveforms acquired by RIEGL VQ-840-G. The results show that estimations with the help of deep learning is less influenced by the intermediate peaks backscattered from objects and particles in water, producing a cleaner point cloud with less isolated points below water surface than the original exponential waveform decomposition. Moreover, the proposed sensitive DL-XDC is even able to detect some very weak bottom returns with low SNR.
{"title":"Deep learning assisted exponential waveform decomposition for bathymetric LiDAR","authors":"Nan Li, M. Truong, Roland Schwarz, M. Pfennigbauer, A. Ullrich","doi":"10.5194/isprs-archives-xlviii-2-2024-195-2024","DOIUrl":"https://doi.org/10.5194/isprs-archives-xlviii-2-2024-195-2024","url":null,"abstract":"Abstract. The processing of bathymetric LiDAR waveforms is an important task, as it provides range and radiometric information to determine the precise location of water surface and bottom, and other characteristics like amplitude. The exponential waveform decomposition proved to be an effective algorithm for bathymetric LiDAR waveforms processing, however, it heavily relies on the high-quality initial estimates of the model parameters. This paper proposes to make use of deep learning to obtain the initial values directly from the input received waveforms without any hand-crafted features and prior-knowledges. Additionally, to provide training samples, we presents a method to create the synthetic bathymetric LiDAR waveforms by simulating of the backscatter cross function returned from water bodies. Two networks with different sensitivities of weak signals were trained by these synthetic waveforms, and used to estimate the initial values of the model parameters, a least square optimization follows up to obtain the final waveform decomposition result. This deep learning assisted exponential waveform decomposition method is applied to the real waveforms acquired by RIEGL VQ-840-G. The results show that estimations with the help of deep learning is less influenced by the intermediate peaks backscattered from objects and particles in water, producing a cleaner point cloud with less isolated points below water surface than the original exponential waveform decomposition. Moreover, the proposed sensitive DL-XDC is even able to detect some very weak bottom returns with low SNR.\u0000","PeriodicalId":505918,"journal":{"name":"The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences","volume":"38 26","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141358801","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-2-2024-471-2024
Yunhan Zhang, J. Qin, Ming Li, Qiyao Han, A. Gruen, Deren Li, J. Zhong
Abstract. Structural connectivity is an important factor in preserving coral diversity. It maintains the stability and adaptability of coral reef ecosystems by facilitating ecological flow, species migration, and gene exchange between coral communities. However, there has always been a lack of consistent solutions for accurate structural connectivity describing and quantifying, which has hindered the understanding of the complex ecological processes in coral reefs. Based on this, this paper proposes a framework that uses advanced remote sensing and deep learning technologies to assess coral structural connectivity. Specifically, accurate coral patches are firstly identified through image segmentation techniques. And the structural connectivity is quantified by assessing the connectivity patterns between and within these coral patches. Furthermore, Tetiaroa Island in the South Pacific is used as a case study to validate the effectiveness and accuracy of the framework in assessing coral structural connectivity. The experimental results demonstrate that the framework proposed in this paper provides a powerful tool for understanding the internal ecological processes and external spatial patterns of coral reef ecosystems, thereby promoting scientific understanding and effective management of coral reef conservation.
{"title":"Advancing Coral Structural Connectivity Analysis through Deep Learning and Remote Sensing: A Case Study of South Pacific Tetiaroa Island","authors":"Yunhan Zhang, J. Qin, Ming Li, Qiyao Han, A. Gruen, Deren Li, J. Zhong","doi":"10.5194/isprs-archives-xlviii-2-2024-471-2024","DOIUrl":"https://doi.org/10.5194/isprs-archives-xlviii-2-2024-471-2024","url":null,"abstract":"Abstract. Structural connectivity is an important factor in preserving coral diversity. It maintains the stability and adaptability of coral reef ecosystems by facilitating ecological flow, species migration, and gene exchange between coral communities. However, there has always been a lack of consistent solutions for accurate structural connectivity describing and quantifying, which has hindered the understanding of the complex ecological processes in coral reefs. Based on this, this paper proposes a framework that uses advanced remote sensing and deep learning technologies to assess coral structural connectivity. Specifically, accurate coral patches are firstly identified through image segmentation techniques. And the structural connectivity is quantified by assessing the connectivity patterns between and within these coral patches. Furthermore, Tetiaroa Island in the South Pacific is used as a case study to validate the effectiveness and accuracy of the framework in assessing coral structural connectivity. The experimental results demonstrate that the framework proposed in this paper provides a powerful tool for understanding the internal ecological processes and external spatial patterns of coral reef ecosystems, thereby promoting scientific understanding and effective management of coral reef conservation.\u0000","PeriodicalId":505918,"journal":{"name":"The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences","volume":"38 24","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141358803","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-2-2024-335-2024
P. C. Pesántez-Cabrera
Abstract. In Reina del Cisne (Cuenca-Ecuador), a dynamic sliding process occurred due to a cut made at the beginning of 2018 on the hillside without technical considerations for the construction of an access road to a house in the sector. From January 2019 to June 2019, the period analyzed in this work, the landslide caused complete structural damage to dwellings near the hillside and partial damage to houses farther away. It also led to the total collapse of the path that initiated the landslide. Field visits and comparisons using CloudCompare of point clouds obtained from UAV flights between January 2019 and June 2019 highlighted the high activity of this landslide. The analysis demonstrates the effectiveness of this technique for early detection of landslides, enabling timely warnings for inhabitants to take immediate measures to avoid disasters.
{"title":"Land Movement Detection from UAV Images for a Sustainable World","authors":"P. C. Pesántez-Cabrera","doi":"10.5194/isprs-archives-xlviii-2-2024-335-2024","DOIUrl":"https://doi.org/10.5194/isprs-archives-xlviii-2-2024-335-2024","url":null,"abstract":"Abstract. In Reina del Cisne (Cuenca-Ecuador), a dynamic sliding process occurred due to a cut made at the beginning of 2018 on the hillside without technical considerations for the construction of an access road to a house in the sector. From January 2019 to June 2019, the period analyzed in this work, the landslide caused complete structural damage to dwellings near the hillside and partial damage to houses farther away. It also led to the total collapse of the path that initiated the landslide. Field visits and comparisons using CloudCompare of point clouds obtained from UAV flights between January 2019 and June 2019 highlighted the high activity of this landslide. The analysis demonstrates the effectiveness of this technique for early detection of landslides, enabling timely warnings for inhabitants to take immediate measures to avoid disasters.\u0000","PeriodicalId":505918,"journal":{"name":"The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences","volume":"15 17","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141356686","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-2-2024-65-2024
Gustavo Rota Collegio, A. P. Dal Poz, Antonio Gaudencio Guimarães Filho, Ayman Habib
Abstract. Due to the frequent road network changes, keeping them updated is fundamental for several purposes. Currently, models based on Deep Learning (DL), specifically, Convolutional Neural Networks (CNNs), such as encoder-decoder type, are state-of-the-art for this purpose. In this context, the high performance in CNNs has two aspects involved: the model needs a large labeled dataset, and the dataset belongs to the same probability distribution. In practical applications, however, this may not hold, since there is a domain shift effect, and it is not customary for the availability of labeled data. To approach these challenges, we propose to adapt the U-Net architecture (encoder-decoder) to the Unsupervised Domain Adaptation (UDA) that does not need labeling data to minimize the domain shift effect. Our results demonstrate that the proposed method contributes to road segmentation, whose model reaches 74.31% (IoU) and 85.04% (F1), against the same model without UDA that reaches 67.36% (IoU) and 80.02% (F1). This implies that the information that comes from the target domain, even unsupervised, contributes to adversarial learning, improving the generalization capacity of the model, enhancing aspects such as better discrimination surrounding classes, and in the geometric delineation of the road network.
{"title":"Convolutional Neural Networks for Road Detection: An Unsupervised Domain Adaptation Approach","authors":"Gustavo Rota Collegio, A. P. Dal Poz, Antonio Gaudencio Guimarães Filho, Ayman Habib","doi":"10.5194/isprs-archives-xlviii-2-2024-65-2024","DOIUrl":"https://doi.org/10.5194/isprs-archives-xlviii-2-2024-65-2024","url":null,"abstract":"Abstract. Due to the frequent road network changes, keeping them updated is fundamental for several purposes. Currently, models based on Deep Learning (DL), specifically, Convolutional Neural Networks (CNNs), such as encoder-decoder type, are state-of-the-art for this purpose. In this context, the high performance in CNNs has two aspects involved: the model needs a large labeled dataset, and the dataset belongs to the same probability distribution. In practical applications, however, this may not hold, since there is a domain shift effect, and it is not customary for the availability of labeled data. To approach these challenges, we propose to adapt the U-Net architecture (encoder-decoder) to the Unsupervised Domain Adaptation (UDA) that does not need labeling data to minimize the domain shift effect. Our results demonstrate that the proposed method contributes to road segmentation, whose model reaches 74.31% (IoU) and 85.04% (F1), against the same model without UDA that reaches 67.36% (IoU) and 80.02% (F1). This implies that the information that comes from the target domain, even unsupervised, contributes to adversarial learning, improving the generalization capacity of the model, enhancing aspects such as better discrimination surrounding classes, and in the geometric delineation of the road network.\u0000","PeriodicalId":505918,"journal":{"name":"The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences","volume":"15 11","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141356238","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-2-2024-181-2024
Xiangxiong Kong
Abstract. Historic structures are important for our society but could be prone to structural deterioration due to long service durations and natural impacts. Monitoring the deterioration of historic structures becomes essential for stakeholders to take appropriate interventions. Existing work in the literature primarily focuses on assessing the structural damage at a given moment instead of evaluating the development of deterioration over time. To address this gap, we proposed a novel five-component digital twin framework to monitor time-varying changes in historic structures. A testbed of a casemate in Fort Soledad on the island of Guam was selected to validate our framework. Using this testbed, key implementation steps in our digital twin framework were performed. The findings from this study confirm that our digital twin framework can effectively monitor deterioration over time, which is an urgent need in the cultural heritage preservation community.
{"title":"Monitoring Time-Varying Changes of Historic Structures Through Photogrammetry-Driven Digital Twinning","authors":"Xiangxiong Kong","doi":"10.5194/isprs-archives-xlviii-2-2024-181-2024","DOIUrl":"https://doi.org/10.5194/isprs-archives-xlviii-2-2024-181-2024","url":null,"abstract":"Abstract. Historic structures are important for our society but could be prone to structural deterioration due to long service durations and natural impacts. Monitoring the deterioration of historic structures becomes essential for stakeholders to take appropriate interventions. Existing work in the literature primarily focuses on assessing the structural damage at a given moment instead of evaluating the development of deterioration over time. To address this gap, we proposed a novel five-component digital twin framework to monitor time-varying changes in historic structures. A testbed of a casemate in Fort Soledad on the island of Guam was selected to validate our framework. Using this testbed, key implementation steps in our digital twin framework were performed. The findings from this study confirm that our digital twin framework can effectively monitor deterioration over time, which is an urgent need in the cultural heritage preservation community.\u0000","PeriodicalId":505918,"journal":{"name":"The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences","volume":"48 15","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141354994","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-2-2024-153-2024
Nathan Hui, E. Lo, D. Rissolo, F. Kuester
Abstract. Photogrammetry is an established technique for producing 3D representations of submerged structures in shallow, naturally lit environments. Natural light is not available in more extreme environments such as in the deep ocean or submerged caves, which are major applications for photogrammetric survey. Additionally, these environments are often accessed with resource-limited sensor platforms, necessitating efficient use of power constraining the level of artificial illumination that can be deployed. A method to estimate the amount of light needed to achieve sufficient image quality in underwater photogrammetric acquisition systems is presented.
{"title":"Lighting Model for Underwater Photogrammetric Captures","authors":"Nathan Hui, E. Lo, D. Rissolo, F. Kuester","doi":"10.5194/isprs-archives-xlviii-2-2024-153-2024","DOIUrl":"https://doi.org/10.5194/isprs-archives-xlviii-2-2024-153-2024","url":null,"abstract":"Abstract. Photogrammetry is an established technique for producing 3D representations of submerged structures in shallow, naturally lit environments. Natural light is not available in more extreme environments such as in the deep ocean or submerged caves, which are major applications for photogrammetric survey. Additionally, these environments are often accessed with resource-limited sensor platforms, necessitating efficient use of power constraining the level of artificial illumination that can be deployed. A method to estimate the amount of light needed to achieve sufficient image quality in underwater photogrammetric acquisition systems is presented.\u0000","PeriodicalId":505918,"journal":{"name":"The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences","volume":"21 21","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141356498","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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