Urban geological information platform for smart city construction: A shift from public service to integration with urban engineering
Bibliographic record
Abstract
ABSTRACT Urban geological information platforms have traditionally focused on static data provision for public service, constrained by funding and limited engagement with engineering applications. This study takes Hangzhou—a major Chinese megacity—as a model to propose a technically integrated platform that aligns with urban infrastructure development, particularly underground space engineering. Through the adoption of the large-scale relational database system Oracle, we first established a comprehensive storage framework for fundamental urban geological and underground infrastructure information, thereby completing the construction of the core databases. To ensure spatial consistency across multi-source data and to meet the platform’s high computational demands while improving overall server responsiveness, we introduced three critical innovations: voxel-based model encoding, distributed computing, and frontend-backend separation with asynchronous processing. To align with urban engineering projects and enhance economic returns, the platform was initially developed through the integration of foundational geological data, including borehole records and aboveground-underground spatial information. Building on this foundation, its practical application in Hangzhou’s Qiantang New Town further demonstrates the platform’s potential in supporting subway routing, underground structure planning, and engineering cost analysis. Consequently, the construction of the Hangzhou geological information platform not only offers robust support for urban decision-making and smart city development but also provides a replicable model for addressing the technical and institutional challenges commonly encountered in the development of urban geological platforms.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".