A Contemporary Survey on Multisource Information Fusion for Smart Sustainable Cities: Emerging Trends and Persistent Challenges
Bibliographic record
Abstract
The emergence of smart sustainable cities has unveiled a wealth of data sources , each contributing to a vast array of urban applications. At the heart of managing this plethora of data is multisource information fusion (MSIF), a sophisticated approach that not only improves the quality of data collected from myriad sources, including sensors, satellites, social media, and citizen-generated content, but also aids in generating actionable insights crucial for sustainable urban management. Unlike simple data fusion , MSIF excels in harmonizing disparate data sources, effectively navigating through their variability, potential conflicts, and the challenges posed by incomplete datasets. This capability is essential for ensuring the integrity and utility of information, which supports comprehensive insights into urban systems and effective planning. This survey combines hierarchical and multi-dimensional classification to examine how MSIF integrates and analyses diverse datasets, enhancing the operational efficiency and intelligence of urban environments. It highlights the most significant challenges and opportunities presented by MSIF in smart sustainable cities, particularly how it overcomes the limitations of existing approaches in scope and coverage. By considering social, economic, and environmental factors, MSIF offers a multidisciplinary approach that is pivotal for advancing sustainable urban development . Recognized as an essential resource for academics and practitioners, this study promotes a new wave of MSIF innovations aimed at improving the cohesion, efficiency, and sustainability of smart cities.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.016 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.012 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".