Building damage risk in sinking Indian megacities
Bibliographic record
Abstract
Building damage poses serious safety risks, causing substantial financial losses worldwide. Engineering shortcomings are commonly cited as the cause of long-term structural failures, often neglecting the exacerbating role of land subsidence. Here we used satellite radar observations during 2015–2023 to estimate differential settlements at 5 fast-growing Indian megacities, including more than 13 million buildings and 80 million people. Our analysis reveals 878 km² of land subsiding, exposing ~1.9 million people to subsidence rates of more than 4 mm yr−1. An estimated 2,406 buildings across Delhi, Mumbai and Chennai are at high risk of structural damage from ongoing land subsidence. Sustained over 50 years, current subsidence rates could place as many as 23,529 buildings at very high risk of structural damage in Chennai, Delhi, Mumbai, Kolkata and Bengaluru. Our results highlight the compounding risk of infrastructure damage from subsidence, assisting policymakers to develop resilience plans and adaptation strategies that prioritize mitigation and maintenance spending. Building damage and collapse is an emerging problem, particularly in large cities in developing countries. Although engineering shortcomings are often blamed, land subsidence is an underappreciated culprit that could put tens of thousands of buildings at risk across Indian megacities.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".