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。
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.
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: (2021) Identifying map users with eye movement data from map-based spatial tasks: user privacy concerns, Cartography and Geographic Information Science,
Abstract: Finding one’s way is a fundamental daily activity and has been widely studied in the field of geospatial cognition. Immersive virtual reality (iVR) techniques provide new approaches for investigating wayfinding behavior and spatial knowledge acquisition. It is currently unclear, however, how wayfinding behavior and spatial knowledge acquisition in iVR differ from those in real-world environments (REs). We conducted an RE wayfinding experiment with twenty-five participants who performed a series of tasks. We then conducted an iVR experiment using the same experimental design with forty participants who completed the same tasks. Participants’ eye movements were recorded in both experiments. In addition, verbal reports and postexperiment questionnaires were collected as . The results revealed that individuals’ wayfinding performance is largely the same between the two environments, whereas their visual attention exhibited significant differences. Participants processed visual information more efficiently in RE but searched visual information more efficiently in iVR. For spatial knowledge acquisition, participants’ distance estimation was more accurate in iVR compared with RE. Participants’ direction estimation and sketch map results were not significantly different, however. This empirical evidence regarding the ecological validity of iVR might encourage further studies of the benefits of VR techniques in geospatial cognition research.
To cite this article: Dong, W.H., Qin, T., Yang, T.Y., Liao, H., Liu, B., Meng, L.Q., Liu, Y., Wayfinding Behavior and Spatial Knowledge Acquisition: Are They the Same in Virtual Reality and in Real-World Environments? Ann. Am. Assoc. Geogr., 21.