Indoor positioning with high accuracy plays an important role in different application scenar-ios.As a widely used mobile communication signal,the Long-Term Evolution(LTE)network can be well received in indoor and out...Indoor positioning with high accuracy plays an important role in different application scenar-ios.As a widely used mobile communication signal,the Long-Term Evolution(LTE)network can be well received in indoor and outdoor environments.This article studies a method of using different reference signals in the LTE downlink for carrier phase time of arrival(TOA)estimation.Specifically,a solution is proposed and a multipath tracking Software Defined Receiver(SDR)is developed for indoor positioning.With our SDR indoor positioning system,the pilot signals of the LTE signals are firstly obtained by the coarse synchronization and demodulation.Then,with the assistance of the pilot signals,the time delay acquisition,the multipath estimating delay lock loop(MEDLL)algorithm,and the multipath anomaly detection are sequentially carried out to obtain navigation observations of received signals.Furthermore,to compare the perfor-mance of different pilot signals,the Secondary Synchronous Signals(SSS)and Cell Reference Signals(CRS)are used as pilot signals for carrier phase-based TOA estimation,respectively.Finally,to quantify the accuracy of our multipath tracking SDR,indoor field tests are carried out in a conference environment,where an LTE base station is installed for commercial use.Our test results based on CRS show that,in the static test scenarios,the TOA accuracy measured by the 1-σerror interval is about 0.5 m,while in the mobile environment,the probability of range accuracy within 1.0 m is 95%.展开更多
To protect the environment,the discharged sewage’s quality must meet the state’s discharge standards.There are many water quality indicators,and the pH(Potential of Hydrogen)value is one of them.The natural water’s...To protect the environment,the discharged sewage’s quality must meet the state’s discharge standards.There are many water quality indicators,and the pH(Potential of Hydrogen)value is one of them.The natural water’s pH value is 6.0–8.5.The sewage treatment plant uses some data in the sewage treatment process to monitor and predict whether wastewater’s pH value will exceed the standard.This paper aims to study the deep learning prediction model of wastewater’s pH.Firstly,the research uses the random forest method to select the data features and then,based on the sliding window,convert the data set into a time series which is the input of the deep learning training model.Secondly,by analyzing and comparing relevant references,this paper believes that the CNN(Convolutional Neural Network)model is better at nonlinear data modeling and constructs a CNN model including the convolution and pooling layers.After alternating the combination of the convolutional layer and pooling layer,all features are integrated into a full-connected neural network.Thirdly,the number of input samples of the CNN model directly affects the prediction effect of the model.Therefore,this paper adopts the sliding window method to study the optimal size.Many experimental results show that the optimal prediction model can be obtained when alternating six convolutional layers and three pooling layers.The last full-connection layer contains two layers and 64 neurons per layer.The sliding window size selects as 12.Finally,the research has carried out data prediction based on the optimal CNN deep learning model.The predicted pH of the sewage is between 7.2 and 8.6 in this paper.The result is applied in the monitoring system platform of the“Intelligent operation and maintenance platform of the reclaimed water plant.”展开更多
Ground-level ozone(O_(3))is a primary air pollutant,which can greatly harm human health and ecosystems.At present,data fusion frameworks only provided ground-level O_(3)concentrations at coarse spatial(e.g.,10 km)or t...Ground-level ozone(O_(3))is a primary air pollutant,which can greatly harm human health and ecosystems.At present,data fusion frameworks only provided ground-level O_(3)concentrations at coarse spatial(e.g.,10 km)or temporal(e.g.,daily)resolutions.As photochemical pollution continues increasing over China in the last few years,a high-spatial–temporal-resolution product is required to enhance the comprehension of ground-level O_(3)formation mechanisms.To address this issue,our study creatively explores a brand-new framework for estimating hourly 2-km ground-level O_(3)concentrations across China(except Xinjiang and Tibet)using the brightness temperature at multiple thermal infrared bands.Considering the spatial heterogeneity of ground-level O_(3),a novel Self-adaptive Geospatially Local scheme based on Categorical boosting(SGLboost)is developed to train the estimation models.Validation results show that SGLboost performs well in the study area,with the R2 s/RMSEs of 0.85/19.041 lg/m^(3)and 0.72/25.112 lg/m^(3)for the space-based cross-validation(CV)(2017–2019)and historical space-based CV(2019),respectively.Meanwhile,SGLboost achieves distinctly better metrics than those of some widely used machine learning methods,such as e Xtreme Gradient boosting and Random Forest.Compared to recent related works over China,the performance of SGLboost is also more desired.Regarding the spatial distribution,the estimated results present continuous spatial patterns without a significantly partitioned boundary effect.In addition,accurate hourly and seasonal variations of ground-level O_(3)concentrations can be observed in the estimated results over the study area.It is believed that the hourly 2-km results estimated by SGLboost will help further understand the formation mechanisms of ground-level O_(3)in China.展开更多
Forest is one of the most challenging environments to be recorded in a three-dimensional(3D)digitized geometrical representation,because of the size and the complexity of the environment and the data-acquisition const...Forest is one of the most challenging environments to be recorded in a three-dimensional(3D)digitized geometrical representation,because of the size and the complexity of the environment and the data-acquisition constraints brought by on-site conditions.Previous studies have indicated that the data-acquisition pattern can have more influence on the registration results than other factors.In practice,the ideal short-baseline observations,i.e.,the dense collection mode,is rarely feasible,considering the low accessibility in forest environments and the commonly limited labor and time resources.The wide-baseline observations that cover a forest site using a few folds less observations than short-baseline observations,are therefore more preferable and commonly applied.Nevertheless,the wide-baseline approach is more challenging for data registration since it typically lacks the required sufficient overlaps between datasets.Until now,a robust automated registration solution that is independent of special hardware requirements has still been missing.That is,the registration accuracy is still far from the required level,and the information extractable from the merged point cloud using automated registration could not match that from the merged point cloud using manual registration.This paper proposes a discrete overlap search(DOS)method to find correspondences in the point clouds to solve the low-overlap problem in the wide-baseline point clouds.The proposed automatic method uses potential correspondences from both original data and selected feature points to reconstruct rough observation geometries without external knowledge and to retrieve precise registration parameters at data-level.An extensive experiment was carried out with 24 forest datasets of different conditions categorized in three difficulty levels.The performance of the proposed method was evaluated using various accuracy criteria,as well as based on data acquired from different hardware,platforms,viewing perspectives,and at different points of time.The proposed method achieved a 3D registration accuracy at a 0.50-cm level in all difficulty categories using static terrestrial acquisitions.In the terrestrial-aerial registration,data sets were collected from different sensors and at different points of time with scene changes,and a registration accuracy at the raw data geometric accuracy level was achieved.These results represent the highest automated registration accuracy and the strictest evaluation so far.The proposed method is applicable in multiple scenarios,such as 1)the global positioning of individual under-canopy observations,which is one of the main challenges in applying terrestrial observations lacking a global context,2)the fusion of point clouds acquired from terrestrial and aerial perspectives,which is required in order to achieve a complete forest observation,3)mobile mapping using a new stop-and-go approach,which solves the problems of lacking mobility and slow data collection in static terrestrial measurements as well as the data-quality issue in the continuous mobile approach.Furthermore,this work proposes a new error estimate that units all parameter-level errors into a single quantity and compensates for the downsides of the widely used parameter-and object-level error estimates;it also proposes a new deterministic point sets registration method as an alternative to the popular sampling methods.展开更多
Although the Convolutional Neural Network(CNN)has shown great potential for land cover classification,the frequently used single-scale convolution kernel limits the scope of informa-tion extraction.Therefore,we propos...Although the Convolutional Neural Network(CNN)has shown great potential for land cover classification,the frequently used single-scale convolution kernel limits the scope of informa-tion extraction.Therefore,we propose a Multi-Scale Fully Convolutional Network(MSFCN)with a multi-scale convolutional kernel as well as a Channel Attention Block(CAB)and a Global Pooling Module(GPM)in this paper to exploit discriminative representations from two-dimensional(2D)satellite images.Meanwhile,to explore the ability of the proposed MSFCN for spatio-temporal images,we expand our MSFCN to three-dimension using three-dimensional(3D)CNN,capable of harnessing each land cover category’s time series interac-tion from the reshaped spatio-temporal remote sensing images.To verify the effectiveness of the proposed MSFCN,we conduct experiments on two spatial datasets and two spatio-temporal datasets.The proposed MSFCN achieves 60.366%on the WHDLD dataset and 75.127%on the GID dataset in terms of mIoU index while the figures for two spatio-temporal datasets are 87.753%and 77.156%.Extensive comparative experiments and abla-tion studies demonstrate the effectiveness of the proposed MSFCN.展开更多
Dear Editor,In pansharpening task,the most existing deep-learning-based pansharpening methods fail to fully utilize the different level features,inevitably leading to spectral or spatial distortions.To address this ch...Dear Editor,In pansharpening task,the most existing deep-learning-based pansharpening methods fail to fully utilize the different level features,inevitably leading to spectral or spatial distortions.To address this challenge,in this letter,we propose a dual-branch multi-level feature aggregation network for pansharpening(DMFANet).展开更多
Close-range sensing has yet to attain the status of being a dependable source for in situ forest information as the conventional field inventory.Each solution has its advantages and disadvantages in terms of accuracy,...Close-range sensing has yet to attain the status of being a dependable source for in situ forest information as the conventional field inventory.Each solution has its advantages and disadvantages in terms of accuracy,completeness,and efficiency.For a forest area,Terrestrial Laser Scanning(TLS)has the highest data quality,but is limited to static perspectives and lack the efficiency.Mobile Mapping Systems(MMS)systems gain on the efficiency but compromise the data quality.More recently,under-canopy UAV caught attentions for its potential to leverage the advantages of both TLS and MMS systems.This study demonstrates the feasibility of autonomous forest in situ investigation using an autonomous under-canopy UAV Laser Scanning(ULS)system,and evaluates the performance of such system in deriving key forest and tree attributes through a comparison with other close-range sensing systems such as the TLS and the Personal Laser Scanning(PLS).The under-canopy ULS system uses an onboard LiDAR sensor to aid its self-traverse in an unknown forest environment and to collect point cloud data during its movement inside the forest.Key factors influencing the systems’overall performance were investigated via various experiments.The point cloud data collected by the under canopy autonomous ULS system deliver similar stem capturing capacity as TLS in single layer forest stands with less undergrowth.The RMSEs of the DBH estimates were 0.81 cm(3.80%),4.12cm(19.92%),and 5.13cm(22.01%),respectively.The RMSEs of the stem curve estimates were 1.27 cm(5.48%),3.97 cm(17.63%),and 5.18 cm(22.49%),respectively.The geometric accuracy and the completeness of the point cloud significantly improved when the trajectory was densified.More studies on autonomous route planning in complex unknown forest is required to improve the system mobility,data quality,and the applicability of such systems in future practical forest in situ observations.展开更多
This paper deals with the massive point cloud segmentation processing technology on the basis of machine vision, which is the second essential factor for the intelligent data processing of three dimensional conformati...This paper deals with the massive point cloud segmentation processing technology on the basis of machine vision, which is the second essential factor for the intelligent data processing of three dimensional conformation in digital photogrammetry. In this paper, multi-model fitting method is used to segment the point cloud according to the spatial distribution and spatial geometric structure of point clouds by fitting the point cloud into different geometric primitives models. Because point cloud usually possesses large amount of 3D points, which are uneven distributed over various complex structures, this paper proposes a point cloud segmentation method based on multi-model fitting. Firstly, the pre-segmentation of point cloud is conducted by using the clustering method based on density distribution. And then the follow fitting and segmentation are carried out by using the multi-model fitting method based on split and merging. For the plane and the arc surface, this paper uses different fitting methods, and finally realizing the indoor dense point cloud segmentation. The experimental results show that this method can achieve the automatic segmentation of the point cloud without setting the number of models in advance. Compared with the existing point cloud segmentation methods, this method has obvious advantages in segmentation effect and time cost, and can achieve higher segmentation accuracy. After processed by method proposed in this paper, the point cloud even with large-scale and complex structures can often be segmented into 3D geometric elements with finer and accurate model parameters, which can give rise to an accurate 3D conformation.展开更多
The GaoFen7(GF7)optical satellite is the first Chinese civilian sub-meter high-resolution stereo mapping satellite and is equipped with a double linear array camera and laser altimeter to achieve large-scale topograph...The GaoFen7(GF7)optical satellite is the first Chinese civilian sub-meter high-resolution stereo mapping satellite and is equipped with a double linear array camera and laser altimeter to achieve large-scale topographic mapping.To improve the accuracy of attitude determination,an attitude determination system comprised of four star sensors is loaded.According to the measurement accuracy and steady performance,the star sensors 1a and 1b is usually used together for satellite attitude calculation,which is called the conventional mode of attitude determination.Then,the combination of star sensors 2a and 2b is called the unconventional mode of attitude determination.Affected by variations in the incident angle of sunlight and solar radiation,thermal deformation occurs in the body and installation structure of the star sensor,which causes Attitude Low-Frequency Error(ALFE)and seriously influences the consistency of attitude determination results of different combination modes for multiple star sensors system.This study proposes an ALFE analysis and calibration approach for the multiple star sensors system of GF7 satellite to ensure the consistency of attitude determination results of different combination modes.Based on the statistical characteristics of the angles of the three axes,the installation parameters of the four star sensors are first calibrated.After analyzing the characteristics of the optical axis angles within 1420 orbit periods over 135 days,the segmented ALFE compensation model between the unconventional and conventional modes is proposed based on the Fourier series model and input parameter of latitude.Based on the on-orbit installation parameters and the ALFE model,the precise attitude determination results of the unconventional mode are calculated.Experimental results show that the attitude determination consistency after compensation is better than 2″.Moreover,the reliable application time range of the compensation model is 30 days to satisfy the requirements for high-precision attitude determination of GF7 satellite.展开更多
Spatial prediction of any geographic phenomenon can be an intractable problem.Predicting sparse and uncertain spatial events related to many influencing factors necessitates the integration of multiple data sources.We...Spatial prediction of any geographic phenomenon can be an intractable problem.Predicting sparse and uncertain spatial events related to many influencing factors necessitates the integration of multiple data sources.We present an innovative approach that combines data in a Discrete Global Grid System(DGGS)and uses machine learning for analysis.A DGGS provides a structured input for multiple types of spatial data,consistent over multiple scales.This data framework facilitates the training of an Artificial Neural Network(ANN)to map and predict a phenomenon.Spatial lag regression models(SLRM)are used to evaluate and rank the outputs of the ANN.In our case study,we predict hate crimes in the USA.Hate crimes get attention from mass media and the scientific community,but data on such events is sparse.We trained the ANN with data ingested in the DGGS based on a 50%sample of hate crimes as identified by the Southern Poverty Law Center(SPLC).Our spatial prediction is up to 78%accurate and verified at the state level against the independent FBI hate crime statistics with a fit of 80%.The derived risk maps are a guide to action for policy makers and law enforcement.展开更多
Emergency communication networks play a vital role in disaster monitoring,transmission,and application during disaster emergency response(DER),however,the performance and stability of edge nodes in the emergency commu...Emergency communication networks play a vital role in disaster monitoring,transmission,and application during disaster emergency response(DER),however,the performance and stability of edge nodes in the emergency communication networks are often weak due to limited communication and computation resources.This weakness directly affects the quality,of service(Qos)of the geospatial edge service(GES)chains involved in emergency monitoring.Existing research predominantly addresses service compositions in stable environments,neglecting the aggregation of efficient and robust GES chains in emergency communication networks.This study proposes an evolutionary_particie swarm optimization(EPSO)-based emergency monitoring GES chain in an_emergency communication network.it includes a GES chain model of emergency environment monitoring for tailing areas,as well as the designs of the particle chromosome encoding method,fitness evaluation model,and particle chromosome swarm update operators of the EPSO-based GES chain.Finally,the study conducts emergency environment monitoring experiments for tailing areas using the proposed method.Experiments results demonstrate that the proposed method significantly enhances the efficiency,stability,and reliability of emergency monitoring GEs chains in the emergency communication network.This is crucial to providing fast and reliable services for DER during natural disasters.展开更多
Earth observation(EO)data,such as high-resolution satellite imagery or LiDAR,has become one primary source for forests Aboveground Biomass(AGB)mapping and estimation.However,managing and analyzing the large amount of ...Earth observation(EO)data,such as high-resolution satellite imagery or LiDAR,has become one primary source for forests Aboveground Biomass(AGB)mapping and estimation.However,managing and analyzing the large amount of globally or locally available EO data remains a great challenge.The Google Earth Engine(GEE),which leverages cloud-computing services to provide powerful capabilities on the management and rapid analysis of various types of EO data,has appeared as an inestimable tool to address this challenge.In this paper,we present a scalable cyberinfrastructure for on-the-fly AGB estimation,statistics,and visualization over a large spatial extent.This cyberinfrastructure integrates state-of-the-art cloud computing applications,including GEE,Fusion Tables,and the Google Cloud Platform(GCP),to establish a scalable,highly extendable,and highperformance analysis environment.Two experiments were designed to demonstrate its superiority in performance over the traditional desktop environment and its scalability in processing complex workflows.In addition,a web portal was developed to integrate the cyberinfrastructure with some visualization tools(e.g.Google Maps,Highcharts)to provide a Graphical User Interfaces(GUI)and online visualization for both general public and geospatial researchers.展开更多
Big data have 4V characteristics of volume, variety, velocity, and veracity, which authentically calls for big data analytics. However, what are the dominant characteristics of big data analysis? Here, the analytics i...Big data have 4V characteristics of volume, variety, velocity, and veracity, which authentically calls for big data analytics. However, what are the dominant characteristics of big data analysis? Here, the analytics is related to the entire methodology rather than the individual specific analysis. In this paper, six techniques concerning big data analytics are proposed, which include: (1) Ensemble analysis related to a large volume of data, (2) Association analysis related to unknown data sampling, (3) High-dimensional analysis related to a variety of data, (4) Deep analysis related to the veracity of data, (5) Precision analysis related to the veracity of data, and (6) Divide-and-conquer analysis related to the velocity of data.The essential of big data analytics is the structural analysis of big data in an optimal criterion of physics, computation, and human cognition. fundamentally, two theoretical challenges, ie the violation of independent and identical distribution, and the extension of general set-theory, are posed. In particular, we have illustrated three kinds of association in geographical big data, ie geometrical associations in space and time, spatiotemporal correlations in statistics, and space-time relations in semantics. furthermore, we have illustrated three kinds of spatiotemporal data analysis, ie measurement (observation) adjustment of geometrical quantities, human spatial behavior analysis with trajectories, data assimilation of physical models and various observations, from which spatiotemporal big data analysis may be largely derived.展开更多
Satellite normalized difference vegetation index(NDVI)time series,essential for ecological and environmental applications,is still limited by a trade-off between the spatiotemporal resolution and time coverage despite...Satellite normalized difference vegetation index(NDVI)time series,essential for ecological and environmental applications,is still limited by a trade-off between the spatiotemporal resolution and time coverage despite various global products.The Advanced Very High-Resolution Radiometer(AVHRR)instrument can provide the longest continuous time series since 1982,but with the drawback of coarse spatial resolution and poor data quality.To address this issue,a spatiotemporal fusion-based long-term NDVI product(STFLNDVI)since 1982 was generated in this study at a 1-km spatial resolution with monthly intervals,by fusing with the Moderate Resolution Imaging Spectroradiometer(MODIS)data.A multi-step processing fusion framework,containing temporal filtering,normalization,spatiotemporal fusion,and residual error correction,was employed to combine the superior characteristics of the two products,respectively.Simulated comparison with MODIS data and real-data assessments with true 1 km AVHRR data both confirm the ideal accuracy of the fusion product in spatial distribution and temporal variation,providing stable long-term results similar to MODIS data.We believe that the STFLNDVI product will be of great significance in characterizing the spatial patterns and long-term variations of global vegetation and the historical radiometric calibrations in AVHRR data gaps around the Arctic and instrument differences between MODIS and AVHRR should be further considered in the future.展开更多
This paper proposed a geoscience model service integrated workflowbased rainstorm waterlogging analysis method to overcome the defects of conventional waterlogging analysis systems.In this research,we studied a genera...This paper proposed a geoscience model service integrated workflowbased rainstorm waterlogging analysis method to overcome the defects of conventional waterlogging analysis systems.In this research,we studied a general OGC WPS service invoking strategy,an automatic asynchronous invoking mechanism of WPS services in the BPEL workflow,and a distributed waterlogging analysis services integrated workflow to realize the reconstruction of a waterlogging analysis model based on the proposed method.The proposed method can make use of the flexible adjustment capability of the workflow and not only overcomes the inherent defects of conventional geoscience analysis methods but also realizes the integration and calculation of distributed geospatial data,models and computing resources automatically.The method has better construction convenience,execution reliability,extensibility and intelligence potential than a conventional method and has important value for dealing with more natural disasters and environmental challenges.展开更多
基金supported by The National Natural Science Foundation of China[grant number 42171417]the Special Fund of Hubei Luojia Laboratory[grant number 220100008]the Key Research and Development Program of Hubei Province[grant number 2021BAA166].
文摘Indoor positioning with high accuracy plays an important role in different application scenar-ios.As a widely used mobile communication signal,the Long-Term Evolution(LTE)network can be well received in indoor and outdoor environments.This article studies a method of using different reference signals in the LTE downlink for carrier phase time of arrival(TOA)estimation.Specifically,a solution is proposed and a multipath tracking Software Defined Receiver(SDR)is developed for indoor positioning.With our SDR indoor positioning system,the pilot signals of the LTE signals are firstly obtained by the coarse synchronization and demodulation.Then,with the assistance of the pilot signals,the time delay acquisition,the multipath estimating delay lock loop(MEDLL)algorithm,and the multipath anomaly detection are sequentially carried out to obtain navigation observations of received signals.Furthermore,to compare the perfor-mance of different pilot signals,the Secondary Synchronous Signals(SSS)and Cell Reference Signals(CRS)are used as pilot signals for carrier phase-based TOA estimation,respectively.Finally,to quantify the accuracy of our multipath tracking SDR,indoor field tests are carried out in a conference environment,where an LTE base station is installed for commercial use.Our test results based on CRS show that,in the static test scenarios,the TOA accuracy measured by the 1-σerror interval is about 0.5 m,while in the mobile environment,the probability of range accuracy within 1.0 m is 95%.
基金This research was funded by the National Key R&D Program of China(No.2018YFB2100603)the Key R&D Program of Hubei Province(No.2022BAA048)+2 种基金the National Natural Science Foundation of China program(No.41890822)the Open Fund of National Engineering Research Centre for Geographic Information System,China University of Geosciences,Wuhan 430074,China(No.2022KFJJ07)The numerical calculations in this paper have been done on the supercomputing system in the Supercomputing Centre of Wuhan University.
文摘To protect the environment,the discharged sewage’s quality must meet the state’s discharge standards.There are many water quality indicators,and the pH(Potential of Hydrogen)value is one of them.The natural water’s pH value is 6.0–8.5.The sewage treatment plant uses some data in the sewage treatment process to monitor and predict whether wastewater’s pH value will exceed the standard.This paper aims to study the deep learning prediction model of wastewater’s pH.Firstly,the research uses the random forest method to select the data features and then,based on the sliding window,convert the data set into a time series which is the input of the deep learning training model.Secondly,by analyzing and comparing relevant references,this paper believes that the CNN(Convolutional Neural Network)model is better at nonlinear data modeling and constructs a CNN model including the convolution and pooling layers.After alternating the combination of the convolutional layer and pooling layer,all features are integrated into a full-connected neural network.Thirdly,the number of input samples of the CNN model directly affects the prediction effect of the model.Therefore,this paper adopts the sliding window method to study the optimal size.Many experimental results show that the optimal prediction model can be obtained when alternating six convolutional layers and three pooling layers.The last full-connection layer contains two layers and 64 neurons per layer.The sliding window size selects as 12.Finally,the research has carried out data prediction based on the optimal CNN deep learning model.The predicted pH of the sewage is between 7.2 and 8.6 in this paper.The result is applied in the monitoring system platform of the“Intelligent operation and maintenance platform of the reclaimed water plant.”
基金supported by the Strategic Priority Research Program of the Chinese Academy of Sciences(No.XDA19090104)the National Natural Science Foundation of China(No.41922008)the Hubei Provincial Natural Science Foundation of China(No.2020CFA051)。
文摘Ground-level ozone(O_(3))is a primary air pollutant,which can greatly harm human health and ecosystems.At present,data fusion frameworks only provided ground-level O_(3)concentrations at coarse spatial(e.g.,10 km)or temporal(e.g.,daily)resolutions.As photochemical pollution continues increasing over China in the last few years,a high-spatial–temporal-resolution product is required to enhance the comprehension of ground-level O_(3)formation mechanisms.To address this issue,our study creatively explores a brand-new framework for estimating hourly 2-km ground-level O_(3)concentrations across China(except Xinjiang and Tibet)using the brightness temperature at multiple thermal infrared bands.Considering the spatial heterogeneity of ground-level O_(3),a novel Self-adaptive Geospatially Local scheme based on Categorical boosting(SGLboost)is developed to train the estimation models.Validation results show that SGLboost performs well in the study area,with the R2 s/RMSEs of 0.85/19.041 lg/m^(3)and 0.72/25.112 lg/m^(3)for the space-based cross-validation(CV)(2017–2019)and historical space-based CV(2019),respectively.Meanwhile,SGLboost achieves distinctly better metrics than those of some widely used machine learning methods,such as e Xtreme Gradient boosting and Random Forest.Compared to recent related works over China,the performance of SGLboost is also more desired.Regarding the spatial distribution,the estimated results present continuous spatial patterns without a significantly partitioned boundary effect.In addition,accurate hourly and seasonal variations of ground-level O_(3)concentrations can be observed in the estimated results over the study area.It is believed that the hourly 2-km results estimated by SGLboost will help further understand the formation mechanisms of ground-level O_(3)in China.
基金financial support from the National Natural Science Foundation of China(Grant Nos.32171789,32211530031)Wuhan University(No.WHUZZJJ202220)Academy of Finland(Nos.334060,334829,331708,344755,337656,334830,293389/314312,334830,319011)。
文摘Forest is one of the most challenging environments to be recorded in a three-dimensional(3D)digitized geometrical representation,because of the size and the complexity of the environment and the data-acquisition constraints brought by on-site conditions.Previous studies have indicated that the data-acquisition pattern can have more influence on the registration results than other factors.In practice,the ideal short-baseline observations,i.e.,the dense collection mode,is rarely feasible,considering the low accessibility in forest environments and the commonly limited labor and time resources.The wide-baseline observations that cover a forest site using a few folds less observations than short-baseline observations,are therefore more preferable and commonly applied.Nevertheless,the wide-baseline approach is more challenging for data registration since it typically lacks the required sufficient overlaps between datasets.Until now,a robust automated registration solution that is independent of special hardware requirements has still been missing.That is,the registration accuracy is still far from the required level,and the information extractable from the merged point cloud using automated registration could not match that from the merged point cloud using manual registration.This paper proposes a discrete overlap search(DOS)method to find correspondences in the point clouds to solve the low-overlap problem in the wide-baseline point clouds.The proposed automatic method uses potential correspondences from both original data and selected feature points to reconstruct rough observation geometries without external knowledge and to retrieve precise registration parameters at data-level.An extensive experiment was carried out with 24 forest datasets of different conditions categorized in three difficulty levels.The performance of the proposed method was evaluated using various accuracy criteria,as well as based on data acquired from different hardware,platforms,viewing perspectives,and at different points of time.The proposed method achieved a 3D registration accuracy at a 0.50-cm level in all difficulty categories using static terrestrial acquisitions.In the terrestrial-aerial registration,data sets were collected from different sensors and at different points of time with scene changes,and a registration accuracy at the raw data geometric accuracy level was achieved.These results represent the highest automated registration accuracy and the strictest evaluation so far.The proposed method is applicable in multiple scenarios,such as 1)the global positioning of individual under-canopy observations,which is one of the main challenges in applying terrestrial observations lacking a global context,2)the fusion of point clouds acquired from terrestrial and aerial perspectives,which is required in order to achieve a complete forest observation,3)mobile mapping using a new stop-and-go approach,which solves the problems of lacking mobility and slow data collection in static terrestrial measurements as well as the data-quality issue in the continuous mobile approach.Furthermore,this work proposes a new error estimate that units all parameter-level errors into a single quantity and compensates for the downsides of the widely used parameter-and object-level error estimates;it also proposes a new deterministic point sets registration method as an alternative to the popular sampling methods.
基金supported by the National Natural Science Foundation of China[grant number 41671452].
文摘Although the Convolutional Neural Network(CNN)has shown great potential for land cover classification,the frequently used single-scale convolution kernel limits the scope of informa-tion extraction.Therefore,we propose a Multi-Scale Fully Convolutional Network(MSFCN)with a multi-scale convolutional kernel as well as a Channel Attention Block(CAB)and a Global Pooling Module(GPM)in this paper to exploit discriminative representations from two-dimensional(2D)satellite images.Meanwhile,to explore the ability of the proposed MSFCN for spatio-temporal images,we expand our MSFCN to three-dimension using three-dimensional(3D)CNN,capable of harnessing each land cover category’s time series interac-tion from the reshaped spatio-temporal remote sensing images.To verify the effectiveness of the proposed MSFCN,we conduct experiments on two spatial datasets and two spatio-temporal datasets.The proposed MSFCN achieves 60.366%on the WHDLD dataset and 75.127%on the GID dataset in terms of mIoU index while the figures for two spatio-temporal datasets are 87.753%and 77.156%.Extensive comparative experiments and abla-tion studies demonstrate the effectiveness of the proposed MSFCN.
基金supported in part by the National Natural Science Foundation of China(42090012)03 Special Research and 5G Project of Jiangxi Province in China(20212ABC03A09)the Open Grants of the State Key Laboratory of Severe Weather(2021LASW-A17)。
文摘Dear Editor,In pansharpening task,the most existing deep-learning-based pansharpening methods fail to fully utilize the different level features,inevitably leading to spectral or spatial distortions.To address this challenge,in this letter,we propose a dual-branch multi-level feature aggregation network for pansharpening(DMFANet).
基金support from Natural Science Fund of China[32171789].
文摘Close-range sensing has yet to attain the status of being a dependable source for in situ forest information as the conventional field inventory.Each solution has its advantages and disadvantages in terms of accuracy,completeness,and efficiency.For a forest area,Terrestrial Laser Scanning(TLS)has the highest data quality,but is limited to static perspectives and lack the efficiency.Mobile Mapping Systems(MMS)systems gain on the efficiency but compromise the data quality.More recently,under-canopy UAV caught attentions for its potential to leverage the advantages of both TLS and MMS systems.This study demonstrates the feasibility of autonomous forest in situ investigation using an autonomous under-canopy UAV Laser Scanning(ULS)system,and evaluates the performance of such system in deriving key forest and tree attributes through a comparison with other close-range sensing systems such as the TLS and the Personal Laser Scanning(PLS).The under-canopy ULS system uses an onboard LiDAR sensor to aid its self-traverse in an unknown forest environment and to collect point cloud data during its movement inside the forest.Key factors influencing the systems’overall performance were investigated via various experiments.The point cloud data collected by the under canopy autonomous ULS system deliver similar stem capturing capacity as TLS in single layer forest stands with less undergrowth.The RMSEs of the DBH estimates were 0.81 cm(3.80%),4.12cm(19.92%),and 5.13cm(22.01%),respectively.The RMSEs of the stem curve estimates were 1.27 cm(5.48%),3.97 cm(17.63%),and 5.18 cm(22.49%),respectively.The geometric accuracy and the completeness of the point cloud significantly improved when the trajectory was densified.More studies on autonomous route planning in complex unknown forest is required to improve the system mobility,data quality,and the applicability of such systems in future practical forest in situ observations.
基金The National Natural Science Foundation of China (61261130587,61571332).
文摘This paper deals with the massive point cloud segmentation processing technology on the basis of machine vision, which is the second essential factor for the intelligent data processing of three dimensional conformation in digital photogrammetry. In this paper, multi-model fitting method is used to segment the point cloud according to the spatial distribution and spatial geometric structure of point clouds by fitting the point cloud into different geometric primitives models. Because point cloud usually possesses large amount of 3D points, which are uneven distributed over various complex structures, this paper proposes a point cloud segmentation method based on multi-model fitting. Firstly, the pre-segmentation of point cloud is conducted by using the clustering method based on density distribution. And then the follow fitting and segmentation are carried out by using the multi-model fitting method based on split and merging. For the plane and the arc surface, this paper uses different fitting methods, and finally realizing the indoor dense point cloud segmentation. The experimental results show that this method can achieve the automatic segmentation of the point cloud without setting the number of models in advance. Compared with the existing point cloud segmentation methods, this method has obvious advantages in segmentation effect and time cost, and can achieve higher segmentation accuracy. After processed by method proposed in this paper, the point cloud even with large-scale and complex structures can often be segmented into 3D geometric elements with finer and accurate model parameters, which can give rise to an accurate 3D conformation.
基金supported by the National Science Fund for Distinguished Young Scholars[grant number 61825103]the Shanghai Aerospace Science and Technology Innovation Fund.
文摘The GaoFen7(GF7)optical satellite is the first Chinese civilian sub-meter high-resolution stereo mapping satellite and is equipped with a double linear array camera and laser altimeter to achieve large-scale topographic mapping.To improve the accuracy of attitude determination,an attitude determination system comprised of four star sensors is loaded.According to the measurement accuracy and steady performance,the star sensors 1a and 1b is usually used together for satellite attitude calculation,which is called the conventional mode of attitude determination.Then,the combination of star sensors 2a and 2b is called the unconventional mode of attitude determination.Affected by variations in the incident angle of sunlight and solar radiation,thermal deformation occurs in the body and installation structure of the star sensor,which causes Attitude Low-Frequency Error(ALFE)and seriously influences the consistency of attitude determination results of different combination modes for multiple star sensors system.This study proposes an ALFE analysis and calibration approach for the multiple star sensors system of GF7 satellite to ensure the consistency of attitude determination results of different combination modes.Based on the statistical characteristics of the angles of the three axes,the installation parameters of the four star sensors are first calibrated.After analyzing the characteristics of the optical axis angles within 1420 orbit periods over 135 days,the segmented ALFE compensation model between the unconventional and conventional modes is proposed based on the Fourier series model and input parameter of latitude.Based on the on-orbit installation parameters and the ALFE model,the precise attitude determination results of the unconventional mode are calculated.Experimental results show that the attitude determination consistency after compensation is better than 2″.Moreover,the reliable application time range of the compensation model is 30 days to satisfy the requirements for high-precision attitude determination of GF7 satellite.
文摘Spatial prediction of any geographic phenomenon can be an intractable problem.Predicting sparse and uncertain spatial events related to many influencing factors necessitates the integration of multiple data sources.We present an innovative approach that combines data in a Discrete Global Grid System(DGGS)and uses machine learning for analysis.A DGGS provides a structured input for multiple types of spatial data,consistent over multiple scales.This data framework facilitates the training of an Artificial Neural Network(ANN)to map and predict a phenomenon.Spatial lag regression models(SLRM)are used to evaluate and rank the outputs of the ANN.In our case study,we predict hate crimes in the USA.Hate crimes get attention from mass media and the scientific community,but data on such events is sparse.We trained the ANN with data ingested in the DGGS based on a 50%sample of hate crimes as identified by the Southern Poverty Law Center(SPLC).Our spatial prediction is up to 78%accurate and verified at the state level against the independent FBI hate crime statistics with a fit of 80%.The derived risk maps are a guide to action for policy makers and law enforcement.
基金funded by the National Natural Science Foundation of China(NSFC)[grant ID 42271425,41871312,42271431].
文摘Emergency communication networks play a vital role in disaster monitoring,transmission,and application during disaster emergency response(DER),however,the performance and stability of edge nodes in the emergency communication networks are often weak due to limited communication and computation resources.This weakness directly affects the quality,of service(Qos)of the geospatial edge service(GES)chains involved in emergency monitoring.Existing research predominantly addresses service compositions in stable environments,neglecting the aggregation of efficient and robust GES chains in emergency communication networks.This study proposes an evolutionary_particie swarm optimization(EPSO)-based emergency monitoring GES chain in an_emergency communication network.it includes a GES chain model of emergency environment monitoring for tailing areas,as well as the designs of the particle chromosome encoding method,fitness evaluation model,and particle chromosome swarm update operators of the EPSO-based GES chain.Finally,the study conducts emergency environment monitoring experiments for tailing areas using the proposed method.Experiments results demonstrate that the proposed method significantly enhances the efficiency,stability,and reliability of emergency monitoring GEs chains in the emergency communication network.This is crucial to providing fast and reliable services for DER during natural disasters.
文摘Earth observation(EO)data,such as high-resolution satellite imagery or LiDAR,has become one primary source for forests Aboveground Biomass(AGB)mapping and estimation.However,managing and analyzing the large amount of globally or locally available EO data remains a great challenge.The Google Earth Engine(GEE),which leverages cloud-computing services to provide powerful capabilities on the management and rapid analysis of various types of EO data,has appeared as an inestimable tool to address this challenge.In this paper,we present a scalable cyberinfrastructure for on-the-fly AGB estimation,statistics,and visualization over a large spatial extent.This cyberinfrastructure integrates state-of-the-art cloud computing applications,including GEE,Fusion Tables,and the Google Cloud Platform(GCP),to establish a scalable,highly extendable,and highperformance analysis environment.Two experiments were designed to demonstrate its superiority in performance over the traditional desktop environment and its scalability in processing complex workflows.In addition,a web portal was developed to integrate the cyberinfrastructure with some visualization tools(e.g.Google Maps,Highcharts)to provide a Graphical User Interfaces(GUI)and online visualization for both general public and geospatial researchers.
基金This study is supported jointly by the Fundamental Research Funds for the Central Universities, the Key Project of National Natural Science Foundation of China [grant number 41331175, and the LIESMARS Special Research Funding
文摘Big data have 4V characteristics of volume, variety, velocity, and veracity, which authentically calls for big data analytics. However, what are the dominant characteristics of big data analysis? Here, the analytics is related to the entire methodology rather than the individual specific analysis. In this paper, six techniques concerning big data analytics are proposed, which include: (1) Ensemble analysis related to a large volume of data, (2) Association analysis related to unknown data sampling, (3) High-dimensional analysis related to a variety of data, (4) Deep analysis related to the veracity of data, (5) Precision analysis related to the veracity of data, and (6) Divide-and-conquer analysis related to the velocity of data.The essential of big data analytics is the structural analysis of big data in an optimal criterion of physics, computation, and human cognition. fundamentally, two theoretical challenges, ie the violation of independent and identical distribution, and the extension of general set-theory, are posed. In particular, we have illustrated three kinds of association in geographical big data, ie geometrical associations in space and time, spatiotemporal correlations in statistics, and space-time relations in semantics. furthermore, we have illustrated three kinds of spatiotemporal data analysis, ie measurement (observation) adjustment of geometrical quantities, human spatial behavior analysis with trajectories, data assimilation of physical models and various observations, from which spatiotemporal big data analysis may be largely derived.
基金supported by the National Key Research and Development Program of China[2017YFA0604402]National Natural Science Foundation of China[42371364]+3 种基金National Natural Science Foundation of China[U23A2021]National Natural Science Foundation of China[42001371]China Postdoctoral Science Foundation Special Project[2022T150489]the China Postdoctoral Science Foundation Special Project(213926).
文摘Satellite normalized difference vegetation index(NDVI)time series,essential for ecological and environmental applications,is still limited by a trade-off between the spatiotemporal resolution and time coverage despite various global products.The Advanced Very High-Resolution Radiometer(AVHRR)instrument can provide the longest continuous time series since 1982,but with the drawback of coarse spatial resolution and poor data quality.To address this issue,a spatiotemporal fusion-based long-term NDVI product(STFLNDVI)since 1982 was generated in this study at a 1-km spatial resolution with monthly intervals,by fusing with the Moderate Resolution Imaging Spectroradiometer(MODIS)data.A multi-step processing fusion framework,containing temporal filtering,normalization,spatiotemporal fusion,and residual error correction,was employed to combine the superior characteristics of the two products,respectively.Simulated comparison with MODIS data and real-data assessments with true 1 km AVHRR data both confirm the ideal accuracy of the fusion product in spatial distribution and temporal variation,providing stable long-term results similar to MODIS data.We believe that the STFLNDVI product will be of great significance in characterizing the spatial patterns and long-term variations of global vegetation and the historical radiometric calibrations in AVHRR data gaps around the Arctic and instrument differences between MODIS and AVHRR should be further considered in the future.
基金funded by the National Key Research and Development Program of China[grant number 2018YFB2100504]the National Science Foundation of China(NSFC)[grant number 41871312]+4 种基金the National Key Research and Development Program of China[grant number 2017YFB0504202]the Fundamental Research Funds for the Central Universities[grant number 2042019kf0226]the Hubei Natural Science Foundation[grant number 2017CFB433]Open Research Project of The Hubei Key Laboratory of Intelligent Geo-Information Processing[grant number KLIGIP-2017A09]the Beijing Key Laboratory of Urban Spatial Information Engineering[grant number 2017209].
文摘This paper proposed a geoscience model service integrated workflowbased rainstorm waterlogging analysis method to overcome the defects of conventional waterlogging analysis systems.In this research,we studied a general OGC WPS service invoking strategy,an automatic asynchronous invoking mechanism of WPS services in the BPEL workflow,and a distributed waterlogging analysis services integrated workflow to realize the reconstruction of a waterlogging analysis model based on the proposed method.The proposed method can make use of the flexible adjustment capability of the workflow and not only overcomes the inherent defects of conventional geoscience analysis methods but also realizes the integration and calculation of distributed geospatial data,models and computing resources automatically.The method has better construction convenience,execution reliability,extensibility and intelligence potential than a conventional method and has important value for dealing with more natural disasters and environmental challenges.
