.png){kind=icon}
返回
GeoAI enabled urban computing: status and challenges
.png){kind=icon}
OA
AI
DOI：10.1080/19475683.2025.2552152.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Geospatial Artificial Intelligence (GeoAI), an interdisciplinary field integrating geographic information science with artificial intelligence (AI), has emerged as a transformative force in advancing urban computing technologies and applications. By synergizing the inherent spatiotemporal characteristics of geospatial data with AI’s advanced inferential capabilities, GeoAI provides innovative methodologies for addressing multifaceted urban challenges. Therefore, we first systematically examined the core technological components of GeoAI, encompassing geospatial data representation, spatiotemporal interpolation and prediction, geo-related knowledge graphs and pretrained spatiotemporal foundation models. Then, we analysed GeoAI’s implementation in urban computing through four representative domains, including including intelligent transportation systems, environmental surveillance, public safety enhancement, and sustainable urban development. Finally, we concluded key challenges in GeoAI-enabled urban computing, emphasizing the integration of deep learning and knowledge graph, interdisciplinary collaboration for intelligent solutions, risk mitigation of deceptive spatiotemporal data, and the incorporation of human-centric principles in GeoAI technologies.
Keyword:
GeoAI
urban computing
advances
challenges
期刊
IF:
3.3
论文数:
76
被引数:
1.1K
机构
引用论文
.png){kind=icon}
A multi-task deep learning model for short-term taxi demand forecasting considering spatiotemporal dependences考虑时空依赖性的多任务深度学习短期出租车需求预测模型
OAAI
OAAI分享收藏
查看更多内容