The May 222021 M_(W)7.4 Madoi,Qinghai,China earthquake presented a rare opportunity to apply the modern unmanned aerial vehicle(UAV)photography method in extreme altitude and weather conditions to image surface ruptur...The May 222021 M_(W)7.4 Madoi,Qinghai,China earthquake presented a rare opportunity to apply the modern unmanned aerial vehicle(UAV)photography method in extreme altitude and weather conditions to image surface ruptures and near-field effects of earthquake-related surface deformations in the remote Tibet.High-resolution aerial photographs were acquired in the days immediately following the mainshock.The complex surface rupture patterns associated with this event were covered comprehensively at 3-6 cm resolution.This effort represents the first time that an earthquake rupture in the interior of the Qinghai-Tibetan Plateau has been fully and systematically captured by such high-resolution imagery,with an unprecedented level of detail,over its entire length.The dataset has proven valuable in documenting subtle and transient rupture features,such as the significant mole-tracks and opening fissures,which were ubiquitous coseismically but degraded during the subsequent summer storm season.Such high-quality imagery also helps to document with high fidelity the fractures of the surface rupture zone(supplements of this paper),the pattern related to how the faults ruptured to the ground surface,and the distribution of off-fault damage.In combination with other ground-based mapping efforts,the data will be analyzed in the following months to better understand the mechanics of earthquake rupture related to the fault zone rheology,rupture dynamics,and frictional properties along with the fault interface.展开更多
Accurately quantifying rates of soil erosion requires capturing both the volumetric nature of the visible,convergent fluvial pathways(also known as rills)and the subtle nature of the less-visible,diffuse pathways(inte...Accurately quantifying rates of soil erosion requires capturing both the volumetric nature of the visible,convergent fluvial pathways(also known as rills)and the subtle nature of the less-visible,diffuse pathways(interrill areas).The aim of this study was to use Rare Earth Oxide(REO)tracers and Structure-from-Motion(SfM)photogrammetry to elucidate retrospective information about soil erosion rates and sediment sources during different soil erosion conditions,within a controlled laboratory environment.The experimental conditions created erosion events consistent with diffuse and convergent erosion processes.REO tracers allowed the sediment transport distances of over 2 m to be described,and helped resolved the relative contribution of diffuse and convergent soil erosion;interrill areas were also iden-tified as a significant sediment sources soil loss under convergent erosion conditions.While the potential for SfM photogrammetry to resolve sub-millimetre elevations changes was demonstrated,under some conditions non-erosional changes in surface elevation,such as compaction,exceeded volumes of soil loss via diffuse erosion.The discrepancies between SfM Photogrammetry calculations and REO tagged sediment export were beneficial,identifying that during soil erosion events sediment in both aggregate and particle form is deposited within the convergent features,even when the rill extended the full length of the soil surface.The combination of SfM photogrammetry and REO tracers has provided a novel platform for building a spatial understanding of patterns of soil loss and source apportionment between rill and interrill erosion.展开更多
Structure-from-Motion(SfM)techniques have been widely used for 3D geometry reconstruction from multi-view images.Nevertheless,the efficiency and quality of the reconstructed geometry depends on multiple factors,i.e.,t...Structure-from-Motion(SfM)techniques have been widely used for 3D geometry reconstruction from multi-view images.Nevertheless,the efficiency and quality of the reconstructed geometry depends on multiple factors,i.e.,the base-height ratio,intersection angle,overlap,and ground control points,etc.,which are rarely quantified in real-world applications.To answer this question,in this paper,we take a data-driven approach by analyzing hundreds of terrestrial stereo image configurations through a typical SfM algorithm.Two main meta-parameters with respect to base-height ratio and intersection angle are analyzed.Following the results,we propose a Skeletal Camera Network(SCN)and embed it into the SfM to lead to a novel SfM scheme called SCN-SfM,which limits tie-point matching to the remaining connected image pairs in SCN.The proposed method was applied in three terrestrial datasets.Experimental results have demonstrated the effectiveness of the proposed SCN-SfM to achieve 3D geometry with higher accuracy and fast time efficiency compared to the typical SfM method,whereas the completeness of the geometry is comparable.展开更多
Deep learning models require many instances of training data to be able to accurately detect the desired object.However,the labeling of images is currently conducted manually due to the inclusion of irrelevant scenes ...Deep learning models require many instances of training data to be able to accurately detect the desired object.However,the labeling of images is currently conducted manually due to the inclusion of irrelevant scenes in the original images,especially for the data collected in a dynamic environment such as from drone imagery.In this work,we developed an automated extraction of training data set using photogrammetry.This approach works with continuous and arbitrary collection of visual data,such as video,encompassing a stationary object.A dense point cloud was first generated to estimate the geometric relationship between individual images using a structure-from-motion(SfM)technique,followed by user-designated region-of-interests,ROIs,that are automatically extracted from the original images.An orthophoto mosaic of the façade plane of the building shown in the point cloud was created to ease the user’s selection of an intended labeling region of the object,which is a one-time process.We verified this method by using the ROIs extracted from a previously obtained dataset to train and test a convolutional neural network which is modeled to detect damage locations.The method put forward in this work allows a relatively small amount of labeling to generate a large amount of training data.We successfully demonstrate the capabilities of the technique with the dataset previously collected by a drone from an abandoned building in which many of the glass windows have been damaged.展开更多
Generally,the distributed bundle adjustment(DBA)method uses multiple worker nodes to solve the bundle adjustment problems and overcomes the computation and memory storage limitations of a single computer.However,the p...Generally,the distributed bundle adjustment(DBA)method uses multiple worker nodes to solve the bundle adjustment problems and overcomes the computation and memory storage limitations of a single computer.However,the performance considerably degrades owing to the overhead introduced by the additional block partitioning step and synchronous waiting.Therefore,we propose a low-overhead consensus framework.A partial barrier based asynchronous method is proposed to early achieve consensus with respect to the faster worker nodes to avoid waiting for the slower ones.A scene summarization procedure is designed and integrated into the block partitioning step to ensure that clustering can be performed on the small summarized scene.Experiments conducted on public datasets show that our method can improve the worker node utilization rate and reduce the block partitioning time.Also,sample applications are demonstrated using our large-scale culture heritage datasets.展开更多
Managed realignment(MR)schemes are being implemented to compensate for the degradation of coastal habitats.However,evidence suggests that MR sites have lower biodiversity than anticipated,which has been linked to poor...Managed realignment(MR)schemes are being implemented to compensate for the degradation of coastal habitats.However,evidence suggests that MR sites have lower biodiversity than anticipated,which has been linked to poor drainage.Despite creek networks playing an important role in enhancing site drainage in natural intertidal environments,there remains a shortage of data on the formation and evolution of creeks within MR sites.This study evaluates creek development at the Medmerry Managed Realignment Site,UK.Creek development is investigated using differential global positioning system(dGPS)data,supported by sedimentological analyses and a high-resolution digital surface model(DSM)derived from images taken using a small unmanned aerial vehicle.Measurements indicated that creeks will develop relatively quickly,but are influenced by differences in the sub-surface sedimentological conditions.A suitable level of agreement was found between the DSM and dGPS measurements,demonstrating the appropriateness of this method to study creek development within intertidal environments at a higher resolution than traditional surveying techniques.These results are used to propose the collapse of sub-surface piping as the primary creek formation mechanism.Findings are discussed in terms of increasing the success of MR schemes and enhancing site design to maximise the ecosystem services provided.展开更多
基金This work was supported by the National Natural Science Foundation of China(U1839203,42011540385)the National Key Laboratory of Earthquake Dynamics(LED2020B03,IGCEA1812)the Science and Technology Projects of Qinghai Province(2020-ZJ-752).
文摘The May 222021 M_(W)7.4 Madoi,Qinghai,China earthquake presented a rare opportunity to apply the modern unmanned aerial vehicle(UAV)photography method in extreme altitude and weather conditions to image surface ruptures and near-field effects of earthquake-related surface deformations in the remote Tibet.High-resolution aerial photographs were acquired in the days immediately following the mainshock.The complex surface rupture patterns associated with this event were covered comprehensively at 3-6 cm resolution.This effort represents the first time that an earthquake rupture in the interior of the Qinghai-Tibetan Plateau has been fully and systematically captured by such high-resolution imagery,with an unprecedented level of detail,over its entire length.The dataset has proven valuable in documenting subtle and transient rupture features,such as the significant mole-tracks and opening fissures,which were ubiquitous coseismically but degraded during the subsequent summer storm season.Such high-quality imagery also helps to document with high fidelity the fractures of the surface rupture zone(supplements of this paper),the pattern related to how the faults ruptured to the ground surface,and the distribution of off-fault damage.In combination with other ground-based mapping efforts,the data will be analyzed in the following months to better understand the mechanics of earthquake rupture related to the fault zone rheology,rupture dynamics,and frictional properties along with the fault interface.
文摘Accurately quantifying rates of soil erosion requires capturing both the volumetric nature of the visible,convergent fluvial pathways(also known as rills)and the subtle nature of the less-visible,diffuse pathways(interrill areas).The aim of this study was to use Rare Earth Oxide(REO)tracers and Structure-from-Motion(SfM)photogrammetry to elucidate retrospective information about soil erosion rates and sediment sources during different soil erosion conditions,within a controlled laboratory environment.The experimental conditions created erosion events consistent with diffuse and convergent erosion processes.REO tracers allowed the sediment transport distances of over 2 m to be described,and helped resolved the relative contribution of diffuse and convergent soil erosion;interrill areas were also iden-tified as a significant sediment sources soil loss under convergent erosion conditions.While the potential for SfM photogrammetry to resolve sub-millimetre elevations changes was demonstrated,under some conditions non-erosional changes in surface elevation,such as compaction,exceeded volumes of soil loss via diffuse erosion.The discrepancies between SfM Photogrammetry calculations and REO tagged sediment export were beneficial,identifying that during soil erosion events sediment in both aggregate and particle form is deposited within the convergent features,even when the rill extended the full length of the soil surface.The combination of SfM photogrammetry and REO tracers has provided a novel platform for building a spatial understanding of patterns of soil loss and source apportionment between rill and interrill erosion.
基金National Natural Science Foundation of China(No.41701534)Open Fund of State Key Laboratory of Coal Resources and Safe Mining(No.SKLCRSM19KFA01)+1 种基金Ecological and Smart Mine Joint Foundation of Hebei Province(No.E2020402086)State Key Laboratory ofGeohazard Prevention and Geoenvironment Protection(No.SKLGP2019K015)
文摘Structure-from-Motion(SfM)techniques have been widely used for 3D geometry reconstruction from multi-view images.Nevertheless,the efficiency and quality of the reconstructed geometry depends on multiple factors,i.e.,the base-height ratio,intersection angle,overlap,and ground control points,etc.,which are rarely quantified in real-world applications.To answer this question,in this paper,we take a data-driven approach by analyzing hundreds of terrestrial stereo image configurations through a typical SfM algorithm.Two main meta-parameters with respect to base-height ratio and intersection angle are analyzed.Following the results,we propose a Skeletal Camera Network(SCN)and embed it into the SfM to lead to a novel SfM scheme called SCN-SfM,which limits tie-point matching to the remaining connected image pairs in SCN.The proposed method was applied in three terrestrial datasets.Experimental results have demonstrated the effectiveness of the proposed SCN-SfM to achieve 3D geometry with higher accuracy and fast time efficiency compared to the typical SfM method,whereas the completeness of the geometry is comparable.
基金supported by the National Research Foundation of Korea(NRF)grant funded by the Ko-rean Government(MSIT)(No.RS-2022-NR067080 and RS-2025-05515607).
文摘Deep learning models require many instances of training data to be able to accurately detect the desired object.However,the labeling of images is currently conducted manually due to the inclusion of irrelevant scenes in the original images,especially for the data collected in a dynamic environment such as from drone imagery.In this work,we developed an automated extraction of training data set using photogrammetry.This approach works with continuous and arbitrary collection of visual data,such as video,encompassing a stationary object.A dense point cloud was first generated to estimate the geometric relationship between individual images using a structure-from-motion(SfM)technique,followed by user-designated region-of-interests,ROIs,that are automatically extracted from the original images.An orthophoto mosaic of the façade plane of the building shown in the point cloud was created to ease the user’s selection of an intended labeling region of the object,which is a one-time process.We verified this method by using the ROIs extracted from a previously obtained dataset to train and test a convolutional neural network which is modeled to detect damage locations.The method put forward in this work allows a relatively small amount of labeling to generate a large amount of training data.We successfully demonstrate the capabilities of the technique with the dataset previously collected by a drone from an abandoned building in which many of the glass windows have been damaged.
基金Project supported by the Key R&D Program of Zhejiang Province,China(No.2018C03051)the Key Scientific Research Base for Digital Conservation of Cave Temples of the National Cultural Heritage Administration,China。
文摘Generally,the distributed bundle adjustment(DBA)method uses multiple worker nodes to solve the bundle adjustment problems and overcomes the computation and memory storage limitations of a single computer.However,the performance considerably degrades owing to the overhead introduced by the additional block partitioning step and synchronous waiting.Therefore,we propose a low-overhead consensus framework.A partial barrier based asynchronous method is proposed to early achieve consensus with respect to the faster worker nodes to avoid waiting for the slower ones.A scene summarization procedure is designed and integrated into the block partitioning step to ensure that clustering can be performed on the small summarized scene.Experiments conducted on public datasets show that our method can improve the worker node utilization rate and reduce the block partitioning time.Also,sample applications are demonstrated using our large-scale culture heritage datasets.
文摘Managed realignment(MR)schemes are being implemented to compensate for the degradation of coastal habitats.However,evidence suggests that MR sites have lower biodiversity than anticipated,which has been linked to poor drainage.Despite creek networks playing an important role in enhancing site drainage in natural intertidal environments,there remains a shortage of data on the formation and evolution of creeks within MR sites.This study evaluates creek development at the Medmerry Managed Realignment Site,UK.Creek development is investigated using differential global positioning system(dGPS)data,supported by sedimentological analyses and a high-resolution digital surface model(DSM)derived from images taken using a small unmanned aerial vehicle.Measurements indicated that creeks will develop relatively quickly,but are influenced by differences in the sub-surface sedimentological conditions.A suitable level of agreement was found between the DSM and dGPS measurements,demonstrating the appropriateness of this method to study creek development within intertidal environments at a higher resolution than traditional surveying techniques.These results are used to propose the collapse of sub-surface piping as the primary creek formation mechanism.Findings are discussed in terms of increasing the success of MR schemes and enhancing site design to maximise the ecosystem services provided.
