Research directions and core tasks for cognitive understanding of spatio-temporal scenes

doi:10.11947/j.AGCS.2026.20250471

Abstract

Abstract:

In response to the prominent contradiction of “massive data, information overload, and scarce knowledge” in Earth sciences, existing spatial analysis methods, which primarily rely on geometric computation and state recognition, remain inadequate for fully characterizing the multidimensional and dynamic properties of spatio-temporal scenes. Consequently, practical applications still face difficulties in locating critical targets, understanding evolutionary processes, and accurately assessing functional effects. This paper regards spatio-temporal scenes as complex dynamic systems with explicit geographic semantics, internal structures, and evolutionary regularities, and examines the fundamental connotation and computational pathways of spatio-temporal scene cognition. On this basis, psychological cognitive mechanisms, domain knowledge, and artificial intelligence algorithms are integrated to construct a hybrid-intelligence cognitive chain centered on element identification, relationship computation, structural reasoning, and functional judgment. Accordingly, multidimensional cognitive methods are proposed for efficient prediction of macro-scale scenes, dynamic diagnosis of local scenes, and precise verification in real-world three-dimensional scenes. Finally, a case study on the dynamic cognition of unauthorized farmland excavation scenes is presented to illustrate the application pathway and feasibility of hybrid intelligence in real-world operational contexts.

Key words: spatio-temporal scenes, cognition, hybrid intelligence, cognitive chain model

CLC Number:

P208

Wanzeng Liu, Jun Chen, Jiaxin Ren, Feng Zhang, Lina Huang, Xinpeng Wang, Ye Zhang, Fuxun Liang, Xiaoyu Liu. Research directions and core tasks for cognitive understanding of spatio-temporal scenes[J]. Acta Geodaetica et Cartographica Sinica, 2026, 55(8): 1465-1481.

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比较维度	传统空间分析	时空场景认知
对象范围	地理要素、空间场、网络及其几何、属性和拓扑关系	具有明确地理语义、内部结构和演化规律的复杂动态系统，包括宏观遥感场景、动态视频场景和三维实景场景
基本构成	几何、属性、位置和拓扑	要素、状态、关系、结构和功能
输入数据	矢量、栅格、属性、统计及专题数据	遥感影像、视频、实景三维、专题数据、传感器数据及领域知识
认知过程	查询、量测、叠加、缓冲、网络分析和空间统计	场景感知、要素识别、关系计算、结构推理、功能判断
输出内容	几何量测结果、空间分布、统计指标及专题图	对象、关系、事件、行为、功能效应及演化规律等时空知识
服务对象	空间数据处理、专题分析及辅助决策	自然资源监管、城市治理、规划决策、应急响应及时空知识服务

心理学认知	可计算抽象	对应的模型实现及作用
知觉组织	相似性、邻近性、连续性和闭合性约束	构建要素关联权重、图邻接关系或结构一致性损失，支持局部要素聚合与整体结构识别
认知地图	对象、位置和关系构成的拓扑图式	将场景要素表示为节点，将空间、时间和语义关系表示为边，构建场景图或时空知识图谱
预测编码	先验预测与实际观测之间的误差修正	设置预测误差、时序一致性损失或异常分数，用于变化识别和动态场景诊断
注意选择	面向任务目标的信息选择与资源分配	通过注意力权重、显著性区域和目标门控机制突出关键要素及重要关系
记忆联想	历史经验、场景原型与当前信息的关联	通过记忆库、历史状态缓存和知识检索支持跨时态关联、案例匹配与推理决策

模型	Precision	Recall	mAP@50	mAP@50-95
YOLOv5n	82.65	85.10	91.15	75.55
YOLOv6n	86.15	81.56	90.08	76.30
YOLOv8n	81.76	86.98	90.44	75.70
YOLOv9t	81.31	84.86	90.02	75.92
YOLOv10n	80.47	82.58	89.16	74.50
YOLO11n	85.31	85.04	90.90	74.76
RT-DETR	77.11	81.85	82.07	69.13
Mask RCNN	87.55	88.47	92.91	74.51
Mask2former	84.69	85.47	86.88	74.25
HIUFE	88.17	88.69	94.47	78.66