GeoAI for Large-Scale Image Analysis and Machine Vision: Recent Progress of Artificial Intelligence in Geography
GeoAI for Large-Scale Image Analysis and Machine Vision: Recent Progress of Artificial Intelligence in Geography
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DOI：
10.3390/ijgi11070385
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发表时间：
2022-07
期刊：
影响因子：
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通讯作者：
Wenwen Li;Chia-Yu Hsu
中科院分区：
文献类型：
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作者：
Wenwen Li;Chia-Yu Hsu
GeoAI，即地理空间人工智能，已经成为一个热门话题，也是地理学空间分析的前沿。尽管在探索人工智能和地理学的整合方面取得了很大进展，但还没有明确的地理人工智能的定义、它的研究范围，也没有关于它如何使社会科学和环境科学能够以新的方式解决问题的广泛讨论。本文综述了用于大比例尺影像分析的GeoAI研究的方法论基础、地理空间应用的最新进展以及与传统方法的比较优势。我们根据不同类型的图像或结构化数据，包括卫星和无人机图像、街景和地球科学数据，以及它们在各种图像分析和机器视觉任务中的应用，来组织对GeoAI研究的回顾。虽然不同的应用程序倾向于使用不同类型的数据和模型，但我们总结了GeoAI研究的六大优势，包括(1)实现大规模分析；(2)自动化；(3)高精度；(4)检测细微变化的敏感度；(5)数据中的噪声容忍度；(6)快速技术进步。由于GeoAI仍然是一个快速发展的领域，我们还描述了当前的知识差距，并讨论了未来的研究方向。
GeoAI, or geospatial artificial intelligence, has become a trending topic and the frontier for spatial analytics in Geography. Although much progress has been made in exploring the integration of AI and Geography, there is yet no clear definition of GeoAI, its scope of research, or a broad discussion of how it enables new ways of problem solving across social and environmental sciences. This paper provides a comprehensive overview of GeoAI research used in large-scale image analysis, and its methodological foundation, most recent progress in geospatial applications, and comparative advantages over traditional methods. We organize this review of GeoAI research according to different kinds of image or structured data, including satellite and drone images, street views, and geo-scientific data, as well as their applications in a variety of image analysis and machine vision tasks. While different applications tend to use diverse types of data and models, we summarized six major strengths of GeoAI research, including (1) enablement of large-scale analytics; (2) automation; (3) high accuracy; (4) sensitivity in detecting subtle changes; (5) tolerance of noise in data; and (6) rapid technological advancement. As GeoAI remains a rapidly evolving field, we also describe current knowledge gaps and discuss future research directions.