王圣音, 高勇, 陆锋, 刘瑜. 场所模型及大数据支持下的场所感知[J]. 武汉大学学报 ( 信息科学版), 2020, 45(12): 1930-1941. DOI: 10.13203/j.whugis20200491
引用本文: 王圣音, 高勇, 陆锋, 刘瑜. 场所模型及大数据支持下的场所感知[J]. 武汉大学学报 ( 信息科学版), 2020, 45(12): 1930-1941. DOI: 10.13203/j.whugis20200491
WANG Shengyin, GAO Yong, LU Feng, LIU Yu. Place Model and Big Geo-Data Supported Place Sensing[J]. Geomatics and Information Science of Wuhan University, 2020, 45(12): 1930-1941. DOI: 10.13203/j.whugis20200491
Citation: WANG Shengyin, GAO Yong, LU Feng, LIU Yu. Place Model and Big Geo-Data Supported Place Sensing[J]. Geomatics and Information Science of Wuhan University, 2020, 45(12): 1930-1941. DOI: 10.13203/j.whugis20200491

场所模型及大数据支持下的场所感知

Place Model and Big Geo-Data Supported Place Sensing

  • 摘要: 场所是连接人类行为与地理环境的重要纽带,为地理分析提供了以人为本的研究视角。由于场所名称固有的模糊性、不确定性与多义性,以及个体层面的空间活动数据的获取成本高,导致早年有关场所的研究难以捕获场所特征并将其在计算机中进行表达。近年来,多源地理大数据和人工智能方法为场所建模与感知带来了新机遇。场所建模也是地理人工智能中知识表达的重要构成。梳理了有关场所模型与场所感知两个方向的研究历程及其技术进展。场所建模的研究目标旨在建立更符合人们空间认知与空间交流习惯的空间知识表达模型,以提高地理信息检索、智能问答的智能化与人机交互水平。场所感知致力于捕获人们对地理环境的情感与认知,深入理解人类活动与地理环境之间的耦合关系,精细表达场所丰富的地理语义属性,从而促进地理人工智能系统的发展。

     

    Abstract: The concept of place plays an important role for coupling between human behaviors and geographic environments, and thus provides a human-centric research perspective for geographical analysis. In early place studies, it was difficult to capture and represent the comprehensive semantics of a place in computer systems due to the inherent vagueness, uncertainty, and ambiguity of places, as well as the high cost of obtaining place-based behavioral data at the individual level. Recently, multi-sourced big geo-data and artificial intelligent methods have brought new opportunities for place modeling and sensing. Moreover, place modeling is an important component of knowledge representation in GeoAI. This article reviews the progress of two directions of place modeling and place sensing.Place modeling aims to construct a platial knowledge model that is more in line with human spatial cognition and communication, so as to improve the intelligence and human-computer interaction of geographical information retrieval and spatial query answering. Place sensing aims to capture human perception and cognition about a place to better understand the intrinsic connections between human activities and the geographic environments, and to represent the rich geographical context of a place. The ultimate objective is to construct "white-box" place-based knowledge representation and reasoning for promoting the development of GeoAI.

     

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