How will ai transform urban observing, sensing, imaging, and mapping?
Advances in artificial intelligence (AI) and Earth observation (EO) have transformed urban studies. This paper provides a commentary on how the AI-EO integration offers advancements in urban studies and applications. We conclude that AI will provide a deeper interpretation and autonomous identification of urban issues and the creation of customized urban designs. Open issues remain, especially in integrating diverse geospatial big data, data security, and developing a general analytical framework.
Authors
- Qihao Weng (ORCID: https://orcid.org/0000-0002-2498-0934)
- Paolo Gamba (ORCID: https://orcid.org/0000-0002-9576-6337)
- Xiaoyan Lu (ORCID: https://orcid.org/0000-0002-9026-8195)
- Rongjun Qin (ORCID: https://orcid.org/0000-0002-5896-1379)
- Tiangang Yin (ORCID: https://orcid.org/0000-0002-2149-6004)
- Gregory P. Asner (ORCID: https://orcid.org/0000-0001-7893-6421)
- Micheal Ying Yang
- Qiming Zheng (ORCID: https://orcid.org/0000-0002-7393-6585)
- Xiao Xiang Zhu (ORCID: https://orcid.org/0000-0001-5530-3613)
- Yonghao Xu (ORCID: https://orcid.org/0000-0002-6857-0152)
- Yinxia Cao (ORCID: https://orcid.org/0000-0002-5896-2814)
- Wei Huang (ORCID: https://orcid.org/0000-0001-8324-8877)
- Peifeng Ma (ORCID: https://orcid.org/0000-0002-1457-5388)
- Fan Zhang (ORCID: https://orcid.org/0000-0003-3291-7887)
- Zhiwei Li (ORCID: https://orcid.org/0000-0001-5635-8499)
- Yuhan Zhou (ORCID: https://orcid.org/0000-0002-6657-7617)
Institutions
- Linköping University (SE)
- Tongji University (CN)
- Hong Kong Polytechnic University (HK)
- Chinese University of Hong Kong (HK)
- Peking University (CN)
- University of Pavia (IT)
- Chinese University of Hong Kong, Shenzhen (CN)
- The Ohio State University (US)
- Arizona State University (US)
- Technical University of Munich (DE)
- University of Twente (NL)
Publication Details
- Journal
- npj Urban Sustainability
- Published
- 2024-11-28
- DOI
- https://doi.org/10.1038/s42949-024-00188-3
- Citations
- 21
- Primary Topic
- Automated Road and Building Extraction
- Type
- article
- Field-Weighted Citation Impact
- 7.05