作者：TongQin

Abstract: Previous behavior experimental studies indicate that geography education facilitates the development of students’ spatial ability. However, it is unclear how geography education shapes student brain activity and promotes spatial ability. In this article, we proposed a neuroscience-based method to explore the relationship between geography education and spatial ability. We conducted a behavioral experiment with 63 participants and an fMRI experiment with 49 participants. All the participants were divided into groups according to their undergraduate years and majors completed four spatial ability tasks. The fMRI and behavioral results revealed that after four years of geography education, students had greater mental rotation, spatial visualization and spatial relation reasoning abilities than non-geography students. The activation and functional connectivity of brain regions further indicated that geography education improved students’ spatial reference, spatial memory, visual attention and spatial decision-making. Our findings offer new neuroscience evidence that geography education can improve the spatial ability of undergraduate students, and provide new neuroimaging approach for geographic talent cultivation and curriculum assessment.

Key words: Spatial ability, geography education, behavioral experiment, functional magnetic resonance imaging, functional connectivity

Abstract: Visual Attention Prediction (VAP) is widely applied in GIS research, such as navigation task identification and driver assistance systems. Previous studies commonly took color information to detect the visual saliency of natural scene images. However, these studies rarely considered adaptively feature integration to different geospatial scenes in specific tasks. To better predict visual attention while driving tasks, in this paper, we firstly propose an Adaptive Feature Integration Fully Convolutional Network (AdaFI-FCN) using Scene-Adaptive Weights (SAW) to integrate RGB-D, motion and semantic features. The quantitative comparison results on the DR(eye)VE dataset show that the proposed framework achieved the best accuracy and robustness performance compared with state-of-the-art models (AUC-Judd = 0.971, CC = 0.767, KL = 1.046, SIM = 0.579). In addition, the experimental results of the ablation study demonstrated the positive effect of the SAW method on the prediction robustness in response to scene changes. The proposed model has the potential to benefit adaptive VAP research in universal geospatial scenes, such as AR-aided navigation, indoor navigation, and street-view image reading.

Key words: Visual Attention Prediction (VAP), feature integration, Fully Convolutional Network (FCN), driving environment, deep learning

 

Abstract: Eye movement is a new type of data for cartography and geographic information science (GIS) research. However, previous studies rarely built eye movement datasets with geospatial images. In this paper, we firstly proposed a geospatial image-based eye movement dataset called GeoEye, a publicly shared, widely available eye movement dataset. This dataset consists of 110 college-aged participants who freely viewed 500 images, including thematic maps, remote sensing images, and street view images. In addition, we used the dataset for geospatial image saliency prediction and map user identification. Results demonstrated the scientific benefits and applications of the proposed dataset. GeoEye dataset will not only promote the application of eye-tracking data in cartography and GIS research but also intelligence and customization of geographic information services.

Key words: Eye movement dataset, geospatial image, cartography, GIS, visual saliency detection

 

Abstract: The spatial familiarity of environments is an important high-level user context for location-based services (LBS). Knowing users’ familiarity level of environments is helpful for enabling context-aware LBS that can automatically adapt information services according to users’ familiarity with the environment. Unlike state-of-the-art studies that used questionnaires, sketch maps, mobile phone positioning (GPS) data, and social media data to measure spatial familiarity, this study explored the potential of a new type of sensory data – eye movement data – to infer users’ spatial familiarity of environments using a machine learning approach. We collected 38 participants’ eye movement data when they were performing map-based navigation tasks in familiar and unfamiliar urban environments. We trained and cross-validated a random forest classifier to infer whether the users were familiar or unfamiliar with the environments (i.e., binary classification). By combining basic statistical features and fixation semantic features, we achieved a best accuracy of 81% in a 10-fold classification and 70% in the leave-one-task-out (LOTO) classification. We found that the pupil diameter, fixation dispersion, saccade duration, fixation count and duration on the map were the most important features for detecting users’ spatial familiarity. Our results indicate that detecting users’ spatial familiarity from eye tracking data is feasible in map-based navigation and only a few seconds (e.g., 5 s) of eye movement data is sufficient for such detection. These results could be used to develop context-aware LBS that adapt their services to users’ familiarity with the environments.

Key words: Pedestrian navigation, Eye tracking, Machine learning, Random forest, Wayfinding, Spatial familiarity

 

Abstract: Voice-assisted digital maps have become mainstream navigation aids for pedestrian navigation. Although these maps are widely studied and applied, it is still unclear how they affect human behavior and spatial knowledge acquisition. In this study, we recruited thirty-three college students to carry out an outdoor wayfinding experiment. We compared the effects of voice-assisted digital maps with those of digital maps without voice instructions and paper maps by using eye tracking, sketch maps, questionnaires and interviews. The results show that, compared to the other map types, voice-assisted digital maps can help users reach their destinations more quickly and pay more attention to moving objects, thereby increasing the comfort levels of participants. However, the efficiency of voice-assisted maps on route memory tasks does not rival that of paper maps. Overall, the use of voice-assisted digital maps saves time but may reduce pedestrians’ spatial knowledge acquisition. The results of this study reveal the influence of voice on pedestrian wayfinding and deepen the scientific understanding of the multimedia navigation mode in shaping human spatial ability.

Key words: Eye tracking, wayfinding experiment, pedestrian navigation, voice-assisted digital map, spatial knowledge acquisition

摘要： 驾驶场景的视觉显著性建模是智能驾驶的重要研究方向。现有的静态和虚拟场景的视觉显著性建模方法不能适应真实驾驶环境下道路场景实时性、动态性和任务驱动特性。构建真实驾驶环境的动态场景视觉显著性模型是目前研究的挑战。从驾驶环境的特点与驾驶员的视觉认知规律出发，本文提取道路场景的低级视觉特征、高级视觉特征和动态视觉特征，并结合速度和道路曲率两个重要影响因素，建立了多特征逻辑回归模型（logistic regression，LR）计算驾驶场景视觉显著性。使用AUC值对模型进行评价，结果显示精度达到了90.43%，与传统的算法相比具有明显的优势。

关键词: 视觉显著性, 驾驶场景, 驾驶环境, 动态性

Abstract: Visual saliency modeling of driving scenes is an important research direction in intelligent driving, especially in the areas of assisted driving and automatic driving. The existing visual saliency modeling methods for static and virtual scenes cannot adapt to the real-time, dynamic and task-driven characteristics of road scenes in real driving environments. Building a visual saliency model of dynamic road scenes in real driving environments is a challenge for current research. Starting from the characteristics of driving environment and driver’s visual cognitive law, this paper extracts low-level visual features, high-level visual features and dynamic visual features of road scenes, and combines two influencing factors of speed and road curvature to build a visual saliency calculation model of driving scenes based on logistic regression model (LR). In this paper, the AUC value is used to evaluate the model, and the results showed an accuracy of 90.43%, which is significant advantage over traditional algorithms.

Key words: visual saliency, driving scene, driving environment, dynamics

引用本文：詹智成, 董卫华. 用于智能驾驶的动态场景视觉显著性多特征建模方法[J]. 测绘学报, 2021, 50(11): 1500-1511.

To cite this article: ZHAN Zhicheng, DONG Weihua. A multi-feature approach for modeling visual saliency of dynamic scene for intelligent driving[J]. Acta Geodaetica et Cartographica Sinica, 2021, 50(11): 1500-1511.

摘要： 眼动追踪技术在人机交互、用户行为识别、预测等方面得到了广泛应用，但是如何自动识别用户的地图阅读行为，眼动行为仍具有一定的挑战性。本文提出了一种基于朴素贝叶斯分类模型的方法识别用户阅读地图线状要素时的眼动行为。本试验首先通过25名被试者阅读地图过程中的眼动行为进行数据采集，然后提取了250个眼动特征并对其进行离散化处理，采用最小冗余最大相关方法进行特征选择排序。结果显示，当采用信息熵法，特征数量为m=5时分类准确率最大为78.27%；而采用信息差法，特征数量为m=4时分类准确率达到最大值为77.01%。本文提出的基于朴素贝叶斯的方法在准确率方面优于已有研究方法。此外，由于特征数量的减少，大幅提高了算法的执行效率。本文提出的地图阅读行为眼动识别方法，为未来眼控交互式地图研究奠定基础。

关键词: 眼动识别, 地图读图行为, 朴素贝叶斯分类器, 特征选择, 最小冗余最大相关

Abstract: At present, eye tracking technology has been widely used in human-computer interaction, user behavior recognition and prediction, but how to automatically identify user’s eye movement behavior in map reading is still a challenge. This paper proposed a method based on the naive Bayesian classification model to identify the users’ behavior when performing linear feature recognition. We first conducted an eye tracking experiment to acquire users’ eye movement dataset of map reading. Then we extracted and discretized 250 eye movement features involved in the algorithm, and used minimum redundancy maximum relevance algorithm to further select the features. The results show that when the attribute selection method is m=5 using mutual information quotient, the classification accuracy is 78.27%. And when using mutual information difference and m=4, the classification accuracy is 77.01%.We suggested that the proposed method can effectively identify the first elements in the map using eye movement data. This study explores the interaction technology by combining the eye tracking, laying the foundation for the future of designing gaze-controlled interactive map. The proposed method based on naive Bayesian model in this paper is comparable to the existing methods. In addition, the execution efficiency of the model is greatly improved due to the reduction in the number of features. The eye-track recognition algorithm of map reading behavior proposed in this study lays a foundation for future gaze-controlled interactive map research.

Key words: eye movement recognition, map reading behavior, naive Bayesian classifier, feature selection, minimum redundancy maximum relevance

引文格式：董卫华, 王圣凯, 王雪元, 杨天宇. 地图线状要素眼动识别的朴素贝叶斯方法[J]. 测绘学报, 2021, 50(6): 749-756.

To cite this article: DONG Weihua, WANG Shengkai, WANG Xueyuan, YANG Tianyu. A naive Bayesian method for eye movement recognition of map linear elements[J]. Acta Geodaetica et Cartographica Sinica, 2021, 50(6): 749-756.

Abstract: Geographic information science (GIScience) and remote sensing have long provided essential data and methodological support for natural resource challenges and environmental problems research. With increasing advances in information technology, natural resource and environmental science research faces the dual challenges of data and computational intensiveness. Therefore, the role of remote sensing and GIScience in the fields of natural resources and environmental science in this new information era is a key concern of researchers. This study clarifies the definition and frameworks of these two disciplines and discusses their role in natural resource and environmental research. GIScience is the discipline that studies the abstract and formal expressions of the basic concepts and laws of geography, and its research framework mainly consists of geo-modeling, geo-analysis, and geo-computation. Remote sensing is a comprehensive technology that deals with the mechanisms of human effects on the natural ecological environment system by observing the earth surface system. Its main areas include sensors and platforms, information processing and interpretation, and natural resource and environmental applications. GIScience and remote sensing provide data and methodological support for resource and environmental science research. They play essential roles in promoting the development of resource and environmental science and other related technologies. This paper provides forecasts of ten future directions for GIScience and eight future directions for remote sensing, which aim to solve issues related to natural resources and the environment.

To cite this article: TAO, P., et al. 2021. GIScience and remote sensing in natural resource and environmental research: Status quo and future perspectives. Geography and Sustainability, 2(3), 207-215.

DOI: 10.1016/j.geosus.2021.08.004

ABSTRACT: Individuals with different characteristics exhibit different eye movement patterns in map reading and wayfinding tasks. In this study, we aim to explore whether and to what extent map users’ eye movements can be used to detect who created them. Specifically, we focus on the use of gaze data for inferring users’ identities when users are performing map-based spatial tasks. We collected 32 participants’ eye movement data as they utilized maps to complete a series of self-localization and spatial orientation tasks. We extracted five sets of eye movement features and trained a random forest classifier. We used a leave-one-task-out approach to cross-validate the classifier and achieved the best identification rate of 89%, with a 2.7% equal error rate. This result is among the best performances reported in eye movement user identification studies. We evaluated the feature importance and found that basic statistical features (e.g. pupil size, saccade latency and fixation dispersion) yielded better performance than other feature sets (e.g. spatial fixation densities, saccade directions and saccade encodings). The results open the potential to develop personalized and adaptive gaze-based map interactions but also raise concerns about user privacy protection in data sharing and gaze-based geoapplications.

To cite this article: Hua Liao, Weihua Dong & Zhicheng Zhan (2021) Identifying map users with eye movement data from map-based spatial tasks: user privacy concerns, Cartography and Geographic Information Science, DOI: 10.1080/15230406.2021.1980435