Building Height Estimation using Street-View Images, Deep-Learning, Contour Processing, and Geospatial Data
Bibliographic record
Abstract
In the recent years, there has been an increasing interest in extracting data from street-view images. This includes various applications such as estimating the demographic makeup of neighborhoods to building instance classification. Building height is an important piece of information that can be used to enrich two-dimensional footprints of buildings, and enhance analysis on such footprints (e.g., economic analysis, urban planning). In this paper, a proposed algorithm (and its open-source implementation) for automatic estimation of building height from street-view images, using Convolutional Neural Networks (CNNs) and image processing techniques, is presented. The algorithm also utilizes geospatial data that can be obtained from different sources. The algorithm will ultimately be used to enrich the Open Database of Buildings (ODB), that has been published by Statistics Canada, as a part of the Linkable Open Data Environment (LODE). Some of the obtained results for building height estimation are presented in this paper. Furthermore, current and future improvements, some challenging cases and the scalability of the system are discussed.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 teacher head, 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".