2020 Vol. 45, No. 6
Display Method:
2020, 45(6): 791-797.
doi: 10.13203/j.whugis20200255
Abstract:
Epidemic diagnosis time can scientifically evaluate and comprehensively reflect the emergency level and physical therapy capacity of a national or local health department. Based on the diagnosis and treatment time records of more than 70 000 diagnosed or suspected cases of coronavirus disease 2019(COVID-19) provided by the Chinese Center for Disease Control and Prevention (CDC), this paper uses regional statistics, spatial mapping, trend simulation, and significance testing to analyze the temporal and spatial distribution, spatial differentiation and dynamic process of the early diagnosis time of COVID-19 in Chinese mainland. Results show that:(1) The average epidemic diagnosis time for COVID-19 outbreak from early onset to diagnosis is 7.35 days. Among them, Hubei Province is 7.99 days, and the average diagnosis time in other provinces is 5.68 days. The average time from suspect to diagnosis is 3.86 days, 4.08 days in Hubei, and 2.91 days in other provinces. Although the epidemic diagnosis time in Hubei Province is slightly higher than the average, the difference in spatial differentiation is not particularly noticeable. The average dispersion of diagnosis time is only 0.58 days. (2) The dynamic process of COVID-19 diagnosis time shows a significant decrease trend(Slope=-0.78, P < 0.01, two-tailed), and the diagnosis time quickly drops to 1 day within 2 months of the early outbreak. This greatly improves the efficacy of epidemiological diagnosis. (3) Pearson correlation analysis between epidemic diagnosis time and healing period shows significantly related (P < 0.01, two-tailed). Early diagnosis and early treatment can reduce the time for patients to heal. All patients are treated in time to effectively improve the cure rate. The above research results indicate that:Chinese government has steadily promoted the overall prevention and control of the epidemic, and achieved remarkable results. Efficient diagnosis reduces the natural exposure time of the virus, reduces the time to cure and the probability of potential cross-infection. It has laid a solid foundation for the prevention and control of the epidemic in China in a short period of time and the nationwide resumption of work and production.
Epidemic diagnosis time can scientifically evaluate and comprehensively reflect the emergency level and physical therapy capacity of a national or local health department. Based on the diagnosis and treatment time records of more than 70 000 diagnosed or suspected cases of coronavirus disease 2019(COVID-19) provided by the Chinese Center for Disease Control and Prevention (CDC), this paper uses regional statistics, spatial mapping, trend simulation, and significance testing to analyze the temporal and spatial distribution, spatial differentiation and dynamic process of the early diagnosis time of COVID-19 in Chinese mainland. Results show that:(1) The average epidemic diagnosis time for COVID-19 outbreak from early onset to diagnosis is 7.35 days. Among them, Hubei Province is 7.99 days, and the average diagnosis time in other provinces is 5.68 days. The average time from suspect to diagnosis is 3.86 days, 4.08 days in Hubei, and 2.91 days in other provinces. Although the epidemic diagnosis time in Hubei Province is slightly higher than the average, the difference in spatial differentiation is not particularly noticeable. The average dispersion of diagnosis time is only 0.58 days. (2) The dynamic process of COVID-19 diagnosis time shows a significant decrease trend(Slope=-0.78, P < 0.01, two-tailed), and the diagnosis time quickly drops to 1 day within 2 months of the early outbreak. This greatly improves the efficacy of epidemiological diagnosis. (3) Pearson correlation analysis between epidemic diagnosis time and healing period shows significantly related (P < 0.01, two-tailed). Early diagnosis and early treatment can reduce the time for patients to heal. All patients are treated in time to effectively improve the cure rate. The above research results indicate that:Chinese government has steadily promoted the overall prevention and control of the epidemic, and achieved remarkable results. Efficient diagnosis reduces the natural exposure time of the virus, reduces the time to cure and the probability of potential cross-infection. It has laid a solid foundation for the prevention and control of the epidemic in China in a short period of time and the nationwide resumption of work and production.
2020, 45(6): 798-807.
doi: 10.13203/j.whugis20200241
Abstract:
Spatial-temporal analysis method provides technical support for epidemiological investigation. To analyse and demonstrate the transmission of coronavirus disease 2019(COVID-19), this paper takes the data of COVID-19 cases in Shenzhen as an example, combines epidemiological investigation knowledge with geo-location linked association analysis, and uses the spatial-temporal five-tuple model to structure and analyze the case data. Rules based on spatial-temporal five-tuple model for case type judgment and statistical analysis are defined, which can use spatial-temporal overlap principles to judge two types of cases, input cases and contact cases, and to make temporal statistics and zoning statistics about the confirmed cases. This paper defines the five-tuple model and its operation rules for judging and analyzing the epidemic gathering situation, which can use the principle of spatial-temporal overlap to judge and mine the epidemic gathering situation and to analyze its propagation process. Combined with GIS spatial-temporal visualization, the entire process of epidemic developments and transmission are displayed in the maps with interactive interface along with temporal series diagrams and social relationship diagrams. During the spreading stage of the epidemic situation, by updating the case data and implementing the analysis, the spatial-temporal five-tuple structure and its operating rules could be feasible to judge, deduce quickly and show the changing status of the epidemic simultaneously with their visualization. The spatial-temporal five-tuple model combined with visualization technology can effectively display the distribution and transmission of the diseases, health or hygiene events, and provide support for disease control agencies to understand and control the spread of epidemic conditions.
Spatial-temporal analysis method provides technical support for epidemiological investigation. To analyse and demonstrate the transmission of coronavirus disease 2019(COVID-19), this paper takes the data of COVID-19 cases in Shenzhen as an example, combines epidemiological investigation knowledge with geo-location linked association analysis, and uses the spatial-temporal five-tuple model to structure and analyze the case data. Rules based on spatial-temporal five-tuple model for case type judgment and statistical analysis are defined, which can use spatial-temporal overlap principles to judge two types of cases, input cases and contact cases, and to make temporal statistics and zoning statistics about the confirmed cases. This paper defines the five-tuple model and its operation rules for judging and analyzing the epidemic gathering situation, which can use the principle of spatial-temporal overlap to judge and mine the epidemic gathering situation and to analyze its propagation process. Combined with GIS spatial-temporal visualization, the entire process of epidemic developments and transmission are displayed in the maps with interactive interface along with temporal series diagrams and social relationship diagrams. During the spreading stage of the epidemic situation, by updating the case data and implementing the analysis, the spatial-temporal five-tuple structure and its operating rules could be feasible to judge, deduce quickly and show the changing status of the epidemic simultaneously with their visualization. The spatial-temporal five-tuple model combined with visualization technology can effectively display the distribution and transmission of the diseases, health or hygiene events, and provide support for disease control agencies to understand and control the spread of epidemic conditions.
2020, 45(6): 808-815.
doi: 10.13203/j.whugis20200212
Abstract:
Locating the coronavirus disease 2019 (COVID-19)cases in the accurate place is important in epidemic prevention and control. Geocoding is an effective method to achieve information space positioning with address description. The English based geocoding methodology is not suitable for Chinese address. Composition and positioning methods of Chinese address are discussed. According to the characteristics of high complexity and diversity of Chinese address, a Chinese word segmentation weighted address matching algorithm considering a variety of semantics is designed, including the same pronunciation but different Chinese word address, abbreviation and alias of Chinese address, different description of the same address. And the matching accuracy and efficiency of the algorithm are tested by using the COVID-19 cases' addresses in Wuhan. The result indicates the algorithm is efficient, accurate, and intelligent, which can realize the efficient location of the COVID-19 cases address, and provide accurate spatial location information for epidemic prevention and control by quickly positioning of the COVID-19 cases.
Locating the coronavirus disease 2019 (COVID-19)cases in the accurate place is important in epidemic prevention and control. Geocoding is an effective method to achieve information space positioning with address description. The English based geocoding methodology is not suitable for Chinese address. Composition and positioning methods of Chinese address are discussed. According to the characteristics of high complexity and diversity of Chinese address, a Chinese word segmentation weighted address matching algorithm considering a variety of semantics is designed, including the same pronunciation but different Chinese word address, abbreviation and alias of Chinese address, different description of the same address. And the matching accuracy and efficiency of the algorithm are tested by using the COVID-19 cases' addresses in Wuhan. The result indicates the algorithm is efficient, accurate, and intelligent, which can realize the efficient location of the COVID-19 cases address, and provide accurate spatial location information for epidemic prevention and control by quickly positioning of the COVID-19 cases.
Construction of the COVID-19 Epidemic Cases Activity Knowledge Graph: A Case Study of Zhengzhou City
2020, 45(6): 816-825.
doi: 10.13203/j.whugis20200201
Abstract:
At present, the number of coronavirus disease 2019(COVID-19) cases worldwide is increasing, the spatio temporal spread of the epidemic becomes more and more complicated. The traditional researches on the transmission process is mainly focus on transmission trends of infectious diseases at the macro level. It is impossible to analyze the transmission relationship between specific cases at the individual level, to accurately locate the transmission paths of the epidemic, and it is difficult to support the precise prevention of infectious diseases. So, it is an urgent need to study the transmission process of infectious diseases on both of the semantic and spatio-temporal features. Based on the analysis of COVID-19 epidemic cases data, we construct the COVID-19 cases activity knowledge graph, which adapts to various description methods. Then, we design the ontology rules to complete the construction of the pattern layer. The epidemiological survey data has been analyzed to recognize the event entities, and to complete the construction of data layer. Finally, through the graph database and the B/S pattern to build a prototype system for experimental verification. The results show that it is effective and feasible to analyze the transmission process, infection relationship, key nodes and activity trajectory through the COVID-19 cases activity knowledge graph.
At present, the number of coronavirus disease 2019(COVID-19) cases worldwide is increasing, the spatio temporal spread of the epidemic becomes more and more complicated. The traditional researches on the transmission process is mainly focus on transmission trends of infectious diseases at the macro level. It is impossible to analyze the transmission relationship between specific cases at the individual level, to accurately locate the transmission paths of the epidemic, and it is difficult to support the precise prevention of infectious diseases. So, it is an urgent need to study the transmission process of infectious diseases on both of the semantic and spatio-temporal features. Based on the analysis of COVID-19 epidemic cases data, we construct the COVID-19 cases activity knowledge graph, which adapts to various description methods. Then, we design the ontology rules to complete the construction of the pattern layer. The epidemiological survey data has been analyzed to recognize the event entities, and to complete the construction of data layer. Finally, through the graph database and the B/S pattern to build a prototype system for experimental verification. The results show that it is effective and feasible to analyze the transmission process, infection relationship, key nodes and activity trajectory through the COVID-19 cases activity knowledge graph.
2020, 45(6): 826-835.
doi: 10.13203/j.whugis20200152
Abstract:
Coronavirus disease 2019 (COVID-19) is a major public health emergency, it is of great significance to study the influence of urban spatial factors on the development of epidemic situation for the future urban safety issues. Wuhan is affected most heavily by this epidemic situation. Based on Sina Weibo data posted in the core area of Wuhan city, we reveal the spatial distribution pattern of COVID-19 epidemic and its impacts in different urban areas of the city. According to the major suspected transmission routes and related factors of the epidemic, indicators of social population, urban morphology, urban facilities, and urban functions, are selected for validation. Through gridding the research area into uniform analytical units, we reveal the effect, spatial heterogeneity, and influence area of these factors, using the geographical weighted regression model. The result indicates that some factors, e.g. the densities of major hospitals, commercial facilities, subway stations, construction scale, aging, and land-use mixture, present significant influence on the epidemic severity.This research helps to explain and perform the mechanism of occurrence and spread of the epidemic in urban space. The analysis and validation of these urban factors help to adopt effective urban planning and architectural design responses in the future crisis, as help decision makers formulate more scientific and reasonable prevention strategies, and avoid or reduce the impact on vulnerable areas and groups in advance.
Coronavirus disease 2019 (COVID-19) is a major public health emergency, it is of great significance to study the influence of urban spatial factors on the development of epidemic situation for the future urban safety issues. Wuhan is affected most heavily by this epidemic situation. Based on Sina Weibo data posted in the core area of Wuhan city, we reveal the spatial distribution pattern of COVID-19 epidemic and its impacts in different urban areas of the city. According to the major suspected transmission routes and related factors of the epidemic, indicators of social population, urban morphology, urban facilities, and urban functions, are selected for validation. Through gridding the research area into uniform analytical units, we reveal the effect, spatial heterogeneity, and influence area of these factors, using the geographical weighted regression model. The result indicates that some factors, e.g. the densities of major hospitals, commercial facilities, subway stations, construction scale, aging, and land-use mixture, present significant influence on the epidemic severity.This research helps to explain and perform the mechanism of occurrence and spread of the epidemic in urban space. The analysis and validation of these urban factors help to adopt effective urban planning and architectural design responses in the future crisis, as help decision makers formulate more scientific and reasonable prevention strategies, and avoid or reduce the impact on vulnerable areas and groups in advance.
2020, 45(6): 836-845.
doi: 10.13203/j.whugis20200153
Abstract:
After the outbreak of coronavirus disease 2019(COVID-19), there are large amounts of data related to spatiotemporal information. It is difficult for the epidemic analysis with geographic spatiotemporal model to take the data of character relationship and events into consideration.Therefore, a method of using geographic knowledge graph and interactive visualization to analyze the epidemic situation of COVID-19 is proposed. First, the entity and relationship types of patients are defined, the event semantic model and event relationship classification are proposed, and according to different types of data, knowledge extraction and knowledge representation methods are designed, and the knowledge graph of patients' spatiotemporal information is constructed.Then, from the macro and micro level of the task of epidemic situation control, an analysis frame combining semantic network and visual analysis model is proposed. Finally, an experimental analysis system is built, which uses the data of confirmed patients from COVID-19 to carry out the experiment on the situation analysis of COVID-19 through multi-view collaborative interaction analysis such as map distribution visualization, knowledge graph visualization and trajectory visualization. The experiment has proved that it can provide a new way to analyze the epidemic situation from the aspects of real-time situation monitoring, patient relationship analysis, high-risk population analysis and regional control situation analysis.
After the outbreak of coronavirus disease 2019(COVID-19), there are large amounts of data related to spatiotemporal information. It is difficult for the epidemic analysis with geographic spatiotemporal model to take the data of character relationship and events into consideration.Therefore, a method of using geographic knowledge graph and interactive visualization to analyze the epidemic situation of COVID-19 is proposed. First, the entity and relationship types of patients are defined, the event semantic model and event relationship classification are proposed, and according to different types of data, knowledge extraction and knowledge representation methods are designed, and the knowledge graph of patients' spatiotemporal information is constructed.Then, from the macro and micro level of the task of epidemic situation control, an analysis frame combining semantic network and visual analysis model is proposed. Finally, an experimental analysis system is built, which uses the data of confirmed patients from COVID-19 to carry out the experiment on the situation analysis of COVID-19 through multi-view collaborative interaction analysis such as map distribution visualization, knowledge graph visualization and trajectory visualization. The experiment has proved that it can provide a new way to analyze the epidemic situation from the aspects of real-time situation monitoring, patient relationship analysis, high-risk population analysis and regional control situation analysis.
2020, 45(6): 846-853.
doi: 10.13203/j.whugis20200225
Abstract:
Chest CT(computed tomography) imaging diagnosis is one of the main diagnostic methods for the coronavirus disease 2019(COVID-19). Deep learning technologies such as convolutional neural networks are widely used in medical image processing because of their powerful nonlinear modeling capabilities.A neural network and digital image processing technology is used to design a lightweight COVID-19 classification model based on intra-volume and inter-volume attention mechanisms. Based on this model, we developed a new COVID-19 intelligent diagnosis system with a set of diagnostic functions, lesion segmentation functions and lung and pixel distribution histogram functions. We collected CT images of lungs from 247 patients with COVID-19, 152 other patients with pneumonia and 92 healthy people from the People's Hospital of Wuhan University and made them as training data sets for network training. The experimental results show that the accuracy of our proposed method on the screening task and the degree grading task on the validation set reached 88.63% and 89.65%, respectively, and the average diagnosis time per person was shortened to 0.4 seconds in the algorithm module, which has important application value.
Chest CT(computed tomography) imaging diagnosis is one of the main diagnostic methods for the coronavirus disease 2019(COVID-19). Deep learning technologies such as convolutional neural networks are widely used in medical image processing because of their powerful nonlinear modeling capabilities.A neural network and digital image processing technology is used to design a lightweight COVID-19 classification model based on intra-volume and inter-volume attention mechanisms. Based on this model, we developed a new COVID-19 intelligent diagnosis system with a set of diagnostic functions, lesion segmentation functions and lung and pixel distribution histogram functions. We collected CT images of lungs from 247 patients with COVID-19, 152 other patients with pneumonia and 92 healthy people from the People's Hospital of Wuhan University and made them as training data sets for network training. The experimental results show that the accuracy of our proposed method on the screening task and the degree grading task on the validation set reached 88.63% and 89.65%, respectively, and the average diagnosis time per person was shortened to 0.4 seconds in the algorithm module, which has important application value.
2020, 45(6): 862-869.
doi: 10.13203/j.whugis20190385
Abstract:
With the advancement of deep space exploration, the research focus gradually shifts from earth moon and terrestrial planets, such as Mars and Mercury, to gaseous planets. In the Jupiter exploration mission, the development of the precise orbit determination software is an important tool for simulation at the mission planning and processing the radio tracking data during task implementation. It is an important scientific problem to solve the Jupiter gravity field model and the rotation orientation model. We developed a software named as Jupiter gravity recovery and analysis software (JUPGREAS) with independent intellectual property rights. In order to cross-validate check, we compared the results of orbit propagation and precise orbit determination (POD) with the well-known software GEODYN-II (geodetic parameter estimation and precision orbit determination system-II). The difference of the orbital prediction is at 10-4 m after 7 days. For POD, the position difference is about 1.84 m under the white noise of 1 mm/s. Meanwhile, we analyzed the Jupiter gravity field recovering with the help of the data from Chinese VLBI (very long baseline interferometry) network (CVN). Gaussian white noise of 0.5 ns and 0.5 mm/s is added to the VLBI delay and the two-way Doppler observation, respectively. It is found that the VLBI delay data from CVN could improve the accuracy of the harmonic coefficients of the Jupiter gravity field and the orbital errors is about 0.822 m.
With the advancement of deep space exploration, the research focus gradually shifts from earth moon and terrestrial planets, such as Mars and Mercury, to gaseous planets. In the Jupiter exploration mission, the development of the precise orbit determination software is an important tool for simulation at the mission planning and processing the radio tracking data during task implementation. It is an important scientific problem to solve the Jupiter gravity field model and the rotation orientation model. We developed a software named as Jupiter gravity recovery and analysis software (JUPGREAS) with independent intellectual property rights. In order to cross-validate check, we compared the results of orbit propagation and precise orbit determination (POD) with the well-known software GEODYN-II (geodetic parameter estimation and precision orbit determination system-II). The difference of the orbital prediction is at 10-4 m after 7 days. For POD, the position difference is about 1.84 m under the white noise of 1 mm/s. Meanwhile, we analyzed the Jupiter gravity field recovering with the help of the data from Chinese VLBI (very long baseline interferometry) network (CVN). Gaussian white noise of 0.5 ns and 0.5 mm/s is added to the VLBI delay and the two-way Doppler observation, respectively. It is found that the VLBI delay data from CVN could improve the accuracy of the harmonic coefficients of the Jupiter gravity field and the orbital errors is about 0.822 m.
2020, 45(6): 870-878.
doi: 10.13203/j.whugis20180472
Abstract:
The tidal correction on data recorded with FG5 absolute gravimeter is investigated in the study using gravity tides observed with a superconducting gravimeter iGrav-012 installed at Beijing Changping station, National Institute of Metrology, China. The difference between measured synthetic gravity tidal and theoretical synthetic gravity tidal obtained by high precision superconducting gravimeter is analyzed. Two kinds of measured data obtained by iGrav-012 superconducting gravimeter and FG5X-249 absolute gravimeter were used to verify the correction effect of measured tides.Numerical results show that the difference between the observed gravity tides and the theoretical gravity tides considering ocean loading effect is less than 1 μGal, which is lower than the observed accuracy of FG5 gravimeter. As a conclusion, it is not necessary to consider the tidal correction with observed tides from the view of the observed accuracy. However, the results from the FG5X-249 absolute gravimeter indicate that different methods of tidal correction can change the absolute gravity value itself. Although this change is very small and at the order of 0.1 μGal, tidal correction using observed tide is still recommended in precise absolute gravimetry.
The tidal correction on data recorded with FG5 absolute gravimeter is investigated in the study using gravity tides observed with a superconducting gravimeter iGrav-012 installed at Beijing Changping station, National Institute of Metrology, China. The difference between measured synthetic gravity tidal and theoretical synthetic gravity tidal obtained by high precision superconducting gravimeter is analyzed. Two kinds of measured data obtained by iGrav-012 superconducting gravimeter and FG5X-249 absolute gravimeter were used to verify the correction effect of measured tides.Numerical results show that the difference between the observed gravity tides and the theoretical gravity tides considering ocean loading effect is less than 1 μGal, which is lower than the observed accuracy of FG5 gravimeter. As a conclusion, it is not necessary to consider the tidal correction with observed tides from the view of the observed accuracy. However, the results from the FG5X-249 absolute gravimeter indicate that different methods of tidal correction can change the absolute gravity value itself. Although this change is very small and at the order of 0.1 μGal, tidal correction using observed tide is still recommended in precise absolute gravimetry.
2020, 45(6): 879-887.
doi: 10.13203/j.whugis20190236
Abstract:
In recent years, wide-swath (WS) interferometric synthetic aperture radar (InSAR) technique that has the potential to produce continental-scale maps has been widely used in geological disaster survey and crustal deformation monitoring. However, the impact of tropospheric delay greatly limits its accuracy in mapping small amounts of ground deformation over large spatial areas. Three common used methods, that is ECMWF (European Centre for Medium-Range Weather Forecasts), GACOS (generic atmospheric correction online service for InSAR) and topography-correlated linear relationship, are evaluated to investigate their statistical performance with WS InSAR time series derived from Envisat ASAR ScanSAR and Sentinel-1 TOPOSAR modes over the western segment of the Altyn Tagh Fault. The results show that the GACOS correction method is superior to the other two methods and performs best in capturing both topography-correlated and turbulent mixing tropospheric delays. For Envisat ASAR and Sentinel-1 datasets, the mean reduction of phase standard deviation after GACOS correction can reach 68.1% and 54.5% respectively. The linear correction method can perform relatively well in large-scale areas with rough topography when vertical atmospheric stratification dominates the tropospheric delay. Due to a lack of ground meteorological observation, ECMWF products with limited spatial and temporal resolution cannot accurately reveal the local details. As a fast, robust and effective online service for tropospheric delay estimation and correction, GACOS products can provide critical and reliable support for global InSAR users in large-scale geological disaster applications.
In recent years, wide-swath (WS) interferometric synthetic aperture radar (InSAR) technique that has the potential to produce continental-scale maps has been widely used in geological disaster survey and crustal deformation monitoring. However, the impact of tropospheric delay greatly limits its accuracy in mapping small amounts of ground deformation over large spatial areas. Three common used methods, that is ECMWF (European Centre for Medium-Range Weather Forecasts), GACOS (generic atmospheric correction online service for InSAR) and topography-correlated linear relationship, are evaluated to investigate their statistical performance with WS InSAR time series derived from Envisat ASAR ScanSAR and Sentinel-1 TOPOSAR modes over the western segment of the Altyn Tagh Fault. The results show that the GACOS correction method is superior to the other two methods and performs best in capturing both topography-correlated and turbulent mixing tropospheric delays. For Envisat ASAR and Sentinel-1 datasets, the mean reduction of phase standard deviation after GACOS correction can reach 68.1% and 54.5% respectively. The linear correction method can perform relatively well in large-scale areas with rough topography when vertical atmospheric stratification dominates the tropospheric delay. Due to a lack of ground meteorological observation, ECMWF products with limited spatial and temporal resolution cannot accurately reveal the local details. As a fast, robust and effective online service for tropospheric delay estimation and correction, GACOS products can provide critical and reliable support for global InSAR users in large-scale geological disaster applications.
2020, 45(6): 888-894.
doi: 10.13203/j.whugis20180457
Abstract:
With the rapid development of urbanization, the changes of urban micro-environment caused by the underlying surface are becoming obvious. The influence of roof structure on the single physical field (such as wind field or temperature field) in the urban micro-environment has been emphasized, while the coupling of two physical fields has been neglected. This paper introduces a model to study the effect of building roof shape on urban micro-environment and the model includes two sub-models, that is computational fluid dynamics (CFD) model and radiation (RAD) model. The two-dimensional numerical model was constructed with four roof-shaped structures and three typical solar incidence angles to simulate the micro-environment in street canyons. Then, the mathematical-physical model was validated by comparing the results of wind tunnel experiment. The results show that the model can simulate urban micro-environment. Moreover, flat roof buildings are more conducive to mitigating the urban heat island effect, and the canyon heat island effect formed by triangular roofs is the strongest, while the downward roof buildings are bad for the heat diffusion on the windward side.
With the rapid development of urbanization, the changes of urban micro-environment caused by the underlying surface are becoming obvious. The influence of roof structure on the single physical field (such as wind field or temperature field) in the urban micro-environment has been emphasized, while the coupling of two physical fields has been neglected. This paper introduces a model to study the effect of building roof shape on urban micro-environment and the model includes two sub-models, that is computational fluid dynamics (CFD) model and radiation (RAD) model. The two-dimensional numerical model was constructed with four roof-shaped structures and three typical solar incidence angles to simulate the micro-environment in street canyons. Then, the mathematical-physical model was validated by comparing the results of wind tunnel experiment. The results show that the model can simulate urban micro-environment. Moreover, flat roof buildings are more conducive to mitigating the urban heat island effect, and the canyon heat island effect formed by triangular roofs is the strongest, while the downward roof buildings are bad for the heat diffusion on the windward side.
2020, 45(6): 895-903.
doi: 10.13203/j.whugis20200253
Abstract:
As a new type of remote sensing sensor, unmanned aerial vehicle (UAV) has been used in various fields such as medical treatment, transportation, environmental monitoring, disaster warning, animal protection and military increasingly. Since UAV images are acquired from multiple flying altitudes, perspectives with high speed, objects in UAV images have various scales and perspectives with different distributions, which brings a series of problems to object detection in UAV images.To address these problems, we propose an object detection method based on multi-scale dilated convolutional neural network. It improves existing detection methods by a creative multi-scale dilated convolutional module which facilitates the whole network to learn deep features with increased field of view perception and further improves the performance of object detection in UAV images.We adopt three comparative experiments on base network and our proposed method. And experimental results show that our proposed network has a high precision and recall for object detection in UAV images. Moreover, objects are detected with high performance in multiple perspectives, various scales and complex backgrounds, which indicates the effectiveness and robustness of our method.Object detection in UAV image is significant in both civil and military fields. However, existing methods are limited with objects in multiple perspectives, scales and backgrounds.Our proposed method improves the performance of existing networks by dilated convolutional operator. Experimental results demonstrate the effectiveness and robustness of the proposed method.
As a new type of remote sensing sensor, unmanned aerial vehicle (UAV) has been used in various fields such as medical treatment, transportation, environmental monitoring, disaster warning, animal protection and military increasingly. Since UAV images are acquired from multiple flying altitudes, perspectives with high speed, objects in UAV images have various scales and perspectives with different distributions, which brings a series of problems to object detection in UAV images.To address these problems, we propose an object detection method based on multi-scale dilated convolutional neural network. It improves existing detection methods by a creative multi-scale dilated convolutional module which facilitates the whole network to learn deep features with increased field of view perception and further improves the performance of object detection in UAV images.We adopt three comparative experiments on base network and our proposed method. And experimental results show that our proposed network has a high precision and recall for object detection in UAV images. Moreover, objects are detected with high performance in multiple perspectives, various scales and complex backgrounds, which indicates the effectiveness and robustness of our method.Object detection in UAV image is significant in both civil and military fields. However, existing methods are limited with objects in multiple perspectives, scales and backgrounds.Our proposed method improves the performance of existing networks by dilated convolutional operator. Experimental results demonstrate the effectiveness and robustness of the proposed method.
2020, 45(6): 904-913.
doi: 10.13203/j.whugis20180412
Abstract:
Today, Sentinel-1A data with terrain observation with progressive scanning (TOPS) imaging mode is increasingly used in earth observation aiming at consistent monitoring of surface change and its deformation. However, due to the limited accuracy of coarse co-registration, spectral aliasing along the azimuth direction enables the presence of phase jumping in overlapping area of neighboring bursts. Although geometrical co-registration in conjunction with enhanced spectral diversity (ESD) is proven to be a feasible strategy to correct such error and has been widely used in some open source softwares (e.g. DORIS (Delft object-oriented radar interferometric software), SNAP (sentinel applications platform), ISCE (InSAR scientific computing environment)), the performance of ESD is still not satisfactory and relies strongly on spatiotemporal decorrelation. Given that decorrelation is generally quantized by interferometric coherence, this paper presents and assesses a new methodology to improve the accuracy of ESD by fully exploring the high coherent targets. Specifically, this method focuses on mitigating the spatiotemporal decorrelation in fine co-registration procedures by:(1) Selecting stable targets with moderate and high coherence using accurate coherence estimation. (2) Maximizing coherence magnitude by optimal interferometric subset chosen from Dijkstra algorithm. We compare and test this method against current favorites based on single master image and network-based enhanced spectral diversity (NESD), and the experimental results demonstrate the value of our method. It can make up for the shortage of NESD method.
Today, Sentinel-1A data with terrain observation with progressive scanning (TOPS) imaging mode is increasingly used in earth observation aiming at consistent monitoring of surface change and its deformation. However, due to the limited accuracy of coarse co-registration, spectral aliasing along the azimuth direction enables the presence of phase jumping in overlapping area of neighboring bursts. Although geometrical co-registration in conjunction with enhanced spectral diversity (ESD) is proven to be a feasible strategy to correct such error and has been widely used in some open source softwares (e.g. DORIS (Delft object-oriented radar interferometric software), SNAP (sentinel applications platform), ISCE (InSAR scientific computing environment)), the performance of ESD is still not satisfactory and relies strongly on spatiotemporal decorrelation. Given that decorrelation is generally quantized by interferometric coherence, this paper presents and assesses a new methodology to improve the accuracy of ESD by fully exploring the high coherent targets. Specifically, this method focuses on mitigating the spatiotemporal decorrelation in fine co-registration procedures by:(1) Selecting stable targets with moderate and high coherence using accurate coherence estimation. (2) Maximizing coherence magnitude by optimal interferometric subset chosen from Dijkstra algorithm. We compare and test this method against current favorites based on single master image and network-based enhanced spectral diversity (NESD), and the experimental results demonstrate the value of our method. It can make up for the shortage of NESD method.
2020, 45(6): 914-922.
doi: 10.13203/j.whugis20190034
Abstract:
The simplified aerosol retrieval algorithm (SARA) gets rid of the dependence of traditional aerosol optical depth (AOD) inversion algorithm on the lookup table, and it can get great inversion effect in both dark surface area and bright surface area.Due to the spatial resolution of AOD obtained from the data of moderate resolution imaging spectroradiometer (MODIS) is insufficient, the AOD inversion is carried out with GF-1 wide field of view(WFV)data based on SARA. The retrieved AODs show a high consistency with ground-based AOD measurements, with average correlation coefficient 0.962, root mean square error 0.073, mean absolute error 0.051 and expected error 88.6%. Compared with the MODIS aerosol products in the same period, the inversion results are more consistent in space and have higher spatial coverage, resolution and accuracy. Algorithm suitability study shows that the inversion error caused by the observation geometry and radiometric calibration error of GF-1 WFV camera is small, the absolute error is within 0.04, and the relative error is within 10%.
The simplified aerosol retrieval algorithm (SARA) gets rid of the dependence of traditional aerosol optical depth (AOD) inversion algorithm on the lookup table, and it can get great inversion effect in both dark surface area and bright surface area.Due to the spatial resolution of AOD obtained from the data of moderate resolution imaging spectroradiometer (MODIS) is insufficient, the AOD inversion is carried out with GF-1 wide field of view(WFV)data based on SARA. The retrieved AODs show a high consistency with ground-based AOD measurements, with average correlation coefficient 0.962, root mean square error 0.073, mean absolute error 0.051 and expected error 88.6%. Compared with the MODIS aerosol products in the same period, the inversion results are more consistent in space and have higher spatial coverage, resolution and accuracy. Algorithm suitability study shows that the inversion error caused by the observation geometry and radiometric calibration error of GF-1 WFV camera is small, the absolute error is within 0.04, and the relative error is within 10%.
2020, 45(6): 923-932.
doi: 10.13203/j.whugis20190060
Abstract:
Landslide evolution is a long-term and complex process. Geo-hazards have different characteristics in various data, and have certain applicability in different stages of landslide. This paper applies different characteristics and applicability of various data sources in landslide by the multi-source data fusion method. The deformation and failure characteristics and temporal and spatial evolution law of Huangnibazi landslide during its dynamic evolution from pre-sliding to mid-sliding to post-sliding are studied. The results show that the deformation and failure process of Huangnibazi landslide can be divided into four stages:start-up stage, accelerated deformation (accelerated sliding) stage, front expansion (decelerated sliding) stage and gradual stabilization stage. Huangnibazi landslide is a creep-pull-split landslide formed under the influence of self-weight, rainfall infiltration, earthquake and vibration caused by human engineering activities. The application of various data in different stages of landslide is summarized:interferometric synthetic aperture radar(InSAR) technology, optical image and topographic data are used to determine potential landslide body before sliding. Optical remote sensing image is used to observe the overall deformation and trend of landslide accumulation body during sliding. Global positioning system (GPS) is used to continuously observe local deformation after sliding. Field investigation is used to determine the characteristics of geological hazard body.
Landslide evolution is a long-term and complex process. Geo-hazards have different characteristics in various data, and have certain applicability in different stages of landslide. This paper applies different characteristics and applicability of various data sources in landslide by the multi-source data fusion method. The deformation and failure characteristics and temporal and spatial evolution law of Huangnibazi landslide during its dynamic evolution from pre-sliding to mid-sliding to post-sliding are studied. The results show that the deformation and failure process of Huangnibazi landslide can be divided into four stages:start-up stage, accelerated deformation (accelerated sliding) stage, front expansion (decelerated sliding) stage and gradual stabilization stage. Huangnibazi landslide is a creep-pull-split landslide formed under the influence of self-weight, rainfall infiltration, earthquake and vibration caused by human engineering activities. The application of various data in different stages of landslide is summarized:interferometric synthetic aperture radar(InSAR) technology, optical image and topographic data are used to determine potential landslide body before sliding. Optical remote sensing image is used to observe the overall deformation and trend of landslide accumulation body during sliding. Global positioning system (GPS) is used to continuously observe local deformation after sliding. Field investigation is used to determine the characteristics of geological hazard body.
2020, 45(6): 933-940.
doi: 10.13203/j.whugis20180467
Abstract:
In cities, the main source of air pollutants is vehicle exhaust. It has become an urgent problem for many cities to explore the law of vehicle exhaust diffusion and actively seek for improvement measures. At present, there are many researches on the simulation of vehicle exhaust diffusion, but few on the modeling of diffusion process and volume visualization analysis. This paper integrates California line source dispersion model-4(CALINE4) model and 3DGIS to solve the problem of urban vehicle exhaust diffusion simulation and dynamic visualization. Firstly, a street-scale framework of vehicle exhaust diffusion simulation and visualization is established, and a continuous concentration field is established to express the diffusion processing. A unified data model is used in the semantic space to integrate the data of buildings, roads, trees and dynamic diffusion process data. Secondly, a graphics processing unit-based direct volume rendering method is used to visualize the dynamic diffusion data, and this algorithm is improved in real-time gradient calculation, texture sampling and time-based interpolation. Finally, this paper analyzes the vehicle exhaust diffusion of a road in the central business district area of Beijing. The experimental results show that the coupling application of 3DGIS and CALINE4 model can better reveal the spatial and temporal distribution characteristics of urban vehicle exhaust and concentration, which verifies the reliabless and efficiency of the proposed method.
In cities, the main source of air pollutants is vehicle exhaust. It has become an urgent problem for many cities to explore the law of vehicle exhaust diffusion and actively seek for improvement measures. At present, there are many researches on the simulation of vehicle exhaust diffusion, but few on the modeling of diffusion process and volume visualization analysis. This paper integrates California line source dispersion model-4(CALINE4) model and 3DGIS to solve the problem of urban vehicle exhaust diffusion simulation and dynamic visualization. Firstly, a street-scale framework of vehicle exhaust diffusion simulation and visualization is established, and a continuous concentration field is established to express the diffusion processing. A unified data model is used in the semantic space to integrate the data of buildings, roads, trees and dynamic diffusion process data. Secondly, a graphics processing unit-based direct volume rendering method is used to visualize the dynamic diffusion data, and this algorithm is improved in real-time gradient calculation, texture sampling and time-based interpolation. Finally, this paper analyzes the vehicle exhaust diffusion of a road in the central business district area of Beijing. The experimental results show that the coupling application of 3DGIS and CALINE4 model can better reveal the spatial and temporal distribution characteristics of urban vehicle exhaust and concentration, which verifies the reliabless and efficiency of the proposed method.
2020, 45(6): 941-948.
doi: 10.13203/j.whugis20190095
Abstract:
Massive spatial data poses increasing challenges to traditional analysis software. For example, landscape pattern analysis software FRAGSTATS has been unable to process provincial-level high-resolution land cover data. Based on Two-Pass connected component labeling algorithm, this paper provides an improved parallel algorithm with GPU programming to solve the landscape metrics computation problem about massive land use data. This parallel algorithm for massive landscape metrics calculation takes full advantage of a general computer, and focuses on patch perimeter and area calculation. It can also accelerate computation speed by multithreading and iteration times reduction to decrease computation time than traditional serial algorithms. We apply the proposed algorithm and serial algorithm to calculate landscape metrics of the land use classification raster images at different resolutions under patch scale.The experiment result shows great improvement of calculation performance of landscape metrics, and the efficiency has been improved by 5 times comparing with the serial algorithm, which proves that our proposed algorithm is a better choice for landscape analysis of massive data.
Massive spatial data poses increasing challenges to traditional analysis software. For example, landscape pattern analysis software FRAGSTATS has been unable to process provincial-level high-resolution land cover data. Based on Two-Pass connected component labeling algorithm, this paper provides an improved parallel algorithm with GPU programming to solve the landscape metrics computation problem about massive land use data. This parallel algorithm for massive landscape metrics calculation takes full advantage of a general computer, and focuses on patch perimeter and area calculation. It can also accelerate computation speed by multithreading and iteration times reduction to decrease computation time than traditional serial algorithms. We apply the proposed algorithm and serial algorithm to calculate landscape metrics of the land use classification raster images at different resolutions under patch scale.The experiment result shows great improvement of calculation performance of landscape metrics, and the efficiency has been improved by 5 times comparing with the serial algorithm, which proves that our proposed algorithm is a better choice for landscape analysis of massive data.
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