Abstract
Remote sensing data and methods have become indispensable for observing and modeling the Earth and have great potential for monitoring a substantial portion of the targets defined under the United Nations Sustainable Development Goals (SDGs). This study investigates remote sensing research on SDG 11 (sustainable cities and communities) from 2016 to 2023, highlighting the growing interest in the field. By evaluating a large number of selected articles (6820) using a specialized keyword selection strategy and various filters, a significant increase in publication frequency was observed. Remote Sensing and Sustainability were found to be the most relevant journals. A trend towards research addressing urban ecological quality, changes in land use patterns, and the impact of impervious surfaces was found in domain-specific citations. Semi-niche motor themes encompass deep learning, feature extraction, and semantic segmentation. Simultaneously, remote sensing, machine learning, and change detection serve as foundational motor themes, merging elements of both basic and motor themes. The introduction of new analytical methods (e.g., new indices), together with the use of open data and crowdsourcing, has gained great interest. While there has been a strong focus on land cover, urban expansion, and land surface temperature, the main gaps were identified in regional development, disaster, resilience, natural and cultural heritage, housing, and inclusiveness. The findings show the significance of remote sensing research and its practical applications for shaping urban policy, planning strategies, and sustainable urban development. By extracting research patterns using centrality and density analyses and identifying underexplored areas, valuable insights into relationships, significance, and developmental progress within SDG 11-related remote sensing research were gained and may contribute to future planning and informing policymaking decisions.
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