The measurement accuracy of the Mobile Mapping System (MMS) is the main problem, which restricts its development and application, so how to calibrate the MMS to improve its measure-ment accuracy has always been a rese...The measurement accuracy of the Mobile Mapping System (MMS) is the main problem, which restricts its development and application, so how to calibrate the MMS to improve its measure-ment accuracy has always been a research hotspot in the industry. This paper proposes a position and attitude calibration method with error correction based on the combination of the feature point and feature surface. First, the initial value of the spatial position relation-ship between each sensor of MMS is obtained by close-range photogrammetry. Second, the optimal solution for error correction is calculated by feature points in global coordinates jointly measured with International GNSS Service (IGS) stations. Then, the final transformation para-meters are solved by combining the initial values obtained originally, thereby realizing the rapid calibration of the MMS. Finally, it analyzed the RMSE of MMS point cloud after calibration, and the results demonstrate the feasibility of the calibration approach proposed by this method. Under the condition of a single measurement sensor accuracy is low, the plane and elevation absolute accuracy of the point cloud after calibration can reach 0.043 m and 0.072 m, respectively, and the relative accuracy is smaller than 0.02 m. It meets the precision require-ments of data acquisition for MMS. It is of great significance for promoting the development of MMS technology and the application of some novel techniques in the future, such as auton-omous driving, digital twin city, urban brain et al.展开更多
In light of the limited efficacy of conventional methods for identifying pavement cracks and the absence of comprehensive depth and location data in two-dimensional photographs,this study presents an intelligent strat...In light of the limited efficacy of conventional methods for identifying pavement cracks and the absence of comprehensive depth and location data in two-dimensional photographs,this study presents an intelligent strategy for extracting road cracks.This methodology involves the integration of laser point cloud data obtained from a vehicle-mounted system and a panoramic sequence of images.The study employs a vehicle-mounted LiDAR measurement system to acquire laser point cloud and panoramic sequence image data simultaneously.A convolutional neural network is utilized to extract cracks from the panoramic sequence image.The extracted sequence image is then aligned with the laser point cloud,enabling the assignment of RGB information to the vehicle-mounted three dimensional(3D)point cloud and location information to the two dimensional(2D)panoramic image.Additionally,a threshold value is set based on the crack elevation change to extract the aligned roadway point cloud.The three-dimensional data pertaining to the cracks can be acquired.The experimental findings demonstrate that the use of convolutional neural networks has yielded noteworthy outcomes in the extraction of road cracks.The utilization of point cloud and image alignment techniques enables the extraction of precise location data pertaining to road cracks.This approach exhibits superior accuracy when compared to conventional methods.Moreover,it facilitates rapid and accurate identification and localization of road cracks,thereby playing a crucial role in ensuring road maintenance and traffic safety.Consequently,this technique finds extensive application in the domains of intelligent transportation and urbanization development.The technology exhibits significant promise for use in the domains of intelligent transportation and city development.展开更多
The characteristics of drought in Xinjiang Uygur Autonomous Region(Xinjiang),China have changed due to changes in the spatiotemporal patterns of temperature and precipitation,however,the effects of temperature and pre...The characteristics of drought in Xinjiang Uygur Autonomous Region(Xinjiang),China have changed due to changes in the spatiotemporal patterns of temperature and precipitation,however,the effects of temperature and precipitation—the two most important factors influencing drought—have not yet been thoroughly explored in this region.In this study,we first calculated the standard precipitation evapotranspiration index(SPEI)in Xinjiang from 1980 to 2020 based on the monthly precipitation and monthly average temperature.Then the spatiotemporal characteristics of temperature,precipitation,and drought in Xinjiang from 1980 to 2020 were analyzed using the Theil-Sen median trend analysis method and Mann-Kendall test.A series of SPEI-based scenario-setting experiments by combining the observed and detrended climatic factors were utilized to quantify the effects of individual climatic factor(i.e.,temperature and precipitation).The results revealed that both temperature and precipitation had experienced increasing trends at most meteorological stations in Xinjiang from 1980 to 2020,especially the spring temperature and winter precipitation.Due to the influence of temperature,trends of intensifying drought have been observed at spring,summer,autumn,and annual scales.In addition,the drought trends in southern Xinjiang were more notable than those in northern Xinjiang.From 1980 to 2020,temperature trends exacerbated drought trends,but precipitation trends alleviated drought trends in Xinjiang.Most meteorological stations in Xinjiang exhibited temperature-dominated drought trend except in winter;in winter,most stations exhibited precipitation-dominated wetting trend.The findings of this study highlight the importance of the impact of temperature on drought in Xinjiang and deepen the understanding of the factors influencing drought.展开更多
Dense matching of remote sensing images is a key step in the generation of accurate digital surface models.The semi-global matching algorithm comprehensively considers the advantages and disadvantages of local matchin...Dense matching of remote sensing images is a key step in the generation of accurate digital surface models.The semi-global matching algorithm comprehensively considers the advantages and disadvantages of local matching and global matching in terms of matching effect and computational efficiency,so it is widely used in close-range,aerial and satellite image matching.Based on the analysis of the original semi-global matching algorithm,this paper proposes a semi-global high-resolution remote sensing image that takes into account the geometric constraints of the connection points and the image texture information based on the large amount of high-resolution remote sensing image data and the characteristics of clear image texture.The method includes 4 parts:(1)Precise orientation.Aiming at the system error in the image orientation model,the system error of the rational function model is compensated by the geometric constraint relationship of the connecting points between the images,and the sub-pixel positioning accuracy is obtained;(2)Epipolar image generation.After the original image is divided into blocks,the epipolar image is generated based on the projection trajectory method;(3)Image dense matching.In order to reduce the size of the cost space and calculation time,the image is pyramided and then semi-globally matched layer by layer.In the matching process,the disparity map expansion and erosion algorithm that takes into account the image texture information is introduced to restrict the disparity search range and better retain the edge characteristics of the ground objects;(4)Generate DSM.In order to test the matching effect,the weighted median filter algorithm is used to filter the disparity map,and the DSM is obtained based on the principle of forward intersection.Finally,the paper uses the matching results of WordView3 and Ziyuan No.3 stereo image to verify the effectiveness of this method.展开更多
The rapid economic growth,urbanization,and industrialization have led to a scarcity of land resources in coastal areas,exacerbating the conflict between humans and the environment.In order to promote economic developm...The rapid economic growth,urbanization,and industrialization have led to a scarcity of land resources in coastal areas,exacerbating the conflict between humans and the environment.In order to promote economic development,attention has turned to the sea,and various coastal engineering projects have been undertaken,sparking a wave of land reclamation.However,while these efforts bring economic and social benefits,they also have implications for ecological relationships.To respond to and plan for changes in the coastline and land cover in a timely manner,this paper proposes and constructs a GIS system that integrates remote sensing image recognition models.The system combines geographic information system development technology with image recognition technology,streamlining the processing and identification of image data.This approach is particularly advantageous for marine management departments in their long-term monitoring and dynamic management of coastal lines,ensuring a more effective and efficient response.展开更多
Underwater images are inherently degraded by color distortion,contrast reduction,and uneven brightness,primarily due to light absorption and scattering in water.To mitigate these challenges,a novel enhancement approac...Underwater images are inherently degraded by color distortion,contrast reduction,and uneven brightness,primarily due to light absorption and scattering in water.To mitigate these challenges,a novel enhancement approach is proposed,integrating Local Adaptive Color Correction(LACC)with contrast enhancement based on adaptive Rayleigh distribution stretching and CLAHE(LACC-RCE).Conventional color correction methods predominantly employ global adjustment strategies,which are often inadequate for handling spatially varying color distortions.In contrast,the proposed LACC method incorporates local color analysis,tone-weighted control,and spatially adaptive adjustments,allowing for region-specific color correction.This approach effectively enhances color fidelity and perceptual naturalness,addressing the limitations of global correction techniques.For contrast enhancement,the proposed method leverages the global mapping characteristics of the Rayleigh distribution to improve overall contrast,while CLAHE is employed to adaptively enhance local regions.A weighted fusion strategy is then applied to synthesize high-quality underwater images.Experimental results indicate that LACC-RCE surpasses conventional methods in color restoration,contrast optimization,and detail preservation,thereby enhancing the visual quality of underwater images.This improvement facilitates more reliable inputs for underwater object detection and recognition tasks.展开更多
Since the beginning of the twenty-first century,several countries have made great efforts to develop space remote sensing for building a high-resolution earth observation system.Under the great attention of the govern...Since the beginning of the twenty-first century,several countries have made great efforts to develop space remote sensing for building a high-resolution earth observation system.Under the great attention of the government and the guidance of the major scientific and technological project of the high-resolution earth observation system,China has made continuous breakthroughs and progress in high-resolution remote sensing imaging technology.The development of domestic high-resolution remote sensing satellites shows a vigorous trend,and consequently,a relatively stable and perfect high-resolution earth observation system has been formed.The development of high-resolution remote sensing satellites has greatly promoted and enriched modern mapping technologies and methods.In this paper,the development status,along with mapping modes and applications of China’s high-resolution remote sensing satellites are reviewed,and the development trend in high-resolution earth observation system for global and ground control-free mapping is discussed,providing a reference for the subsequent development of high-resolution remote sensing satellites in China.展开更多
In order to meet the requirements of high-precision vehicle positioning in complex scenes,an observation noise adaptive robust GNSS/MIMU tight fusion model based on the gain matrix is proposed considering static zero ...In order to meet the requirements of high-precision vehicle positioning in complex scenes,an observation noise adaptive robust GNSS/MIMU tight fusion model based on the gain matrix is proposed considering static zero speed,non-integrity,attitude,and odometer constraint models.In this model,the robust equivalent gain matrix is constructed by the IGG-Ⅲmethod to weaken the influence of gross error,and the on-line adaptive update of observation noise matrix is carried out according to the change of actual observation environment,so as to improve the solution performance of filtering system and realize high-precision position,attitude and velocity measurement when GNSS signal is unlocked.A real test on a road over 600 km demonstrates that,in about 100 km shaded environment,the fixed rate of GNSS ambiguity resolution in the shaded road is 10%higher than that of GNSS only ambiguity resolution.For all the test,the positioning accuracy can reach the centimeter level in an open environment,better than 0.6 m in the tree shaded environment,better than 1.5 m in the three-dimensional traffic environment,and can still maintain a positioning accuracy of 0.1 m within 10 s when the satellite is unlocked in the tunnel scene.The proposal and verification of the algorithm model show that low-cost MIMU equipment can still achieve high-precision positioning when there are scene feature constraints,which can meet the problem of high-precision vehicle navigation and location in the urban complex environment.展开更多
Subway tunnels often suffer from surface pathologies such as cracks,corrosion,fractures,peeling,water and sand infiltration,and sudden hazards caused by foreign object intrusions.Installing a mobile visual pathology s...Subway tunnels often suffer from surface pathologies such as cracks,corrosion,fractures,peeling,water and sand infiltration,and sudden hazards caused by foreign object intrusions.Installing a mobile visual pathology sensing system at the front end of operating trains is a critical measure to ensure subway safety.Taking leakage as the typical pathology,a tunnel pathology automatic visual detection method based on Deeplabv3+(ASTPDS)was proposed to achieve automatic and high-precision detection and pixel-level morphology extraction of pathologies.Compared with similar methods,this approach showed significant advantages and achieved a detection accuracy of 93.12%,surpassing FCN and U-Net.Moreover,it also exceeded the recall rates for detecting leaks of FCN and U-Net by 8.33%and 8.19%,respectively.展开更多
The rapid growth of the global population has resulted in a continuous increase in cropland intensity and a shortening of the fallow period as part of the cropland rotation cycle.Yet,there is a lack of systematic know...The rapid growth of the global population has resulted in a continuous increase in cropland intensity and a shortening of the fallow period as part of the cropland rotation cycle.Yet,there is a lack of systematic knowledge on the extent of fallow lands,particularly in complex landscapes,such as the mountainous regions of China.To fill this knowledge gap,taking Yuanyang County(YYC),Yunnan Province,China,as a case study,we tested a method to identify the spatial-temporal distribution of fallow land by mapping cropland with Landsat data.The overall accuracy of land cover classification,including cropland,ranged between 90.1%and 95.8%from 1998 to 2019.The average accuracy of fallow plots was 75.7%from 2001 to 2019.The annual fallow rate varied between 8.3%and 54.3%,with an average of 20.7%.Kernel density estimated with the probability density function showed that fallow varied between 5 and 13 blocks per km2,gradually decreasing from the central area to the periphery.Increasing elevation,the low value of regional domestic products,and the increased distance to rural settlements were closely related to the higher proportions of fallow land.The approach presented here can be applied to map fallow land in other regions.展开更多
Urban Functional Zone(UFZ)identification is vital for urban planning,renewal,and development.Point of Interest(POI),as one of the most popular data in UFZ studies,is transformed into a geo-corpus under specific sampli...Urban Functional Zone(UFZ)identification is vital for urban planning,renewal,and development.Point of Interest(POI),as one of the most popular data in UFZ studies,is transformed into a geo-corpus under specific sampling strategies,which can be used with Natural Language Processing(NLP)technology to extract geo-semantic features and identify UFZs.However,existing studies only capture a single spatial distribution pattern of POIs,while ignoring the other spatial distribution information.In this paper,we developed an integrated geo-corpus construction approach to capture multi-spatial distribution patterns of POIs that were represented by different modal POI embeddings.Subsequently,random forest model was leveraged to classify UFZs based on those embeddings.A set of combination experiments were designed for performance validation.The results show that our proposed method can effectively identify UFZs with an accuracy of 72.9%,with an improvement of 8.5%compared to the baseline methods.The outcome of this study will help urban planners to better understand UFZs through investigating the integrated spatial distribution patterns of POIs embedded in UFZs.展开更多
An Extended Kalman Filter（EKF） is commonly used to fuse raw Global Navigation Satellite System（GNSS） measurements and Inertial Navigation System（INS） derived measurements. However, the Conventional EKF（CEKF） s...An Extended Kalman Filter（EKF） is commonly used to fuse raw Global Navigation Satellite System（GNSS） measurements and Inertial Navigation System（INS） derived measurements. However, the Conventional EKF（CEKF） suffers the problem for which the uncertainty of the statistical properties to dynamic and measurement models will degrade the performance.In this research, an Adaptive Interacting Multiple Model（AIMM） filter is developed to enhance performance. The soft-switching property of Interacting Multiple Model（IMM） algorithm allows the adaptation between two levels of process noise, namely lower and upper bounds of the process noise. In particular, the Sage adaptive filtering is applied to adapt the measurement covariance on line. In addition, a classified measurement update strategy is utilized, which updates the pseudorange and Doppler observations sequentially. A field experiment was conducted to validate the proposed algorithm, the pseudorange and Doppler observations from Global Positioning System（GPS） and Bei Dou Navigation Satellite System（BDS） were post-processed in differential mode.The results indicate that decimeter-level positioning accuracy is achievable with AIMM for GPS/INS and GPS/BDS/INS configurations, and the position accuracy is improved by 35.8%, 34.3% and 33.9% for north, east and height components, respectively, compared to the CEKF counterpartfor GPS/BDS/INS. Degraded performance for BDS/INS is obtained due to the lower precision of BDS pseudorange observations.展开更多
China’s urbanization has attracted a lot of attention due to its unprecedented pace and intensity in terms of land,population,and economic impact.However,due to the lack of consistent and harmonized data,little is kn...China’s urbanization has attracted a lot of attention due to its unprecedented pace and intensity in terms of land,population,and economic impact.However,due to the lack of consistent and harmonized data,little is known about the patterns and dynamics of the interaction between these different aspects over the past few decades.Along with the implementation of the 2030 Agenda for Sustainable Development,a standardized dataset for assessing the sustainability of urbanization in China is needed.In this paper,we used remote sensing data from multiple sources(time-series of Landsat and Sentinel images)to map the impervious surface area(ISA)at five-year intervals from 1990 to 2015 and then converted the results into a standardized dataset of the built-up area for 433 Chinese cities with 300,000 inhabitants or more.This dataset was produced following the well-established rules adopted by the United Nations(UN).Validation of the ISA maps in urban areas based on the visual interpretation of Google Earth images showed that the average overall accuracy(OA),producer’s accuracy(PA)and user’s accuracy(UA)were 91.24%,92.58%and 89.65%,respec-tively.Comparisons with other existing urban built-up area datasets derived from the National Bureau of Statistics of China,the World Bank and UN-habitat indicated that our dataset,namely the stan-dardized urban built-up area dataset for China(SUBAD-China),provides an improved description of the spatiotemporal character-istics of the urbanization process and is especially applicable to a combined analysis of the spatial and socio-economic domains in urban areas.Potential applications of this dataset include combin-ing the spatial expansion and demographic information provided by UN to calculate sustainable development indicators such as SDG 11.3.1.The dataset could also be used in other multidimensional syntheses related to the study of urbanization in China.展开更多
Urban Functional Zones(UFZs)can be identified by measuring the spatiotemporal patterns of activities that occur within them.Geosocial media data possesses abundant spatial and temporal information for activity mining....Urban Functional Zones(UFZs)can be identified by measuring the spatiotemporal patterns of activities that occur within them.Geosocial media data possesses abundant spatial and temporal information for activity mining.Identifying UFZs from geosocial media data aids urban planning,infrastructure,resource allocation,and transportation modernization in the complex urban system.In this work,we proposed an integrated approach by combining the spatiotemporal clustering method with a machine learning classifier.The spatiotemporal clustering method was used to mine the spatiotemporal patterns of activities,of which the distinctive features were extracted as inputs into a machine learning classifier for UFZ identification.The results show that more than 80%of the UFZs can be correctly identified by our proposed method.It reveals that this work serves as a functional groundwork for future studies,facilitating the understanding of urban systems as well as promoting sustainable urban development.展开更多
The local climate zone(LCZ)scheme has been widely utilized in regional climate modeling,urban planning,and thermal comfort investigations.However,existing LCz classification methods face challenges in characterizing c...The local climate zone(LCZ)scheme has been widely utilized in regional climate modeling,urban planning,and thermal comfort investigations.However,existing LCz classification methods face challenges in characterizing complex urban structures and human activities involving local climatic environments.In this study,we proposed a novel LCZ mapping method that fully uses space-borne multi-view and diurnal observations,i.e.daytime Ziyuan-3 stereo imageries(2.1 m)and Luojia-1 nighttime light(NTL)data(130 m).Firstly,we performed land cover classification using multiple machine learning methods(i.e.random forest(RF)and XGBoost algorithms)and various features(i.e.spectral,textural,multi-view features,3D urban structure parameters(USPs),and NTL).In addition,we developed a set of new cumulative elevation indexes to improve building roughness assessments.The indexes can estimate building roughness directly from fused point clouds generated by both along-and across-track modes.Finally,based on the land cover and building roughness results,we extracted 2D and 3D USPs for different land covers and used multi-classifiers to perform LCZ mapping.The results for Beijing,China,show that our method yielded satisfactory accuracy for LCZ mapping,with an overall accuracy(OA)of 90.46%.The overall accuracy of land cover classification using 3D USPs generated from both along-and across-track modes increased by 4.66%,compared to that of using the single along-track mode.Additionally,the OA value of LCZ mapping using 2D and 3D USPs(88.18%)achieved a better result than using only 2D USPs(83.83%).The use of NTL data increased the classification accuracy of LCZs E(bare rock or paved)and F(bare soil or sand)by 6.54%and 3.94%,respectively.The refined LCZ classification achieved through this study will not only contribute to more accurate regional climate modeling but also provide valuable guidance for urban planning initiatives aimed at enhancing thermal comfort and overall livabillity in urban areas.Ultimately,this study paves the way for more comprehensive and effective strategies in addressing the challenges posed by urban microclimates.展开更多
The sudden outbreak of the Coronavirus disease(COVID-19)swept across the world in early 2020,triggering the lockdowns of several billion people across many countries,including China,Spain,India,the U.K.,Italy,France,G...The sudden outbreak of the Coronavirus disease(COVID-19)swept across the world in early 2020,triggering the lockdowns of several billion people across many countries,including China,Spain,India,the U.K.,Italy,France,Germany,Brazil,Russia,and the U.S.The transmission of the virus accelerated rapidly with the most confirmed cases in the U.S.,India,Russia,and Brazil.In response to this national and global emergency,the NSF Spatiotemporal Innovation Center brought together a taskforce of international researchers and assembled implementation strategies to rapidly respond to this crisis,for supporting research,saving lives,and protecting the health of global citizens.This perspective paper presents our collective view on the global health emergency and our effort in collecting,analyzing,and sharing relevant data on global policy and government responses,human mobility,environmental impact,socioeconomical impact;in developing research capabilities and mitigation measures with global scientists,promoting collaborative research on outbreak dynamics,and reflecting on the dynamic responses from human societies.展开更多
This article presents the virtual restoration of the Nine Eyes Watchtower,a signifcant cultural heritage site along the Great Wall.By applying the Seville Charter and digital technology,a detailed virtual restoration ...This article presents the virtual restoration of the Nine Eyes Watchtower,a signifcant cultural heritage site along the Great Wall.By applying the Seville Charter and digital technology,a detailed virtual restoration workfow is developed.The methodology involves acquiring data from multiple sources,including physical evidence,historical data,and comparative data.Advanced survey technologies,architectural knowledge,historical research,and computer modelling techniques are integrated to accurately capture the architectural and historical signifcance of the Nine Eyes Watchtower.The virtual restoration process follows a systematic approach,combining evidence interpretation and explicit deduction steps.The main outcome is a comprehensive virtual restoration model that accurately represents the architectural features and historical context of the Nine Eyes Watchtower.The virtual scene includes environmental elements,with potential for immersive exploration.By bridging the gap between interpretation and deduction,this study advances the scientifc understanding and presentation of virtual restorations.The project contributes to ongoing research,education,and appreciation of the Great Wall’s cultural legacy,ensuring its continued relevance for future generations.展开更多
Being a kind of non-Euclidean data,spatiotemporal graph data exists everywhere from trafficflow,air quality index to crime case,etc.Unlike the raster data,the irregular and disordered characteristics of spatiotemporal...Being a kind of non-Euclidean data,spatiotemporal graph data exists everywhere from trafficflow,air quality index to crime case,etc.Unlike the raster data,the irregular and disordered characteristics of spatiotemporal graph data have attracted the research interest of scholars,with the prediction of spatiotemporal graph data being one of the research hot spots.The emergence of spatiotemporal graph neural networks(ST-GNNs)provides a new insight for solving the problem of obtaining spatial correlation for spatiotemporal graph data prediction while achieving state-of-the-art performance.In this paper,comprehensive survey of research on ST-GNNs prediction domain isa presented,where the background of ST-GNNs is introduced before the computational paradigm of ST-GNN is thoroughly reviewed.From the perspective of model construction,59 well-known models in recent years are classified and discussed.Some of these models are further analyzed in terms of performance and efficiency.Subsequently,the categories and applicationfields of spatiotemporal graph data are summarized,providing a clear idea of technology selection for different applications.Finally,the evolution history and future direction of ST-GNNs are also summarized,to facilitate future researchers to timely understand the current state of prediction research by ST-GNNs.展开更多
With the development of satellite remote sensing technology,image classification task,as the basis of remote sensing data interpretation,has received wide attention to improving accuracy and robustness.At the same tim...With the development of satellite remote sensing technology,image classification task,as the basis of remote sensing data interpretation,has received wide attention to improving accuracy and robustness.At the same time,in-depth learning technology has been widely used in remote sensing and has a far-reaching impact.Since the existing image classification methods ignore the feature that the general image semantics are the same as the semantics of a single pixel,this paper presents an algorithm that uses the semantics of an image to achieve high-precision image classification.Based on the idea of partial substitution for global,this algorithm designs a split result voting mechanism and builds a Vgg-Vote network model.This mechanism votes on the semantically segmented result of an image and uses the maximum filtering function to select the category containing the most significant number of pixels as the prediction category of the image.Experiments on UC Merced Land-User complete datasets and five types of incomplete datasets with varying degrees of interference,including noise,data occlusion and loss,show that the Vote mechanism dramatically improves the classification accuracy,robustness and anti-jamming capability of Vgg-Vote.展开更多
基金This research was funded by the National Natural Science Foundation of China[grant number 41971350 and 41571437]Beijing Advanced Innovation Centre for Future Urban Design Project[grant number UDC2019031724]+4 种基金Teacher Support Program for Pyramid Talent Training Project of Beijing University of Civil Engineering and Architecture[grant number JDJQ20200307]State Key Laboratory of Geo-Information Engineering[grant number SKLGIE2019-Z-3-1]Open Research Fund Program of LIESMARS[grant number 19E01]National Key Research and Development Program of China[grant number 2019YFC1520100]The Fundamental Research Funds for Beijing University of Civil Engineering and Architecture[grant number X18050].
文摘The measurement accuracy of the Mobile Mapping System (MMS) is the main problem, which restricts its development and application, so how to calibrate the MMS to improve its measure-ment accuracy has always been a research hotspot in the industry. This paper proposes a position and attitude calibration method with error correction based on the combination of the feature point and feature surface. First, the initial value of the spatial position relation-ship between each sensor of MMS is obtained by close-range photogrammetry. Second, the optimal solution for error correction is calculated by feature points in global coordinates jointly measured with International GNSS Service (IGS) stations. Then, the final transformation para-meters are solved by combining the initial values obtained originally, thereby realizing the rapid calibration of the MMS. Finally, it analyzed the RMSE of MMS point cloud after calibration, and the results demonstrate the feasibility of the calibration approach proposed by this method. Under the condition of a single measurement sensor accuracy is low, the plane and elevation absolute accuracy of the point cloud after calibration can reach 0.043 m and 0.072 m, respectively, and the relative accuracy is smaller than 0.02 m. It meets the precision require-ments of data acquisition for MMS. It is of great significance for promoting the development of MMS technology and the application of some novel techniques in the future, such as auton-omous driving, digital twin city, urban brain et al.
基金founded by National Key R&D Program of China (No.2021YFB2601200)National Natural Science Foundation of China (No.42171416)Teacher Support Program for Pyramid Talent Training Project of Beijing University of Civil Engineering and Architecture (No.JDJQ20200307).
文摘In light of the limited efficacy of conventional methods for identifying pavement cracks and the absence of comprehensive depth and location data in two-dimensional photographs,this study presents an intelligent strategy for extracting road cracks.This methodology involves the integration of laser point cloud data obtained from a vehicle-mounted system and a panoramic sequence of images.The study employs a vehicle-mounted LiDAR measurement system to acquire laser point cloud and panoramic sequence image data simultaneously.A convolutional neural network is utilized to extract cracks from the panoramic sequence image.The extracted sequence image is then aligned with the laser point cloud,enabling the assignment of RGB information to the vehicle-mounted three dimensional(3D)point cloud and location information to the two dimensional(2D)panoramic image.Additionally,a threshold value is set based on the crack elevation change to extract the aligned roadway point cloud.The three-dimensional data pertaining to the cracks can be acquired.The experimental findings demonstrate that the use of convolutional neural networks has yielded noteworthy outcomes in the extraction of road cracks.The utilization of point cloud and image alignment techniques enables the extraction of precise location data pertaining to road cracks.This approach exhibits superior accuracy when compared to conventional methods.Moreover,it facilitates rapid and accurate identification and localization of road cracks,thereby playing a crucial role in ensuring road maintenance and traffic safety.Consequently,this technique finds extensive application in the domains of intelligent transportation and urbanization development.The technology exhibits significant promise for use in the domains of intelligent transportation and city development.
文摘The characteristics of drought in Xinjiang Uygur Autonomous Region(Xinjiang),China have changed due to changes in the spatiotemporal patterns of temperature and precipitation,however,the effects of temperature and precipitation—the two most important factors influencing drought—have not yet been thoroughly explored in this region.In this study,we first calculated the standard precipitation evapotranspiration index(SPEI)in Xinjiang from 1980 to 2020 based on the monthly precipitation and monthly average temperature.Then the spatiotemporal characteristics of temperature,precipitation,and drought in Xinjiang from 1980 to 2020 were analyzed using the Theil-Sen median trend analysis method and Mann-Kendall test.A series of SPEI-based scenario-setting experiments by combining the observed and detrended climatic factors were utilized to quantify the effects of individual climatic factor(i.e.,temperature and precipitation).The results revealed that both temperature and precipitation had experienced increasing trends at most meteorological stations in Xinjiang from 1980 to 2020,especially the spring temperature and winter precipitation.Due to the influence of temperature,trends of intensifying drought have been observed at spring,summer,autumn,and annual scales.In addition,the drought trends in southern Xinjiang were more notable than those in northern Xinjiang.From 1980 to 2020,temperature trends exacerbated drought trends,but precipitation trends alleviated drought trends in Xinjiang.Most meteorological stations in Xinjiang exhibited temperature-dominated drought trend except in winter;in winter,most stations exhibited precipitation-dominated wetting trend.The findings of this study highlight the importance of the impact of temperature on drought in Xinjiang and deepen the understanding of the factors influencing drought.
基金The National Key Research and Development Program of China(No.2016YFB0500304)The Fund of Beijing Key Laboratory of Urban Spatial Information Engineering(No.2017212)The Advanced Project of Urban Design Big Data Acquisition and Processing(30059917306)
文摘Dense matching of remote sensing images is a key step in the generation of accurate digital surface models.The semi-global matching algorithm comprehensively considers the advantages and disadvantages of local matching and global matching in terms of matching effect and computational efficiency,so it is widely used in close-range,aerial and satellite image matching.Based on the analysis of the original semi-global matching algorithm,this paper proposes a semi-global high-resolution remote sensing image that takes into account the geometric constraints of the connection points and the image texture information based on the large amount of high-resolution remote sensing image data and the characteristics of clear image texture.The method includes 4 parts:(1)Precise orientation.Aiming at the system error in the image orientation model,the system error of the rational function model is compensated by the geometric constraint relationship of the connecting points between the images,and the sub-pixel positioning accuracy is obtained;(2)Epipolar image generation.After the original image is divided into blocks,the epipolar image is generated based on the projection trajectory method;(3)Image dense matching.In order to reduce the size of the cost space and calculation time,the image is pyramided and then semi-globally matched layer by layer.In the matching process,the disparity map expansion and erosion algorithm that takes into account the image texture information is introduced to restrict the disparity search range and better retain the edge characteristics of the ground objects;(4)Generate DSM.In order to test the matching effect,the weighted median filter algorithm is used to filter the disparity map,and the DSM is obtained based on the principle of forward intersection.Finally,the paper uses the matching results of WordView3 and Ziyuan No.3 stereo image to verify the effectiveness of this method.
文摘The rapid economic growth,urbanization,and industrialization have led to a scarcity of land resources in coastal areas,exacerbating the conflict between humans and the environment.In order to promote economic development,attention has turned to the sea,and various coastal engineering projects have been undertaken,sparking a wave of land reclamation.However,while these efforts bring economic and social benefits,they also have implications for ecological relationships.To respond to and plan for changes in the coastline and land cover in a timely manner,this paper proposes and constructs a GIS system that integrates remote sensing image recognition models.The system combines geographic information system development technology with image recognition technology,streamlining the processing and identification of image data.This approach is particularly advantageous for marine management departments in their long-term monitoring and dynamic management of coastal lines,ensuring a more effective and efficient response.
基金Graduate Student Innovation Projects of Beijing University of Civil Engineering and Architecture(No.PG2024121)。
文摘Underwater images are inherently degraded by color distortion,contrast reduction,and uneven brightness,primarily due to light absorption and scattering in water.To mitigate these challenges,a novel enhancement approach is proposed,integrating Local Adaptive Color Correction(LACC)with contrast enhancement based on adaptive Rayleigh distribution stretching and CLAHE(LACC-RCE).Conventional color correction methods predominantly employ global adjustment strategies,which are often inadequate for handling spatially varying color distortions.In contrast,the proposed LACC method incorporates local color analysis,tone-weighted control,and spatially adaptive adjustments,allowing for region-specific color correction.This approach effectively enhances color fidelity and perceptual naturalness,addressing the limitations of global correction techniques.For contrast enhancement,the proposed method leverages the global mapping characteristics of the Rayleigh distribution to improve overall contrast,while CLAHE is employed to adaptively enhance local regions.A weighted fusion strategy is then applied to synthesize high-quality underwater images.Experimental results indicate that LACC-RCE surpasses conventional methods in color restoration,contrast optimization,and detail preservation,thereby enhancing the visual quality of underwater images.This improvement facilitates more reliable inputs for underwater object detection and recognition tasks.
基金This work is supported by the National Natural Science Foundation of China[grant numbers 91738302 and 91838303]the National Science Fund for Distinguished Young Scholars[grant number 61825103]Thanks for the support of China Centre for Resources Satellite Data and Application(CRESDA).
文摘Since the beginning of the twenty-first century,several countries have made great efforts to develop space remote sensing for building a high-resolution earth observation system.Under the great attention of the government and the guidance of the major scientific and technological project of the high-resolution earth observation system,China has made continuous breakthroughs and progress in high-resolution remote sensing imaging technology.The development of domestic high-resolution remote sensing satellites shows a vigorous trend,and consequently,a relatively stable and perfect high-resolution earth observation system has been formed.The development of high-resolution remote sensing satellites has greatly promoted and enriched modern mapping technologies and methods.In this paper,the development status,along with mapping modes and applications of China’s high-resolution remote sensing satellites are reviewed,and the development trend in high-resolution earth observation system for global and ground control-free mapping is discussed,providing a reference for the subsequent development of high-resolution remote sensing satellites in China.
基金Youth Program of National Natural Science Foundation of China (No. 41904029)Scientific Research Project of Beijing Educational Committee (No. KM202010016009)。
文摘In order to meet the requirements of high-precision vehicle positioning in complex scenes,an observation noise adaptive robust GNSS/MIMU tight fusion model based on the gain matrix is proposed considering static zero speed,non-integrity,attitude,and odometer constraint models.In this model,the robust equivalent gain matrix is constructed by the IGG-Ⅲmethod to weaken the influence of gross error,and the on-line adaptive update of observation noise matrix is carried out according to the change of actual observation environment,so as to improve the solution performance of filtering system and realize high-precision position,attitude and velocity measurement when GNSS signal is unlocked.A real test on a road over 600 km demonstrates that,in about 100 km shaded environment,the fixed rate of GNSS ambiguity resolution in the shaded road is 10%higher than that of GNSS only ambiguity resolution.For all the test,the positioning accuracy can reach the centimeter level in an open environment,better than 0.6 m in the tree shaded environment,better than 1.5 m in the three-dimensional traffic environment,and can still maintain a positioning accuracy of 0.1 m within 10 s when the satellite is unlocked in the tunnel scene.The proposal and verification of the algorithm model show that low-cost MIMU equipment can still achieve high-precision positioning when there are scene feature constraints,which can meet the problem of high-precision vehicle navigation and location in the urban complex environment.
文摘Subway tunnels often suffer from surface pathologies such as cracks,corrosion,fractures,peeling,water and sand infiltration,and sudden hazards caused by foreign object intrusions.Installing a mobile visual pathology sensing system at the front end of operating trains is a critical measure to ensure subway safety.Taking leakage as the typical pathology,a tunnel pathology automatic visual detection method based on Deeplabv3+(ASTPDS)was proposed to achieve automatic and high-precision detection and pixel-level morphology extraction of pathologies.Compared with similar methods,this approach showed significant advantages and achieved a detection accuracy of 93.12%,surpassing FCN and U-Net.Moreover,it also exceeded the recall rates for detecting leaks of FCN and U-Net by 8.33%and 8.19%,respectively.
基金supported by the National Natural Science Foundation of China[grant number 42071233]the Strategic Priority Research Program of Chinese Academy of Sciences[grant number XDA20040201]and the Second Tibetan Plateau Scientific Expedition and Research Program[grant number 2019QZKK0603].
文摘The rapid growth of the global population has resulted in a continuous increase in cropland intensity and a shortening of the fallow period as part of the cropland rotation cycle.Yet,there is a lack of systematic knowledge on the extent of fallow lands,particularly in complex landscapes,such as the mountainous regions of China.To fill this knowledge gap,taking Yuanyang County(YYC),Yunnan Province,China,as a case study,we tested a method to identify the spatial-temporal distribution of fallow land by mapping cropland with Landsat data.The overall accuracy of land cover classification,including cropland,ranged between 90.1%and 95.8%from 1998 to 2019.The average accuracy of fallow plots was 75.7%from 2001 to 2019.The annual fallow rate varied between 8.3%and 54.3%,with an average of 20.7%.Kernel density estimated with the probability density function showed that fallow varied between 5 and 13 blocks per km2,gradually decreasing from the central area to the periphery.Increasing elevation,the low value of regional domestic products,and the increased distance to rural settlements were closely related to the higher proportions of fallow land.The approach presented here can be applied to map fallow land in other regions.
基金supported by the China Scholarship Council[03998521001]the Beijing Categorized Development Quota Project[03082722002]+2 种基金the Beijing University of Civil Engineering and Architecture Young Scholars’Research Ability Improvement Program[X21018]the National Natural Science Foundation of China[41930650]the Natural Sciences and Engineering Research Council of Canada[RGPIN-2017-05950].
文摘Urban Functional Zone(UFZ)identification is vital for urban planning,renewal,and development.Point of Interest(POI),as one of the most popular data in UFZ studies,is transformed into a geo-corpus under specific sampling strategies,which can be used with Natural Language Processing(NLP)technology to extract geo-semantic features and identify UFZs.However,existing studies only capture a single spatial distribution pattern of POIs,while ignoring the other spatial distribution information.In this paper,we developed an integrated geo-corpus construction approach to capture multi-spatial distribution patterns of POIs that were represented by different modal POI embeddings.Subsequently,random forest model was leveraged to classify UFZs based on those embeddings.A set of combination experiments were designed for performance validation.The results show that our proposed method can effectively identify UFZs with an accuracy of 72.9%,with an improvement of 8.5%compared to the baseline methods.The outcome of this study will help urban planners to better understand UFZs through investigating the integrated spatial distribution patterns of POIs embedded in UFZs.
基金co-supported by the National Key Research and Development Program of China(No.2016YFC0803103)Beijing Advanced Innovation Center for Future Urban Design(No.UDC2016050100)Beijing Postdoctoral Research Foundation
文摘An Extended Kalman Filter（EKF） is commonly used to fuse raw Global Navigation Satellite System（GNSS） measurements and Inertial Navigation System（INS） derived measurements. However, the Conventional EKF（CEKF） suffers the problem for which the uncertainty of the statistical properties to dynamic and measurement models will degrade the performance.In this research, an Adaptive Interacting Multiple Model（AIMM） filter is developed to enhance performance. The soft-switching property of Interacting Multiple Model（IMM） algorithm allows the adaptation between two levels of process noise, namely lower and upper bounds of the process noise. In particular, the Sage adaptive filtering is applied to adapt the measurement covariance on line. In addition, a classified measurement update strategy is utilized, which updates the pseudorange and Doppler observations sequentially. A field experiment was conducted to validate the proposed algorithm, the pseudorange and Doppler observations from Global Positioning System（GPS） and Bei Dou Navigation Satellite System（BDS） were post-processed in differential mode.The results indicate that decimeter-level positioning accuracy is achievable with AIMM for GPS/INS and GPS/BDS/INS configurations, and the position accuracy is improved by 35.8%, 34.3% and 33.9% for north, east and height components, respectively, compared to the CEKF counterpartfor GPS/BDS/INS. Degraded performance for BDS/INS is obtained due to the lower precision of BDS pseudorange observations.
基金funded by the Strategic Priority Research Program of the Chinese Academy of Sciences[XDA19030104,XDA19090121]the Key Research and Development Projects of Hainan Province[ZDYF2019008].
文摘China’s urbanization has attracted a lot of attention due to its unprecedented pace and intensity in terms of land,population,and economic impact.However,due to the lack of consistent and harmonized data,little is known about the patterns and dynamics of the interaction between these different aspects over the past few decades.Along with the implementation of the 2030 Agenda for Sustainable Development,a standardized dataset for assessing the sustainability of urbanization in China is needed.In this paper,we used remote sensing data from multiple sources(time-series of Landsat and Sentinel images)to map the impervious surface area(ISA)at five-year intervals from 1990 to 2015 and then converted the results into a standardized dataset of the built-up area for 433 Chinese cities with 300,000 inhabitants or more.This dataset was produced following the well-established rules adopted by the United Nations(UN).Validation of the ISA maps in urban areas based on the visual interpretation of Google Earth images showed that the average overall accuracy(OA),producer’s accuracy(PA)and user’s accuracy(UA)were 91.24%,92.58%and 89.65%,respec-tively.Comparisons with other existing urban built-up area datasets derived from the National Bureau of Statistics of China,the World Bank and UN-habitat indicated that our dataset,namely the stan-dardized urban built-up area dataset for China(SUBAD-China),provides an improved description of the spatiotemporal character-istics of the urbanization process and is especially applicable to a combined analysis of the spatial and socio-economic domains in urban areas.Potential applications of this dataset include combin-ing the spatial expansion and demographic information provided by UN to calculate sustainable development indicators such as SDG 11.3.1.The dataset could also be used in other multidimensional syntheses related to the study of urbanization in China.
基金supported by the Natural Sciences and Engineering Research Council of Canada[RGPIN-2017-05950]China Scholarship Council[03998521001]+1 种基金Beijing Categorized Development Quota Project[03082722002]Beijing University of Civil Engineering and Architecture Young Scholars’Research Ability Improvement Program[X21018]。
文摘Urban Functional Zones(UFZs)can be identified by measuring the spatiotemporal patterns of activities that occur within them.Geosocial media data possesses abundant spatial and temporal information for activity mining.Identifying UFZs from geosocial media data aids urban planning,infrastructure,resource allocation,and transportation modernization in the complex urban system.In this work,we proposed an integrated approach by combining the spatiotemporal clustering method with a machine learning classifier.The spatiotemporal clustering method was used to mine the spatiotemporal patterns of activities,of which the distinctive features were extracted as inputs into a machine learning classifier for UFZ identification.The results show that more than 80%of the UFZs can be correctly identified by our proposed method.It reveals that this work serves as a functional groundwork for future studies,facilitating the understanding of urban systems as well as promoting sustainable urban development.
基金supported by the National Natural Science Foundation of China[grant number:41930650]the Scientific Research Project of Beijing Municipal Education Commission[grant number:KM202110016004]the Beijing Key Laboratory of Urban Spatial Information Engineering[grant number 20220111].
文摘The local climate zone(LCZ)scheme has been widely utilized in regional climate modeling,urban planning,and thermal comfort investigations.However,existing LCz classification methods face challenges in characterizing complex urban structures and human activities involving local climatic environments.In this study,we proposed a novel LCZ mapping method that fully uses space-borne multi-view and diurnal observations,i.e.daytime Ziyuan-3 stereo imageries(2.1 m)and Luojia-1 nighttime light(NTL)data(130 m).Firstly,we performed land cover classification using multiple machine learning methods(i.e.random forest(RF)and XGBoost algorithms)and various features(i.e.spectral,textural,multi-view features,3D urban structure parameters(USPs),and NTL).In addition,we developed a set of new cumulative elevation indexes to improve building roughness assessments.The indexes can estimate building roughness directly from fused point clouds generated by both along-and across-track modes.Finally,based on the land cover and building roughness results,we extracted 2D and 3D USPs for different land covers and used multi-classifiers to perform LCZ mapping.The results for Beijing,China,show that our method yielded satisfactory accuracy for LCZ mapping,with an overall accuracy(OA)of 90.46%.The overall accuracy of land cover classification using 3D USPs generated from both along-and across-track modes increased by 4.66%,compared to that of using the single along-track mode.Additionally,the OA value of LCZ mapping using 2D and 3D USPs(88.18%)achieved a better result than using only 2D USPs(83.83%).The use of NTL data increased the classification accuracy of LCZs E(bare rock or paved)and F(bare soil or sand)by 6.54%and 3.94%,respectively.The refined LCZ classification achieved through this study will not only contribute to more accurate regional climate modeling but also provide valuable guidance for urban planning initiatives aimed at enhancing thermal comfort and overall livabillity in urban areas.Ultimately,this study paves the way for more comprehensive and effective strategies in addressing the challenges posed by urban microclimates.
基金NSF(1841520,1835507,1832465,2028791 and 2025783)the NSF Spatiotemporal Innovation Center members.
文摘The sudden outbreak of the Coronavirus disease(COVID-19)swept across the world in early 2020,triggering the lockdowns of several billion people across many countries,including China,Spain,India,the U.K.,Italy,France,Germany,Brazil,Russia,and the U.S.The transmission of the virus accelerated rapidly with the most confirmed cases in the U.S.,India,Russia,and Brazil.In response to this national and global emergency,the NSF Spatiotemporal Innovation Center brought together a taskforce of international researchers and assembled implementation strategies to rapidly respond to this crisis,for supporting research,saving lives,and protecting the health of global citizens.This perspective paper presents our collective view on the global health emergency and our effort in collecting,analyzing,and sharing relevant data on global policy and government responses,human mobility,environmental impact,socioeconomical impact;in developing research capabilities and mitigation measures with global scientists,promoting collaborative research on outbreak dynamics,and reflecting on the dynamic responses from human societies.
基金funded by the following funds in terms of research,analysis,and writing,which are:2024 Scientifc Research Project of Colleges and universities in Hebei Province(No.QN2024044)Research on immersive multimedia multidimensional interaction and presentation technology(No.2022YFF0902402).
文摘This article presents the virtual restoration of the Nine Eyes Watchtower,a signifcant cultural heritage site along the Great Wall.By applying the Seville Charter and digital technology,a detailed virtual restoration workfow is developed.The methodology involves acquiring data from multiple sources,including physical evidence,historical data,and comparative data.Advanced survey technologies,architectural knowledge,historical research,and computer modelling techniques are integrated to accurately capture the architectural and historical signifcance of the Nine Eyes Watchtower.The virtual restoration process follows a systematic approach,combining evidence interpretation and explicit deduction steps.The main outcome is a comprehensive virtual restoration model that accurately represents the architectural features and historical context of the Nine Eyes Watchtower.The virtual scene includes environmental elements,with potential for immersive exploration.By bridging the gap between interpretation and deduction,this study advances the scientifc understanding and presentation of virtual restorations.The project contributes to ongoing research,education,and appreciation of the Great Wall’s cultural legacy,ensuring its continued relevance for future generations.
基金supported by National Social Science Fund of China[grant number 21JCA004]Soft Science Research Project of Ministry of Housing and Urban-Rural Development of China[grant number R20200287]Open Research Fund of Key Laboratory of Digital Cartography and Land Information Application,Ministry of Natural Resources[grant number ZRZYBWD202102].
文摘Being a kind of non-Euclidean data,spatiotemporal graph data exists everywhere from trafficflow,air quality index to crime case,etc.Unlike the raster data,the irregular and disordered characteristics of spatiotemporal graph data have attracted the research interest of scholars,with the prediction of spatiotemporal graph data being one of the research hot spots.The emergence of spatiotemporal graph neural networks(ST-GNNs)provides a new insight for solving the problem of obtaining spatial correlation for spatiotemporal graph data prediction while achieving state-of-the-art performance.In this paper,comprehensive survey of research on ST-GNNs prediction domain isa presented,where the background of ST-GNNs is introduced before the computational paradigm of ST-GNN is thoroughly reviewed.From the perspective of model construction,59 well-known models in recent years are classified and discussed.Some of these models are further analyzed in terms of performance and efficiency.Subsequently,the categories and applicationfields of spatiotemporal graph data are summarized,providing a clear idea of technology selection for different applications.Finally,the evolution history and future direction of ST-GNNs are also summarized,to facilitate future researchers to timely understand the current state of prediction research by ST-GNNs.
基金supported by the Project of the Natural Science Foundation of Beijing[8172016]National Natural Science Foundation Project[41601409,41971350]+6 种基金Open Fund Project of State Key Laboratory of Surveying and Remote Sensing Information Engineering of Wuhan University[19E01]Open Fund Project of State Key Laboratory of Geographic Information Engineering[SKLGIE2019-Z-3-1]Special fund project for basic scientific research business expenses of municipal colleges and universities of Beijing Jianzhu University[X18063]National Key R&D Program Project[2018YFC0807806]Digital Mapping and Open Research Foundation Project of the Key Laboratory for Land Information Applications of the Ministry of Natural Resources[ZRZYBWD202102]the Soft Science Research Project of Ministry of Housing and Urban-Rural Development of China(R20200287)Major Decision Consulting Project of the Beijing Social Science Foundation(21JCA004)。
文摘With the development of satellite remote sensing technology,image classification task,as the basis of remote sensing data interpretation,has received wide attention to improving accuracy and robustness.At the same time,in-depth learning technology has been widely used in remote sensing and has a far-reaching impact.Since the existing image classification methods ignore the feature that the general image semantics are the same as the semantics of a single pixel,this paper presents an algorithm that uses the semantics of an image to achieve high-precision image classification.Based on the idea of partial substitution for global,this algorithm designs a split result voting mechanism and builds a Vgg-Vote network model.This mechanism votes on the semantically segmented result of an image and uses the maximum filtering function to select the category containing the most significant number of pixels as the prediction category of the image.Experiments on UC Merced Land-User complete datasets and five types of incomplete datasets with varying degrees of interference,including noise,data occlusion and loss,show that the Vote mechanism dramatically improves the classification accuracy,robustness and anti-jamming capability of Vgg-Vote.
