Supporting Urban Energy Efficiency With A Volunteered Geographic Information (VGI) System: A Calgary Case Study
Bibliographic record
Abstract
The Heat Energy Assessment Technologies (HEAT) project uses high-resolution airborne thermal imagery, GIS cadastral data, and Geographic Object-Based Image Analysis (GEOBIA) to allow the citizens of Calgary, Alberta, Canada to visualize the amount and location of waste heat leaving their houses, communities, and the city. This information is presented to them in an interactive and multi-scale Geoweb application with three energy efficiency feedback solutions: (i) HEAT Scores, (ii) Hot Spots, and (iii) Estimated Savings – that help residents reduce their greenhouse gas emissions and save their money. To ensure the accuracy of these measures, the correct emissivity of roof materials needs to be known. However, roof material information is not readily available in the Canadian public domain. To overcome this challenge, a new and unique Volunteered Geographic Information (VGI) system was developed using Google Street View and Google Satellite data that engages citizens to classify the roof materials of single dwelling residences in a simple and intuitive manner. Since data credibility, quality, and accuracy are major concerns when using VGI, a private Multiple Listing Services (MLS) dataset was used for cross-verification. Results show that from May-November 2013, 1,244 volunteers from 85 cities and 14 countries classified 1,815 roofs in the study area. Additional analysis reveals (i) a 72% match between the VGI and MLS data, and (ii) in the majority of cases, roofs with greater than, or equal to 5 contributions have the same material defined in both datasets. These results demonstrate that citizens are engaged in correctly classifying the roof materials of houses, and implementing changes to the HEAT VGI system based on feedback from volunteers can further improve data quality and increase participation. Furthermore, to the best of author’s knowledge, this is the first time that Google Street View has been used for classifying roof materials, or has been implemented in the domain of VGI. By building on the lessons learned and the success of this research, we suggest that similar VGI systems may be implemented to create new geo-information in support of urban energy efficiency.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".