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To overcome the difficulties of quantitatively optimizing and evaluating evacuation guidance systems, we proposed a perception model based on virtual reality (VR) experiments and the social force model (SFM). We used VR and eye tracking devices to carry out experiments. The VR experiment data was mainly used for three purposes: to determine the parameter values of the perception model, to optimize the evacuation guidance system by quantitative analysis, and to validate the perception model. Additionally, we compared the VR experimental and model simulation results before and after the optimization to quantitatively assess the improvement in the optimized evacuation guidance system. The results showed that our model can effectively simulate the perception behaviors of evacuees on the evacuation guidance system and it can quantitatively evaluate different evacuation guidance system schemes. The model simulations showed that the optimized evacuation guidance system improved the evacuation efficiency, with the average escape time and distance of the two starting positions reduced by 37% and 28%, respectively.
Lin Huang; Jianhua Gong; Wenhang Li. A Perception Model for Optimizing and Evaluating Evacuation Guidance Systems. ISPRS International Journal of Geo-Information 2021, 10, 54 .
AMA StyleLin Huang, Jianhua Gong, Wenhang Li. A Perception Model for Optimizing and Evaluating Evacuation Guidance Systems. ISPRS International Journal of Geo-Information. 2021; 10 (2):54.
Chicago/Turabian StyleLin Huang; Jianhua Gong; Wenhang Li. 2021. "A Perception Model for Optimizing and Evaluating Evacuation Guidance Systems." ISPRS International Journal of Geo-Information 10, no. 2: 54.
Solar3D is an open-source software application designed to interactively calculate solar irradiation on three-dimensional (3D) surfaces in a virtual environment constructed with combinations of 3D-city models, digital elevation models (DEMs), digital surface models (DSMs) and feature layers. The GRASS GIS r.sun solar radiation model computes solar irradiation based on two-dimensional (2D) raster maps for a given day, latitude, surface and atmospheric conditions. With the increasing availability of 3D-city models and demand for solar energy, there is an urgent need for better tools to computes solar radiation directly with 3D-city models. Solar3D extends the GRASS GIS r.sun model from 2D to 3D by feeding the model with input, including surface slope, aspect and time-resolved shading, which is derived directly from the 3D scene using computer graphics techniques. To summarize, Solar3D offers several new features that—as a whole—distinguish this novel approach from existing 3D solar irradiation tools in the following ways. (1) Solar3D can consume massive heterogeneous 3D-city models, including massive 3D-city models such as oblique airborne photogrammetry-based 3D-city models (OAP3Ds or integrated meshes); (2) Solar3D can perform near real-time pointwise calculation for duration from daily to annual; (3) Solar3D can integrate and interactively explore large-scale heterogeneous geospatial data; (4) Solar3D can calculate solar irradiation at arbitrary surface positions including on rooftops, facades and the ground.
Jianming Liang; Jianhua Gong; Xiuping Xie; Jun Sun. Solar3D: An Open-Source Tool for Estimating Solar Radiation in Urban Environments. ISPRS International Journal of Geo-Information 2020, 9, 524 .
AMA StyleJianming Liang, Jianhua Gong, Xiuping Xie, Jun Sun. Solar3D: An Open-Source Tool for Estimating Solar Radiation in Urban Environments. ISPRS International Journal of Geo-Information. 2020; 9 (9):524.
Chicago/Turabian StyleJianming Liang; Jianhua Gong; Xiuping Xie; Jun Sun. 2020. "Solar3D: An Open-Source Tool for Estimating Solar Radiation in Urban Environments." ISPRS International Journal of Geo-Information 9, no. 9: 524.
Transport emissions and street dust are important sources of summertime air pollution in urban centers. Street greening and buildings have an influence on the diffusion of air pollution from streets. For field measurements, many studies have analyzed the effect of street green space arrangement on the diffusion of air pollution, but these studies have neglected the patterns at the landscape scale. Other studies have analyzed the effects of the large scale of green space on air pollution, but the vertical distribution of street buildings and greening has rarely been considered. In this study, we analyzed the impact of the vertical distribution of urban street green space on summertime air pollution in urban centers on the urban scale for the first time by using a deep-learning method to extract the vertical distribution of street greening and buildings from street view image data. A total of 687,354 street view images were collected. The green index and building index were proposed to quantify the street greening and street buildings. The multilevel regression method was used to analyze the association between the street green index, building index and air pollution indexes. For the cases in this study, including the central urban areas of Beijing, Shanghai and Nanjing, our multilevel regressions results suggested that, in the central area of the city, the vertical distribution of street greening and buildings within a certain range of the monitoring site is association with the summertime air pollution index of the monitoring site. There was a significant negative association between the street greening and air pollution indexes (radius = 1–2 km, NO2, p = 0.042; radius = 3–4 km, AQI, p = 0.034; PM10, p = 0.028). The street length within a certain range of the monitoring site has a positive association with the air pollution indexes (radius = 1–2 km, AQI, p = 0.072; PM10, p = 0.062). With the increase of the distance between streets and the monitoring sites, the association between streets and air pollution indexes decreases. Our findings on the association between the vertical structure of street greening, street buildings and summertime air pollution in urban centers can support urban street planning.
Dong Wu; Jianhua Gong; Jianming Liang; Jin Sun; Guoyong Zhang. Analyzing the Influence of Urban Street Greening and Street Buildings on Summertime Air Pollution Based on Street View Image Data. ISPRS International Journal of Geo-Information 2020, 9, 500 .
AMA StyleDong Wu, Jianhua Gong, Jianming Liang, Jin Sun, Guoyong Zhang. Analyzing the Influence of Urban Street Greening and Street Buildings on Summertime Air Pollution Based on Street View Image Data. ISPRS International Journal of Geo-Information. 2020; 9 (9):500.
Chicago/Turabian StyleDong Wu; Jianhua Gong; Jianming Liang; Jin Sun; Guoyong Zhang. 2020. "Analyzing the Influence of Urban Street Greening and Street Buildings on Summertime Air Pollution Based on Street View Image Data." ISPRS International Journal of Geo-Information 9, no. 9: 500.
Solar3D is an open-source software application designed to interactively calculate solar irradiation at three-dimensional (3D) surfaces in a virtual environment constructed with combinations of 3D city models, digital elevation models (DEMs), digital surface models (DSMs) and feature layers. The GRASS GIS r.sun solar radiation model computes solar irradiation based on two-dimensional (2D) raster maps for given day, latitude, surface and atmospheric conditions. With the increasing availability of 3D city models and demand for solar energy, there is an urgent need for better tools to computes solar radiation directly with 3D city models. Solar3D extends GRASS GIS r.sun from 2D to 3D by feeding the model with input, including surface slope, aspect and time-resolved shading, that is derived directly from the 3D scene using computer graphics techniques. To summarize, Solar3D offers several new features which, as a whole, distinguish itself from existing 3D solar irradiation tools: (1) the ability to consume massive heterogeneous 3D city models, including massive 3D city models such as oblique airborne photogrammetry-based 3D city models (OAP3Ds or integrated meshes); (2) the ability to perform near real-time pointwise calculation for duration from daily to annual; (3) the ability to integrate and interactively explore large-scale heterogeneous geospatial data. (4) the ability to calculate solar irradiation at arbitrary surface positions including at rooftops, facades and the ground. Solar3D is publicly available at https://github.com/jian9695/Solar3D.
Jianming Liang; Jianhua Gong; Xiuping Xie; Jun Sun. Solar3D: A 3D Extension of GRASS GIS r.sun for Estimating Solar Radiation in Urban Environments. 2020, 1 .
AMA StyleJianming Liang, Jianhua Gong, Xiuping Xie, Jun Sun. Solar3D: A 3D Extension of GRASS GIS r.sun for Estimating Solar Radiation in Urban Environments. . 2020; ():1.
Chicago/Turabian StyleJianming Liang; Jianhua Gong; Xiuping Xie; Jun Sun. 2020. "Solar3D: A 3D Extension of GRASS GIS r.sun for Estimating Solar Radiation in Urban Environments." , no. : 1.
Ambient PM2.5 pollution has been a major environmental concern in recent years. Beijing, the capital of China, is enduring frequent and severe PM2.5 pollution. In this study, 186 valid PM2.5 pollution episodes during the 2014-2017 period were identified and classified into four categories according to the mechanism of pollution formation and evolution. Category I often occurs in autumn, winter and early spring, depending on accumulation during stagnant weather. Category II featured by photochemistry is dominant in summer, and category III caused by dust storms occasionally occurs in spring. Category IV represents a combination during transition periods. Interannual variations show that particulate pollution decreased from 2014 to 2017, and the decline in categories I and II played the most important role. To further understand the PM2.5 pollution patterns in Beijing, the temporal and spatial characteristics and relationships between PM2.5 levels and meteorological features were analyzed. Category I is the main pollution type that brings forth heavy or severe pollution and has the longest duration in those cases, while category II often leads to light or moderate pollution. There is a north-south gap in the PM2.5 levels in Beijing. The high-level pollution in category I tends to evolve northward, while the low-level pollution in category I and category II pollution tend to evolve southward and widen the north-south gap, affected by the regional transport and more local emissions in the south. Additionally, the relationship between the concentrations and meteorology also vary with the pollution categories. High relative humidity, low wind speed and low boundary layer height are likely to lead to category I or II pollution, but category III requires high winds. These results provide insights into the annual tendency and characteristics of Beijing's particulate pollution in recent years.
Jin Sun; Jianhua Gong; Jieping Zhou; Jiantao Liu; Jianming Liang. Analysis of PM2.5 pollution episodes in Beijing from 2014 to 2017: Classification, interannual variations and associations with meteorological features. Atmospheric Environment 2019, 213, 384 -394.
AMA StyleJin Sun, Jianhua Gong, Jieping Zhou, Jiantao Liu, Jianming Liang. Analysis of PM2.5 pollution episodes in Beijing from 2014 to 2017: Classification, interannual variations and associations with meteorological features. Atmospheric Environment. 2019; 213 ():384-394.
Chicago/Turabian StyleJin Sun; Jianhua Gong; Jieping Zhou; Jiantao Liu; Jianming Liang. 2019. "Analysis of PM2.5 pollution episodes in Beijing from 2014 to 2017: Classification, interannual variations and associations with meteorological features." Atmospheric Environment 213, no. : 384-394.
Jianming Liang; Jianhua Gong; Yu Liu. Introduction to the Special Issue: “State-of-the-Art Virtual/Augmented Reality and 3D Modeling Techniques for Virtual Urban Geographic Experiments”. ISPRS International Journal of Geo-Information 2018, 7, 366 .
AMA StyleJianming Liang, Jianhua Gong, Yu Liu. Introduction to the Special Issue: “State-of-the-Art Virtual/Augmented Reality and 3D Modeling Techniques for Virtual Urban Geographic Experiments”. ISPRS International Journal of Geo-Information. 2018; 7 (9):366.
Chicago/Turabian StyleJianming Liang; Jianhua Gong; Yu Liu. 2018. "Introduction to the Special Issue: “State-of-the-Art Virtual/Augmented Reality and 3D Modeling Techniques for Virtual Urban Geographic Experiments”." ISPRS International Journal of Geo-Information 7, no. 9: 366.
Virtual geographic environments (VGEs) are extensively used to explore the relationship between humans and environments. Crowd simulation provides a method for VGEs to represent crowd behaviors that are observed in the real world. The social force model (SFM) can simulate interactions among individuals, but it has not sufficiently accounted for inter-group and intra-group behaviors which are important components of crowd dynamics. We present the social group force model (SGFM), based on an extended SFM, to simulate group behaviors in VGEs with focuses on the avoiding behaviors among different social groups and the coordinate behaviors among subgroups that belong to one social group. In our model, psychological repulsions between social groups make them avoid with the whole group and group members can stick together as much as possible; while social groups are separated into several subgroups, the rear subgroups try to catch up and keep the whole group cohesive. We compare the simulation results of the SGFM with the extended SFM and the phenomena in videos. Then we discuss the function of Virtual Reality (VR) in crowd simulation visualization. The results indicate that the SGFM can enhance social group behaviors in crowd dynamics.
Lin Huang; Jianhua Gong; Wenhang Li; Tao Xu; Shen Shen; Jianming Liang; Quanlong Feng; Dong Zhang; Jun Sun. Social Force Model-Based Group Behavior Simulation in Virtual Geographic Environments. ISPRS International Journal of Geo-Information 2018, 7, 79 .
AMA StyleLin Huang, Jianhua Gong, Wenhang Li, Tao Xu, Shen Shen, Jianming Liang, Quanlong Feng, Dong Zhang, Jun Sun. Social Force Model-Based Group Behavior Simulation in Virtual Geographic Environments. ISPRS International Journal of Geo-Information. 2018; 7 (2):79.
Chicago/Turabian StyleLin Huang; Jianhua Gong; Wenhang Li; Tao Xu; Shen Shen; Jianming Liang; Quanlong Feng; Dong Zhang; Jun Sun. 2018. "Social Force Model-Based Group Behavior Simulation in Virtual Geographic Environments." ISPRS International Journal of Geo-Information 7, no. 2: 79.
Due to their strong immersion and real-time interactivity, helmet-mounted virtual reality (VR) devices are becoming increasingly popular. Based on these devices, an immersive virtual geographic environment (VGE) provides a promising method for research into crowd behavior in an emergency. However, the current cheaper helmet-mounted VR devices are not popular enough, and will continue to coexist with personal computer (PC)-based systems for a long time. Therefore, a heterogeneous distributed virtual geographic environment (HDVGE) could be a feasible solution to the heterogeneous problems caused by various types of clients, and support the implementation of spatiotemporal crowd behavior experiments with large numbers of concurrent participants. In this study, we developed an HDVGE framework, and put forward a set of design principles to define the similarities between the real world and the VGE. We discussed the HDVGE architecture, and proposed an abstract interaction layer, a protocol-based interaction algorithm, and an adjusted dead reckoning algorithm to solve the heterogeneous distributed problems. We then implemented an HDVGE prototype system focusing on subway fire evacuation experiments. Two types of clients are considered in the system: PC, and all-in-one VR. Finally, we evaluated the performances of the prototype system and the key algorithms. The results showed that in a low-latency local area network (LAN) environment, the prototype system can smoothly support 90 concurrent users consisting of PC and all-in-one VR clients. HDVGE provides a feasible solution for studying not only spatiotemporal crowd behaviors in normal conditions, but also evacuation behaviors in emergency conditions such as fires and earthquakes. HDVGE could also serve as a new means of obtaining observational data about individual and group behavior in support of human geography research.
Shen Shen; Jianhua Gong; Jianming Liang; Wenhang Li; Dong Zhang; Lin Huang; Guoyong Zhang. A Heterogeneous Distributed Virtual Geographic Environment—Potential Application in Spatiotemporal Behavior Experiments. ISPRS International Journal of Geo-Information 2018, 7, 54 .
AMA StyleShen Shen, Jianhua Gong, Jianming Liang, Wenhang Li, Dong Zhang, Lin Huang, Guoyong Zhang. A Heterogeneous Distributed Virtual Geographic Environment—Potential Application in Spatiotemporal Behavior Experiments. ISPRS International Journal of Geo-Information. 2018; 7 (2):54.
Chicago/Turabian StyleShen Shen; Jianhua Gong; Jianming Liang; Wenhang Li; Dong Zhang; Lin Huang; Guoyong Zhang. 2018. "A Heterogeneous Distributed Virtual Geographic Environment—Potential Application in Spatiotemporal Behavior Experiments." ISPRS International Journal of Geo-Information 7, no. 2: 54.
Hemispherical (fisheye) photography is a well-established approach for estimating the sky view factor (SVF). High-resolution urban models from LiDAR and oblique airborne photogrammetry can provide continuous SVF estimates over a large urban area, but such data are not always available and are difficult to acquire. Street view panoramas have become widely available in urban areas worldwide: Google Street View (GSV) maintains a global network of panoramas excluding China and several other countries; Baidu Street View (BSV) and Tencent Street View (TSV) focus their panorama acquisition efforts within China, and have covered hundreds of cities therein. In this paper, we approach this issue from a big data perspective by presenting and validating a method for automatic estimation of SVF from massive amounts of street view photographs. Comparisons were made with SVF estimates derived from two independent sources: a LiDAR-based Digital Surface Model (DSM) and an oblique airborne photogrammetry-based 3D city model (OAP3D), resulting in a correlation coefficient of 0.863 and 0.987, respectively. The comparisons demonstrated the capacity of the proposed method to provide reliable SVF estimates. Additionally, we present an application of the proposed method with about 12,000 GSV panoramas to characterize the spatial distribution of SVF over Manhattan Island in New York City. Although this is a proof-of-concept study, it has shown the potential of the proposed approach to assist urban climate and urban planning research. However, further development is needed before this approach can be finally delivered to the urban climate and urban planning communities for practical applications.
Jianming Liang; Jianhua Gong; Jun Sun; Jieping Zhou; Wenhang Li; Yi Li; Jin Liu; Shen Shen. Automatic Sky View Factor Estimation from Street View Photographs—A Big Data Approach. Remote Sensing 2017, 9, 411 .
AMA StyleJianming Liang, Jianhua Gong, Jun Sun, Jieping Zhou, Wenhang Li, Yi Li, Jin Liu, Shen Shen. Automatic Sky View Factor Estimation from Street View Photographs—A Big Data Approach. Remote Sensing. 2017; 9 (5):411.
Chicago/Turabian StyleJianming Liang; Jianhua Gong; Jun Sun; Jieping Zhou; Wenhang Li; Yi Li; Jin Liu; Shen Shen. 2017. "Automatic Sky View Factor Estimation from Street View Photographs—A Big Data Approach." Remote Sensing 9, no. 5: 411.
This paper presents an approach to process raw unmanned aircraft vehicle (UAV) image-derived point clouds for automatically detecting, segmenting and regularizing buildings of complex urban landscapes. For regularizing, we mean the extraction of the building footprints with precise position and details. In the first step, vegetation points were extracted using a support vector machine (SVM) classifier based on vegetation indexes calculated from color information, then the traditional hierarchical stripping classification method was applied to classify and segment individual buildings. In the second step, we first determined the building boundary points with a modified convex hull algorithm. Then, we further segmented these points such that each point was assigned to a fitting line using a line growing algorithm. Then, two mutually perpendicular directions of each individual building were determined through a W-k-means clustering algorithm which used the slop information and principal direction constraints. Eventually, the building edges were regularized to form the final building footprints. Qualitative and quantitative measures were used to evaluate the performance of the proposed approach by comparing the digitized results from ortho images.
Yucheng Dai; Jianhua Gong; Yi Li; Quanlong Feng. Building segmentation and outline extraction from UAV image-derived point clouds by a line growing algorithm. International Journal of Digital Earth 2017, 10, 1077 -1097.
AMA StyleYucheng Dai, Jianhua Gong, Yi Li, Quanlong Feng. Building segmentation and outline extraction from UAV image-derived point clouds by a line growing algorithm. International Journal of Digital Earth. 2017; 10 (11):1077-1097.
Chicago/Turabian StyleYucheng Dai; Jianhua Gong; Yi Li; Quanlong Feng. 2017. "Building segmentation and outline extraction from UAV image-derived point clouds by a line growing algorithm." International Journal of Digital Earth 10, no. 11: 1077-1097.
Few studies have focused on the evacuation of multi-floor classroom buildings in a primary school, a process that differs from evacuations in other buildings. A stair-unit model was proposed to describe the spatial topology of twisting stairwells and to describe the spatial relationship between stairwells and floors. Based on the stair-unit model, a schedule-line model was proposed to calculate evacuation paths in stair-units; a modified algorithm to calculate pedestrian forces were proposed to describe the evacuee movements in stairwells; and a projection strategy was proposed to model the 3-dimensional evacuation process in multi-floor buildings. The simulated processes were compared with a real evacuation drill. The results showed that the simulated process achieved qualitative and quantitative consistencies with the real drill, proving the appropriateness of the proposed models and algorithms. Based on the validation, further simulations were conducted and a few rules for evacuations in stairwells were identified including rules governing the impact of the moment of entering a staircase, the number of students in a class, the stagger strategy, and the layout of grades on different floors on the time in stairwell and the total evacuation duration. The results can be used to mitigate the effects of a fire disaster, and the proposed models and algorithms can also be referenced by evacuation simulation for other multi-floor buildings such as residential buildings.
Wenhang Li; Yi Li; Ping Yu; Jianhua Gong; Shen Shen; Lin Huang; Jianming Liang. Modeling, simulation and analysis of the evacuation process on stairs in a multi-floor classroom building of a primary school. Physica A: Statistical Mechanics and its Applications 2016, 469, 157 -172.
AMA StyleWenhang Li, Yi Li, Ping Yu, Jianhua Gong, Shen Shen, Lin Huang, Jianming Liang. Modeling, simulation and analysis of the evacuation process on stairs in a multi-floor classroom building of a primary school. Physica A: Statistical Mechanics and its Applications. 2016; 469 ():157-172.
Chicago/Turabian StyleWenhang Li; Yi Li; Ping Yu; Jianhua Gong; Shen Shen; Lin Huang; Jianming Liang. 2016. "Modeling, simulation and analysis of the evacuation process on stairs in a multi-floor classroom building of a primary school." Physica A: Statistical Mechanics and its Applications 469, no. : 157-172.
2.5D map is a convenient and efficient approach to exploiting a massive three-dimensional (3D) city model in web GIS. With the rapid development of oblique airborne photogrammetry and photo-based 3D reconstruction, 3D city models are becoming more and more accessible. 3D Geographic Information System (GIS) can support the interactive visualization of massive 3D city models on various platforms and devices. However, the value and accessibility of existing 3D city models can be augmented by integrating them into web-based two-dimensional (2D) GIS applications. In this paper, we present a step-by-step workflow for generating orthorectified oblique images (2.5D maps) from massive 3D city models. The proposed framework can produce 2.5D maps from an arbitrary perspective, defined by the elevation angle and azimuth angle of a virtual orthographic camera. We demonstrate how 2.5D maps can benefit web-based visualization and exploitation of massive 3D city models. We conclude that a 2.5D map is a compact data representation optimized for web data streaming of 3D city models and that geometric analysis of buildings can be effectively conducted on 2.5D maps.
Jianming Liang; Jianhua Gong; Jin Liu; Yuling Zou; Jinming Zhang; Jun Sun; Shuisen Chen. Generating Orthorectified Multi-Perspective 2.5D Maps to Facilitate Web GIS-Based Visualization and Exploitation of Massive 3D City Models. ISPRS International Journal of Geo-Information 2016, 5, 212 .
AMA StyleJianming Liang, Jianhua Gong, Jin Liu, Yuling Zou, Jinming Zhang, Jun Sun, Shuisen Chen. Generating Orthorectified Multi-Perspective 2.5D Maps to Facilitate Web GIS-Based Visualization and Exploitation of Massive 3D City Models. ISPRS International Journal of Geo-Information. 2016; 5 (11):212.
Chicago/Turabian StyleJianming Liang; Jianhua Gong; Jin Liu; Yuling Zou; Jinming Zhang; Jun Sun; Shuisen Chen. 2016. "Generating Orthorectified Multi-Perspective 2.5D Maps to Facilitate Web GIS-Based Visualization and Exploitation of Massive 3D City Models." ISPRS International Journal of Geo-Information 5, no. 11: 212.
Oblique airborne photogrammetry-based three-dimensional (3D) city model (OAP3D) provides a spatially continuous representation of urban landscapes that encompasses buildings, road networks, trees, bushes, water bodies, and topographic features. OAP3D is usually present in the form of a group of unclassified triangular meshes under a multi-resolution data structure. Modifying such a non-separable landscape constitutes a daunting task because manual mesh editing is normally required. In this paper, we present a systematic approach for easily embedding user-generated content into OAP3D. We reduce the complexity of OAP3D modification from a 3D mesh operation to a two-dimensional (2D) raster operation through the following workflow: (1) A region of interest (ROI) is selected to cover the area that is intended to be modified for accommodating user-defined content. (2) Spatial interpolation using a set of manually controlled elevation samples is employed to generate a user-defined digital surface model (DSM), which is used to reform the ROI surface. (3) User-generated objects, for example, artistically painted road textures, procedurally generated water effects, and manually created 3D building models, are overlaid onto the reformed ROI.
Jianming Liang; Shen Shen; Jianhua Gong; Jin Liu; Jinming Zhang. Embedding user-generated content into oblique airborne photogrammetry-based 3D city model. International Journal of Geographical Information Science 2016, 31, 1 -16.
AMA StyleJianming Liang, Shen Shen, Jianhua Gong, Jin Liu, Jinming Zhang. Embedding user-generated content into oblique airborne photogrammetry-based 3D city model. International Journal of Geographical Information Science. 2016; 31 (1):1-16.
Chicago/Turabian StyleJianming Liang; Shen Shen; Jianhua Gong; Jin Liu; Jinming Zhang. 2016. "Embedding user-generated content into oblique airborne photogrammetry-based 3D city model." International Journal of Geographical Information Science 31, no. 1: 1-16.
As an important ecosystem, wetlands play a crucial role in both regional and global environments. Accurate land-cover classification can facilitate the management and understanding of wetlands. Considering the timely and cost-effective characteristics of remote sensing, this technique was used to obtain land-cover information for the Yellow River Delta (YRD) wetland in this investigation. Landsat-8 Operational Land Imager (OLI) sensor data were selected for the data set in this study. A combined approach of multiple end-member spectral mixture analysis (MESMA) and Random Forest (RF) was developed for land-cover classification mapping of the YRD wetland. This study aimed (1) to determine whether the MESMA technique in combination with RF significantly improves the accuracy of classification in complex landscapes such as the YRD wetland, (2) to determine whether the RF classifier shows good performance in land-cover classification of the YRD wetland, and (3) to compare the proposed method with the traditional Maximum Likelihood Classifier (MLC). The proposed hybrid method showed good performance, with an overall accuracy of 89.5% and a kappa coefficient (κ) of 0.88. The inclusion of fractional information derived from MESMA can improve the classification accuracy by 2–3%. In addition, through a comparison with traditional maximum likelihood (ML) methodology, the effectiveness of the proposed approach was evaluated. Overall, the proposed approach in this study can relatively accurately delineate a land-cover classification map of the YRD wetland with Landsat-8 OLI remotely sensed data.
Jiantao Liu; Quanlong Feng; Jianhua Gong; Jieping Zhou; Yi Li. Land-cover classification of the Yellow River Delta wetland based on multiple end-member spectral mixture analysis and a Random Forest classifier. International Journal of Remote Sensing 2016, 37, 1845 -1867.
AMA StyleJiantao Liu, Quanlong Feng, Jianhua Gong, Jieping Zhou, Yi Li. Land-cover classification of the Yellow River Delta wetland based on multiple end-member spectral mixture analysis and a Random Forest classifier. International Journal of Remote Sensing. 2016; 37 (8):1845-1867.
Chicago/Turabian StyleJiantao Liu; Quanlong Feng; Jianhua Gong; Jieping Zhou; Yi Li. 2016. "Land-cover classification of the Yellow River Delta wetland based on multiple end-member spectral mixture analysis and a Random Forest classifier." International Journal of Remote Sensing 37, no. 8: 1845-1867.
Natural deltas can provide human beings with flat and fertile land to be cultivated. It is important to monitor cropland dynamics to provide policy-relevant information for regional sustainable development. This paper utilized Landsat imagery to study the cropland dynamics of the Yellow River Delta during the last three decades. Multi-temporal Landsat data were used to account for the phenological variations of different plants. Several spectral and textural features were adopted to increase the between-class separability. The robust random forest classifier was used to generate the land cover maps of the Yellow River Delta for 1986, 1995, 2005 and 2015. Experimental results indicated that the proposed methodology showed good performance with an average classification accuracy of 89.44%. The spatial-temporal analysis indicated that the cropland area increased from 467.6 km2 in 1986 to 718.5 km2 in 2015 with an average growth rate of 8.65 km2/year. The newly created croplands were mainly due to the reclamation of grassland and bare soil while the losses of croplands were due to abandoned cultivation and urban sprawl. The results demonstrate that a sustainable perspective should be adopted by the decision makers in order to simultaneously maintain food security, industrial development and ecosystem safety.
Quanlong Feng; Jianhua Gong; Jiantao Liu; Yi Li. Monitoring Cropland Dynamics of the Yellow River Delta based on Multi-Temporal Landsat Imagery over 1986 to 2015. Sustainability 2015, 7, 14834 -14858.
AMA StyleQuanlong Feng, Jianhua Gong, Jiantao Liu, Yi Li. Monitoring Cropland Dynamics of the Yellow River Delta based on Multi-Temporal Landsat Imagery over 1986 to 2015. Sustainability. 2015; 7 (11):14834-14858.
Chicago/Turabian StyleQuanlong Feng; Jianhua Gong; Jiantao Liu; Yi Li. 2015. "Monitoring Cropland Dynamics of the Yellow River Delta based on Multi-Temporal Landsat Imagery over 1986 to 2015." Sustainability 7, no. 11: 14834-14858.
Remote sensing is recognized as a valuable tool for flood mapping due to its synoptic view and continuous coverage of the flooding event. This paper proposed a hybrid approach based on multiple endmember spectral analysis (MESMA) and Random Forest classifier to extract inundated areas in Yuyao City in China using medium resolution optical imagery. MESMA was adopted to tackle the mixing pixel problem induced by medium resolution data. Specifically, 35 optimal endmembers were selected to construct a total of 3111 models in the MESMA procedure to derive accurate fraction information. A multi-dimensional feature space was constructed including the normalized difference water index (NDWI), topographical parameters of height, slope, and aspect together with the fraction maps. A Random Forest classifier consisting of 200 decision trees was adopted to classify the post-flood image based on the above multi-features. Experimental results indicated that the proposed method can extract the inundated areas precisely with a classification accuracy of 94% and a Kappa index of 0.88. The inclusion of fraction information can help improve the mapping accuracy with an increase of 2.5%. Moreover, the proposed method also outperformed the maximum likelihood classifier and the NDWI thresholding method. This research provided a useful reference for flood mapping using medium resolution optical remote sensing imagery.
Quanlong Feng; Jianhua Gong; Jiantao Liu; Yi Li. Flood Mapping Based on Multiple Endmember Spectral Mixture Analysis and Random Forest Classifier—The Case of Yuyao, China. Remote Sensing 2015, 7, 12539 -12562.
AMA StyleQuanlong Feng, Jianhua Gong, Jiantao Liu, Yi Li. Flood Mapping Based on Multiple Endmember Spectral Mixture Analysis and Random Forest Classifier—The Case of Yuyao, China. Remote Sensing. 2015; 7 (9):12539-12562.
Chicago/Turabian StyleQuanlong Feng; Jianhua Gong; Jiantao Liu; Yi Li. 2015. "Flood Mapping Based on Multiple Endmember Spectral Mixture Analysis and Random Forest Classifier—The Case of Yuyao, China." Remote Sensing 7, no. 9: 12539-12562.
Flooding is a severe natural hazard, which poses a great threat to human life and property, especially in densely-populated urban areas. As one of the fastest developing fields in remote sensing applications, an unmanned aerial vehicle (UAV) can provide high-resolution data with a great potential for fast and accurate detection of inundated areas under complex urban landscapes. In this research, optical imagery was acquired by a mini-UAV to monitor the serious urban waterlogging in Yuyao, China. Texture features derived from gray-level co-occurrence matrix were included to increase the separability of different ground objects. A Random Forest classifier, consisting of 200 decision trees, was used to extract flooded areas in the spectral-textural feature space. Confusion matrix was used to assess the accuracy of the proposed method. Results indicated the following: (1) Random Forest showed good performance in urban flood mapping with an overall accuracy of 87.3% and a Kappa coefficient of 0.746; (2) the inclusion of texture features improved classification accuracy significantly; (3) Random Forest outperformed maximum likelihood and artificial neural network, and showed a similar performance to support vector machine. The results demonstrate that UAV can provide an ideal platform for urban flood monitoring and the proposed method shows great capability for the accurate extraction of inundated areas.
Quanlong Feng; Jiantao Liu; Jianhua Gong. Urban Flood Mapping Based on Unmanned Aerial Vehicle Remote Sensing and Random Forest Classifier—A Case of Yuyao, China. Water 2015, 7, 1437 -1455.
AMA StyleQuanlong Feng, Jiantao Liu, Jianhua Gong. Urban Flood Mapping Based on Unmanned Aerial Vehicle Remote Sensing and Random Forest Classifier—A Case of Yuyao, China. Water. 2015; 7 (12):1437-1455.
Chicago/Turabian StyleQuanlong Feng; Jiantao Liu; Jianhua Gong. 2015. "Urban Flood Mapping Based on Unmanned Aerial Vehicle Remote Sensing and Random Forest Classifier—A Case of Yuyao, China." Water 7, no. 12: 1437-1455.
Tuberculosis (TB) remains a major public health problem in China, and its incidence shows certain regional disparities. Systematic investigations of the social and environmental factors influencing TB are necessary for the prevention and control of the disease. Data on cases were obtained from the Chinese Center for Disease and Prevention. Social and environmental variables were tabulated to investigate the latent factor structure of the data using exploratory factor analysis (EFA). Partial least square path modeling (PLS-PM) was used to analyze the complex causal relationship and hysteresis effects between the factors and TB prevalence. A geographically weighted regression (GWR) model was used to explore the local association between factors and TB prevalence. EFA and PLS-PM indicated significant associations between TB prevalence and its latent factors. Altitude, longitude, climate, and education burden played an important role; primary industry employment, population density, air quality, and economic level had hysteresis with different lag time; health service and unemployment played a limited role but had limited hysteresis. Additionally, the GWR model showed that each latent factor had different effects on TB prevalence in different areas. It is necessary to formulate regional measures and strategies for TB control and prevention in China according to the local regional effects of specific factors.
Wenyi Sun; Jianhua Gong; Jieping Zhou; Yanlin Zhao; Junxiang Tan; Abdoul Nasser Ibrahim; Yang Zhou. A Spatial, Social and Environmental Study of Tuberculosis in China Using Statistical and GIS Technology. International Journal of Environmental Research and Public Health 2015, 12, 1425 -1448.
AMA StyleWenyi Sun, Jianhua Gong, Jieping Zhou, Yanlin Zhao, Junxiang Tan, Abdoul Nasser Ibrahim, Yang Zhou. A Spatial, Social and Environmental Study of Tuberculosis in China Using Statistical and GIS Technology. International Journal of Environmental Research and Public Health. 2015; 12 (2):1425-1448.
Chicago/Turabian StyleWenyi Sun; Jianhua Gong; Jieping Zhou; Yanlin Zhao; Junxiang Tan; Abdoul Nasser Ibrahim; Yang Zhou. 2015. "A Spatial, Social and Environmental Study of Tuberculosis in China Using Statistical and GIS Technology." International Journal of Environmental Research and Public Health 12, no. 2: 1425-1448.
Unmanned aerial vehicle (UAV) remote sensing has great potential for vegetation mapping in complex urban landscapes due to the ultra-high resolution imagery acquired at low altitudes. Because of payload capacity restrictions, off-the-shelf digital cameras are widely used on medium and small sized UAVs. The limitation of low spectral resolution in digital cameras for vegetation mapping can be reduced by incorporating texture features and robust classifiers. Random Forest has been widely used in satellite remote sensing applications, but its usage in UAV image classification has not been well documented. The objectives of this paper were to propose a hybrid method using Random Forest and texture analysis to accurately differentiate land covers of urban vegetated areas, and analyze how classification accuracy changes with texture window size. Six least correlated second-order texture measures were calculated at nine different window sizes and added to original Red-Green-Blue (RGB) images as ancillary data. A Random Forest classifier consisting of 200 decision trees was used for classification in the spectral-textural feature space. Results indicated the following: (1) Random Forest outperformed traditional Maximum Likelihood classifier and showed similar performance to object-based image analysis in urban vegetation classification; (2) the inclusion of texture features improved classification accuracy significantly; (3) classification accuracy followed an inverted U relationship with texture window size. The results demonstrate that UAV provides an efficient and ideal platform for urban vegetation mapping. The hybrid method proposed in this paper shows good performance in differentiating urban vegetation mapping. The drawbacks of off-the-shelf digital cameras can be reduced by adopting Random Forest and texture analysis at the same time.
Quanlong Feng; Jiantao Liu; Jianhua Gong. UAV Remote Sensing for Urban Vegetation Mapping Using Random Forest and Texture Analysis. Remote Sensing 2015, 7, 1074 -1094.
AMA StyleQuanlong Feng, Jiantao Liu, Jianhua Gong. UAV Remote Sensing for Urban Vegetation Mapping Using Random Forest and Texture Analysis. Remote Sensing. 2015; 7 (1):1074-1094.
Chicago/Turabian StyleQuanlong Feng; Jiantao Liu; Jianhua Gong. 2015. "UAV Remote Sensing for Urban Vegetation Mapping Using Random Forest and Texture Analysis." Remote Sensing 7, no. 1: 1074-1094.
Photovoltaic energy has become a popular renewable energy source for sustainable urban development. As a result, 3D solar radiation models are needed to facilitate the interactive assessment of photovoltaic potential in complex urban environments. SURFSUN3D is a visualization-oriented full 3D solar radiation model that has been shown to achieve efficient computation and visualization for 3D urban models. The present paper introduces a framework to integrate SURFSUN3D into a 3D GIS-based application to interactively assess the photovoltaic potential in urban areas.
Jianming Liang; Jianhua Gong; Jieping Zhou; Abdoul Nasser Ibrahim; Ming Li. An open-source 3D solar radiation model integrated with a 3D Geographic Information System. Environmental Modelling & Software 2014, 64, 94 -101.
AMA StyleJianming Liang, Jianhua Gong, Jieping Zhou, Abdoul Nasser Ibrahim, Ming Li. An open-source 3D solar radiation model integrated with a 3D Geographic Information System. Environmental Modelling & Software. 2014; 64 ():94-101.
Chicago/Turabian StyleJianming Liang; Jianhua Gong; Jieping Zhou; Abdoul Nasser Ibrahim; Ming Li. 2014. "An open-source 3D solar radiation model integrated with a 3D Geographic Information System." Environmental Modelling & Software 64, no. : 94-101.