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Abstract The human thermal stress indices and datasets are vital for promoting public health and reducing negative environmental impacts as global climate change and extreme meteorological events increase. The current thermal indices generally use an instantaneous or average value to describe thermal stress which cannot reflect the distribution of thermal comfort conditions over time, and there are no global-scale thermal stress datasets with both 0.1° or higher spatial resolution and hourly temporal resolution available yet. A novel human thermal metric, Thermal Stress Duration (TSD), is proposed to represent the accumulative time of different thermal stress levels within a certain period. A high temporal resolution global gridded dataset of human thermal stress metrics (HiGTS) is presented, which consists of hourly gridded maps of Universal Thermal Climate Index (UTCI), Universal Thermal Stress (UTS), and daily TSD at 0.1° × 0.1° spatial resolution over the global land surface, spanning from January 1, 2000, to December 31, 2023.

Visual 3D reconstruction enables rebuilding 3D scenes from captured images, serving as a fundamental data source for digital earth modeling and intelligent cities. In the foundational step, recent methods leverage learning-based descriptors for image registration and achieve tremendous advances in precision and robustness. However, these methods inevitably execute down sampling towards high-resolution images to fit the needs of neural networks, which leads to precision degradation of feature localization and matching. Thus, we propose GeoRecon: a novel coarse-to-fine visual 3D reconstruction method that optimally utilizes high-resolution images for high-quality visual 3D reconstruction. Firstly, the coarse stage conducts coarse reconstruction from downsampled images by performing neural matching with geometric priors. Secondly, we define the fine-grained stage, proposing a GPU-based algorithm for generating image-patch correspondences based on the neural matching priors to perform fine-grained image registration. Finally, based on the optimized camera poses under this coarse-to-fine paradigm, progressive dense reconstruction leveraging efficient neural radiance fields is proposed to accomplish the high-quality MVS reconstruction. Comparative experiments across various scenarios demonstrate the proposed method’s superior precision, robustness, and reconstruction quality.

We present GeoGlue, a novel method using high-resolution UAV imagery for accurate feature matching, which is normally challenging due to the complicated scenes. Current feature detection methods are performed without guidance of geometric priors (e.g., geometric lines), lacking enough attention given to salient geometric features which are indispensable for accurate matching due to their stable existence across views. In this work, geometric lines are firstly detected by a CNN-based geometry detector (GD) which is pre-trained in a self-supervised manner through automatically generated images. Then, geometric lines are naturally vectorized based on GD and thus non-significant features can be disregarded as judged by their disordered geometric morphology. A graph attention network (GAT) is utilized for final feature matching, spanning across the image pair with geometric priors informed by GD. Comprehensive experiments show that GeoGlue outperforms other state-of-the-art methods in feature-matching accuracy and performance stability, achieving pose estimation with maximum rotation and translation errors under 1% in challenging scenes from benchmark datasets, Tanks & Temples and ETH3D. This study also proposes the first self-supervised deep-learning model for curved line detection, generating geometric priors for matching so that more attention is put on prominent features and improving the visual effect of 3D reconstruction.

Extracting water bodies from synthetic aperture radar (SAR) images plays a crucial role in the management of water resources, flood monitoring, and other applications. Recently, transformer-based models have been extensively utilized in the remote sensing domain. However, due to regular patch-partition and weak inductive bias, transformer-based models face challenges such as edge serration and high data dependency when used for water body extraction from SAR images. To address these challenges, we introduce a new model, the Superpixel-based Transformer (SPT), based on the adaptive characteristic of superpixels and knowledge constraints of the adjacency matrix. (1) To mitigate edge serration, the SPT replaces regular patch partition with superpixel segmentation to fully utilize the internal homogeneity of superpixels. (2) To reduce data dependency, the SPT incorporates a normalized adjacency matrix between superpixels into the Multi-Layer Perceptron (MLP) to impose knowledge constraints. (3) Additionally, to integrate superpixel-level learning from the SPT with pixel-level learning from the CNN, we combine these two deep networks to form SPT-UNet for water body extraction. The results show that our SPT-UNet is competitive compared with other state-of-the-art extraction models, both in terms of quantitative metrics and visual effects.

Deep learning is currently the mainstream approach for building extraction tasks in remote-sensing imagery, capable of automatically learning features of buildings in imagery and yielding satisfactory extraction results. However, due to the diverse sizes, irregular layouts, and complex spatial relationships of buildings, extracted buildings often suffer from incompleteness and boundary issues. Gaofen-7 (GF-7), as a high-resolution stereo mapping satellite, provides well-rectified images from its rear-view imagery, which helps mitigate occlusions in highly varied terrain, thereby offering rich information for building extraction. To improve the integrity of the edges of the building extraction results, this paper proposes a dual-task network (Edge-UNet, EUnet) based on UNet, incorporating an edge extraction branch to emphasize edge information while predicting building targets. We evaluate this method using a self-made GF-7 Building Dataset, the Wuhan University (WHU) Building Dataset, and the Massachusetts Buildings Dataset. Comparative analysis with other mainstream semantic segmentation networks reveals significantly higher F1 scores for the extraction results of our method. Our method exhibits superior completeness and accuracy in building edge extraction compared to unmodified algorithms, demonstrating robust performance.

The rapid growth of remote sensing big data (RSBD) has attracted considerable attention from both academia and industry. Despite the progress of computer technologies, conventional computing implementations have become technically inefficient for processing RSBD. Cloud computing is effective in activating and mining large-scale heterogeneous data and has been widely applied to RSBD over the past years. This study performs a technical review of cloud-based RSBD storage and computing from an interdisciplinary viewpoint of remote sensing and computer science. First, we elaborate on four critical technical challenges resulting from the scale expansion of RSBD applications, i.e. raster storage, metadata management, data homogeneity, and computing paradigms. Second, we introduce state-of-the-art cloud-based data management technologies for RSBD storage. The unit for manipulating remote sensing data has evolved due to the scale expansion and use of novel technologies, which we name the RSBD data model. Four data models are suggested, i.e. scenes, ARD, data cubes, and composite layers. Third, we summarize recent research on the application of various cloud-based parallel computing technologies to RSBD computing implementations. Finally, we categorize the architectures of mainstream RSBD platforms. This research provides a comprehensive review of the fundamental issues of RSBD for computing experts and remote sensing researchers.

Existing research indicates that detecting near-surface methane point sources using Sentinel-2 satellite imagery can offer crucial data support for mitigating climate change. However, current retrieval methods necessitate the identification of reference images unaffected by methane, which presents certain limitations. This study introduces the use of a matched filter, developing a novel methane detection algorithm for Sentinel-2 imagery. Compared to existing algorithms, this algorithm does not require selecting methane-free images from historical imagery in methane-sensitive bands, but estimates the background spectral information across the entire scene to extract methane gas signals. We tested the algorithm using simulated Sentinel-2 datasets. The results indicated that the newly proposed algorithm effectively reduced artifacts and noise. It was then validated in a known methane emission point source event and a controlled release experiment for its ability to quantify point source emission rates. The average estimated difference between the new algorithm and other algorithms was about 34%. Compared to the actual measured values in the controlled release experiment, the average estimated values ranged from −48% to 42% of the measurements. These estimates had a detection limit ranging from approximately 1.4 to 1.7 t/h and an average error percentage of 19%, with no instances of false positives reported. Finally, in a real case scenario, we demonstrated the algorithm’s ability to precisely locate the source position and identify, as well as quantify, methane point source emissions.

In order to alleviate the conflict between populations and land resource, Tianjin adopted multi-phase reclamation projects to the formed large-scale artificial reclamation land. The reclamation areas, however, are prone to subsidence, which poses a significant threat to infrastructure as well as the safety and assets of the residents. The SBAS-InSAR was used to acquire surface deformation of Tianjin Binhai New Area from January 2017 to December 2022, analyze in depth the response relationship between land subsidence, reclamation project time, and land-use type. There is a strong correlation between surface deformation and reclamation time. Severe land subsidence occurred over newly reclaimed areas. In the offshore direction, the deformation values of the Nangang Industrial Zone, the Lingang Industrial Zone, and Hangu Harbor were −98 mm to −890 mm, 45 mm to −580 mm, and −140 mm to −290 mm, respectively. Significant differences in deformation were detected among different land-use types where reclamation projects were completed in the same time. Subsidence was positively correlated with surface load; in areas with higher surface loads, the surface settlement was also more severe. The average surface settlement for the heavy shipyard, with 67 grain storage tanks and 27 grain storage tanks, road, and bare land were −201 mm, −166 mm, −107 mm, −64 mm, and −43 mm, respectively. This study reveals significant differences of surface deformation in the reclamation completed at different times, and determines that the load is the main driving factor of settlement difference in the reclamation land completed at the same time. This has important guiding significance for preventing and controlling geological disasters in the reclamation area and later development planning.

Augmented virtual environments (AVE) combine real-time videos with 3D scenes in a Digital Earth System or 3D GIS to present dynamic information and a virtual scene simultaneously. AVE can provide solutions for continuous tracking of moving objects, camera scheduling, and path planning in the real world. This paper proposes a novel approach for 3D path prediction of moving objects in a video-augmented indoor virtual environment. The study includes 3D motion analysis of moving objects, multi-path prediction, hierarchical visualization, and path-based multi-camera scheduling. The results show that these methods can give a closed-loop process of 3D path prediction and continuous tracking of moving objects in an AVE. The path analysis algorithms proved accurate and time-efficient, costing less than 1.3 ms to get the optimal path. The experiment ran a 3D scene containing 295,000 triangles at around 35 frames per second on a laptop with 1 GB of graphics card memory, which means the performance of the proposed methods is good enough to maintain high rendering efficiency for a video-augmented indoor virtual scene.

The Open Geospatial Consortium (OGC) is an international non-profit standards organization. Established in 1994, OGC aims to make geospatial information and services FAIR-Findable, Accessible, Interoperable, and Reusable. OGC specifications have greatly facilitated interoperability among software, hardware, data, and users in the GIS field. This study collected publications related to OGC specifications from the Web of Science (WoS database) between 1994 to 2020 and conducted a literature analysis using Derwent Data Analyzer and VosViewer, finding that OGC specifications have been widely applied in academic fields. The most productive organizations were Wuhan University and George Mason University; the most common keywords were interoperability, data, and web service. Since 2018, the emerging keywords that have attracted much attention from researchers were 3D city models, 3D modeling, and smart cities. To make geospatial data FAIR, the OGC specifications SWE and WMS served more for “Findable”, SWE contributed more to “Accessible”, WPS and WCS served more for “Interoperable”, and WPS, XML schemas, WFS, and WMS served more for “Reusable”. The OGC specification also serves data and web services for large-scale infrastructure such as the Digital Earth Platform of the Chinese Academy of Sciences.

Object detection in remote sensing images (RSIs) is currently one of the most important topics, which can promote the understanding of the earth and better serve for the construction of digital earth. In addition to single objects, there are many composite objects in RSIs, especially primary and secondary schools (PSSs), which are composed of some parts and surrounded by complex background. Existing deep learning methods have difficulty detecting composite objects effectively. In this article, we propose a feature enhanced Network (FENet) based on an anchor-free method for PSSs detection. FENet can not only realize more accurate pixel-level detection based on enhanced features but also simplify the training process by avoiding hyper-parameters. First, an enhanced feature module (EFM) is designed to improve the representation ability of complex features. Second, a context-aware strategy is used for alleviating the interference of background information. In addition, complete intersection over union (CIoU) loss is employed for bounding box regression, which can obtain better convergence speed and accuracy. At the same time, we build a PSSs dataset for composite object detection. This dataset contains 1685 images of PSSs in Beijing–Tianjin–Hebei region. Experimental results demonstrate that FENet outperforms several object detectors and achieves 78.7% average precision. The study demonstrates the advantage of our proposed method on PSSs detection.

The detection of primary and secondary schools (PSSs) is a meaningful task for composite object detection in remote sensing images (RSIs). As a typical composite object in RSIs, PSSs have diverse appearances with complex backgrounds, which makes it difficult to effectively extract their features using the existing deep-learning-based object detection algorithms. Aiming at the challenges of PSSs detection, we propose an end-to-end framework called the attention-guided dense network (ADNet), which can effectively improve the detection accuracy of PSSs. First, a dual attention module (DAM) is designed to enhance the ability in representing complex characteristics and alleviate distractions in the background. Second, a dense feature fusion module (DFFM) is built to promote attention cues flow into low layers, which guides the generation of hierarchical feature representation. Experimental results demonstrate that our proposed method outperforms the state-of-the-art methods and achieves 79.86% average precision. The study proves the effectiveness of our proposed method on PSSs detection.

The violent and persistent wildfires that broke out along the southeast coast of Australia in 2019 caused a large number of pollutant emissions, which seriously affected air quality and the global climate. The existing two methods for estimating combustion emissions based on burned area and fire radiative power mainly use a medium resolution imaging spectrometer (MODIS) on the Aqua and Terra satellites. However, the low temporal resolution of MODIS and insensitivity to small fires lead to deviation in the estimation of fire emissions. In order to solve this problem, the Visible Infrared Imaging Radiometer Suite (VIIRS) with better performance is adopted in this paper, combined with the fire diurnal cycle information obtained by geostationary satellite Himawari-8, to explore the spatio-temporal model of biomass combustion emissions. Using this, a high-spatial- and -temporal-resolution fire emission inventory was generated for southeastern Australia from November 2019 to January 2020, which aims to fully consider the highly dynamic nature of fires and small fires (low FRP) that are much lower than the MODIS burned area or active fire detection limit, with emphasis on dry matter burned (DMB). We found that during the study period, the fire gradually moved from north to south, and the diurnal cycle of the fire in the study area changed greatly. The peak time of the fire gradually delayed as the fire moved south. Our inventory shows that the DMB in southeast Australia during the study period was about 146 Tg, with major burned regions distributed along the Great Dividing Range, with December 2019 being the main burning period. The total DMB we calculated is 0.5–3.1 times that reported by the GFAS (Global Fire Assimilation System) and 1.5 to 4 times lower than that obtained using the traditional “Burned Area Based Method (FINN)”. We believe that the GFAS may underestimate the results by ignoring a large number of small fires, and that the excessive combustion rate used in the FINN may be a source of overestimation. Therefore, we conclude that the combination of high-temporal-resolution and high-spatial-resolution satellites can improve FRE estimation and may also allow further verification of biomass combustion estimates from different inventories, which are far better approaches for fire emission estimation.

An Augmented virtual environment (AVE) is concerned with the fusion of real-time video with 3D models or scenes so as to augment the virtual environment. In this paper, a new approach to establish an AVE with a wide field of view is proposed, including real-time video projection, multiple video texture fusion and 3D visualization of moving objects. A new diagonally weighted algorithm is proposed to smooth the apparent gaps within the overlapping area between the two adjacent videos. A visualization method for the location and trajectory of a moving virtual object is proposed to display the moving object and its trajectory in the 3D virtual environment. The experimental results showed that the proposed set of algorithms are able to fuse multiple real-time videos with 3D models efficiently, and the experiment runs a 3D scene containing two million triangles and six real-time videos at around 55 frames per second on a laptop with 1GB of graphics card memory. In addition, a realistic AVE with a wide field of view was created based on the Digital Earth Science Platform by fusing three videos with a complex indoor virtual scene, visualizing a moving object and drawing its trajectory in the real time.

Visual simultaneous localization and mapping (SLAM) is challenging in dynamic environments as moving objects can impair camera pose tracking and mapping. This paper introduces a method for robust dense bject-level SLAM in dynamic environments that takes a live stream of RGB-D frame data as input, detects moving objects, and segments the scene into different objects while simultaneously tracking and reconstructing their 3D structures. This approach provides a new method of dynamic object detection, which integrates prior knowledge of the object model database constructed, object-oriented 3D tracking against the camera pose, and the association between the instance segmentation results on the current frame data and an object database to find dynamic objects in the current frame. By leveraging the 3D static model for frame-to-model alignment, as well as dynamic object culling, the camera motion estimation reduced the overall drift. According to the camera pose accuracy and instance segmentation results, an object-level semantic map representation was constructed for the world map. The experimental results obtained using the TUM RGB-D dataset, which compares the proposed method to the related state-of-the-art approaches, demonstrating that our method achieves similar performance in static scenes and improved accuracy and robustness in dynamic scenes.

Conventional numerical methods have made significant advances in forecasting tropical cyclone (TC) tracks, using remote sensing data with high spatial and temporal resolutions. However, over the past two decades, no significant improvements have been made with regard to the accuracy of TC intensity prediction, which remains challenging, as the internal convection and formation mechanisms of such storms are not fully understood. This study investigated the relationship between remote sensing data and TC intensity to improve the accuracy of TC intensity prediction. An intensity forecast model for the South China Sea was built using the eXtreme Gradient Boosting (XGBoost) model and FengYun-2 (FY-2) satellite data, environmental data, and best track datasets from 2006 to 2017. First, correlation analysis algorithms were used to extract the TC regions in which the satellite data were best correlated, with TC intensity at lead times of 6, 12, 18, and 24 h. Then, satellite, best track, and environmental data were used as source data to develop three different XGBoost models for predicting TC intensity: model A1 (climatology and persistence predictors + environmental predictors), model A2 (A1 + satellite-based predictors extracted as mean values), and model A3 (A1 + satellite-based predictors extracted by our method). Finally, we analyzed the impact of the FY-2 satellite data on the accuracy of TC intensity prediction using the forecast skill parameter. The results revealed that the equivalent blackbody temperature (TBB) of the FY-2 data has a strong correlation with TC intensity at 6, 12, 18, and 24 h lead times. The mean absolute error (MAE) of model A3 was reduced by 0.47%, 1.79%, 1.91%, and 5.04% in 6, 12, 18, and 24 h forecasts, respectively, relative to those of model A2, respectively, and by 2.73%, 7.58%, 7.64%, and 5.04% in 6, 12, 18, and 24 h forecasts, respectively, relative to those of model A1. Furthermore, the accuracy of TC intensity prediction is improved by FY-2 satellite images, and our extraction method was found to significantly improve upon the traditional extraction method.

Mass remote sensing data management and processing is currently one of the most important topics. In this study, we introduce ScienceEarth, a cluster-based data processing framework. The aim of ScienceEarth is to store, manage, and process large-scale remote sensing data in a cloud-based cluster-computing environment. The platform consists of the following three main parts: ScienceGeoData, ScienceGeoIndex, and ScienceGeoSpark. ScienceGeoData stores and manages remote sensing data. ScienceGeoIndex is an index and query system, a spatial index based on quad-tree and Hilbert curve which is combined for heterogeneous tiled remote sensing data that makes efficient data retrieval in ScienceGeoData. ScienceGeoSpark is an easy-to-use computing framework in which we use Apache Spark as the analytics engine for big remote sensing data processing. The result of tests proves that ScienceEarth can efficiently store, retrieve, and process remote sensing data. The results reveal ScienceEarth has the potential and capabilities of efficient big remote sensing data processing.

In recent years, low-cost and lightweight RGB and depth (RGB-D) sensors, such as Microsoft Kinect, have made available rich image and depth data, making them very popular in the field of simultaneous localization and mapping (SLAM), which has been increasingly used in robotics, self-driving vehicles, and augmented reality. The RGB-D SLAM constructs 3D environmental models of natural landscapes while simultaneously estimating camera poses. However, in highly variable illumination and motion blur environments, long-distance tracking can result in large cumulative errors and scale shifts. To address this problem in actual applications, in this study, we propose a novel multithreaded RGB-D SLAM framework that incorporates a highly accurate prior terrestrial Light Detection and Ranging (LiDAR) point cloud, which can mitigate cumulative errors and improve the system’s robustness in large-scale and challenging scenarios. First, we employed deep learning to achieve system automatic initialization and motion recovery when tracking is lost. Next, we used terrestrial LiDAR point cloud to obtain prior data of the landscape, and then we applied the point-to-surface inductively coupled plasma (ICP) iterative algorithm to realize accurate camera pose control from the previously obtained LiDAR point cloud data, and finally expanded its control range in the local map construction. Furthermore, an innovative double window segment-based map optimization method is proposed to ensure consistency, better real-time performance, and high accuracy of map construction. The proposed method was tested for long-distance tracking and closed-loop in two different large indoor scenarios. The experimental results indicated that the standard deviation of the 3D map construction is 10 cm in a mapping distance of 100 m, compared with the LiDAR ground truth. Further, the relative cumulative error of the camera in closed-loop experiments is 0.09%, which is twice less than that of the typical SLAM algorithm (3.4%). Therefore, the proposed method was demonstrated to be more robust than the ORB-SLAM2 algorithm in complex indoor environments.

Coastal cities in China are frequently hit by tropical cyclones (TCs), which result in tremendous loss of life and property. Even though the capability of numerical weather prediction models to forecast and track TCs has considerably improved in recent years, forecasting the intensity of a TC is still very difficult; thus, it is necessary to improve the accuracy of TC intensity prediction. To this end, we established a series of predictors using the Best Track TC dataset to predict the intensity of TCs in the Western North Pacific with an eXtreme Gradient BOOSTing (XGBOOST) model. The climatology and persistence factors, environmental factors, brainstorm features, intensity categories, and TC months are considered inputs for the models while the output is the TC intensity. The performance of the XGBOOST model was tested for very strong TCs such as Hato (2017), Rammasum (2014), Mujiage (2015), and Hagupit (2014). The results obtained show that the combination of inputs chosen were the optimal predictors for TC intensification with lead times of 6, 12, 18, and 24 h. Furthermore, the mean absolute error (MAE) of the XGBOOST model was much smaller than the MAEs of a back propagation neural network (BPNN) used to predict TC intensity. The MAEs of the forecasts with 6, 12, 18, and 24 h lead times for the test samples used were 1.61, 2.44, 3.10, and 3.70 m/s, respectively, for the XGBOOST model. The results indicate that the XGBOOST model developed in this study can be used to improve TC intensity forecast accuracy and can be considered a better alternative to conventional operational forecast models for TC intensity prediction.

Generating 3D path models (with textures) from indoor paths is a good way to improve the visualization performance of 3D indoor path analysis. In this paper, a quaternion-based piecewise 3D modeling method is proposed to automatically generate highly recognizable 3D models for indoor path networks. To create such models, indoor paths are classified into four types of basic elements: corridor, stairs, elevator and node, which contain six kinds of edges and seven kinds of nodes. A quaternion-based method is devised to calculate the coordinates of the designed elements, and a piecewise 3D modeling method is implemented to create the entire 3D indoor path models in a 3D GIS scene. The numerical comparison of 3D scene primitives in different visualization modes indicates that the proposed method can generate detailed and irredundant models for indoor path networks. The result of 3D path analysis shows that indoor path models can improve the visualization performance of a 3D indoor path network by displaying paths with different shapes, textures and colors and that the models can maintain a high rendering efficiency (above 50 frames per second) in a 3D GIS scene containing more than 50,000 polygons and triangles.

A target recognition method of synthetic aperture radar (SAR) images is proposed via matching attributed scattering centers (ASCs) to binary target regions. The ASCs extracted from the test image are predicted as binary regions. In detail, each ASC is first transformed to the image domain based on the ASC model. Afterwards, the resulting image is converted to a binary region segmented by a global threshold. All the predicted binary regions of individual ASCs from the test sample are mapped to the binary target regions of the corresponding templates. Then, the matched regions are evaluated by three scores which are combined as a similarity measure via the score-level fusion. In the classification stage, the target label of the test sample is determined according to the fused similarities. The proposed region matching method avoids the conventional ASC matching problem, which involves the assignment of ASC sets. In addition, the predicted regions are more robust than the point features. The Moving and Stationary Target Acquisition and Recognition (MSTAR) dataset is used for performance evaluation in the experiments. According to the experimental results, the method in this study outperforms some traditional methods reported in the literature under several different operating conditions. Under the standard operating condition (SOC), the proposed method achieves very good performance, with an average recognition rate of 98.34%, which is higher than the traditional methods. Moreover, the robustness of the proposed method is also superior to the traditional methods under different extended operating conditions (EOCs), including configuration variants, large depression angle variation, noise contamination, and partial occlusion.

Fingerprinting-based Wi-Fi indoor positioning has great potential for positioning in GPS-denied areas. However, establishing a fingerprinting map (also called a radio map) prior to positioning (site survey) is normally a labor-intensive task. This paper proposes a method for easy site survey without need for any extra hardware. The user can conduct the site survey adopting only a smart phone. The collected inertial-based readings are processed using the pedestrian dead-reckoning algorithms to generate a raw trajectory. Then a factor graph optimization method is proposed to re-estimate the trajectory by adding constraints originated from collected Wi-Fi fingerprints and landmark positions. The proposed method is verified through an experiment in a mall. The mean positioning error is 1.10 m and the maximum error is 2.25 m. This level of positioning accuracy is considered sufficient for radio map generation purposes. A classical baseline algorithm, the k-Nearest Neighbor (kNN) algorithm, is adopted to test the positioning performance of the radio map (RM), which also validates the quality of the constructed RM from the proposed method.

Most models designed to simulate pedestrian dynamical behavior are based on the assumption that human decision-making can be described using precise values. This study proposes a new pedestrian model that incorporates fuzzy logic theory into a multi-agent system to address cognitive behavior that introduces uncertainty and imprecision during decision-making. We present a concept of decision preferences to represent the intrinsic control factors of decision-making. To realize the different decision preferences of heterogeneous pedestrians, the Five-Factor (OCEAN) personality model is introduced to model the psychological characteristics of individuals. Then, a fuzzy logic-based approach is adopted for mapping the relationships between the personality traits and the decision preferences. Finally, we have developed an application using our model to simulate pedestrian dynamical behavior in several normal or non-panic scenarios, including a single-exit room, a hallway with obstacles, and a narrowing passage. The effectiveness of the proposed model is validated with a user study. The results show that the proposed model can generate more reasonable and heterogeneous behavior in the simulation and indicate that individual personality has a noticeable effect on pedestrian dynamical behavior.