Identifying Hard-to-Decarbonize houses from multi-source data in Cambridge, UK
Bibliographic record
Abstract
As the urban population continues to expand and is expected to comprise 80% of the total population in 2050, it is crucial to ensure the sustainability and energy efficiency of cities. Among all the homes globally, Hard-to-Decarbonize (HtD) buildings are estimated to be a quarter of them. Identifying the HtD houses and proposing strategies for these houses is important to reach the global net zero target. However, the study of HtD houses has historically been neglected. Previous studies mainly focus on simulating, predicting and understanding attributes that are directly related to energy usage and efficiency. In this research, a methodology for identifying HtD buildings with publicly available data is proposed and tested in Cambridge, UK. A dataset of HtD houses in Cambridge is organized, with criteria derived from the Energy Performance Certificate (EPC), which results from detailed inspections of houses. Street view images (SVI), ariel view images (AVI), land surface temperature (LST), and building stock data are used together for the prediction with deep learning. The classification accuracy for HtD buildings is able to achieve 82\%. This study also explores the minimal data needed for the high-accuracy prediction of HtD houses. Results show that SVI contributes the most to the prediction.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".