Quantification of construction and demolition waste disposal behaviors during COVID-19 using satellite imagery
Bibliographic record
Abstract
The COVID-19 pandemic disrupted conventional municipal solid waste (MSW) management practices and affected waste generation rates. While MSW streams have been extensively studied and reported, the impact on construction and demolition (C&D) waste remains overlooked. This research develops an innovative analytical framework utilizing satellite imagery to quantify C&D waste disposal rates during COVID-19 restrictions in a mid-sized Canadian city. Supervised classification of Landsat-8 images is conducted to derive the settlement area over a period of 8.8 years (2014-2022). The C&D disposal rates and settlement area relationship is evaluated using regression analysis. Results reveal a 73.4% reduction in mean weekly C&D disposal in 2020 compared to pre-pandemic years, reflecting diminished construction activity. The settlement area exhibits a strong positive correlation (R 2 =0.812) with per capita C&D disposal rate, providing spatial evidence of urbanization patterns affecting C&D waste generation. Among socioeconomic factors examined, the value of building permits issued most influences C&D quantities (R 2 =0.934). The satellite imagery-based approach allows indirect estimation of disrupted C&D waste streams when on-site auditing is restricted during pandemics. The framework offers municipal authorities spatial decision support to formulate data-driven C&D waste management policies that are essential to smart cities and resilient to future public health emergencies. • COVID Construction and Demolition Waste (CDW) disposal behaviors are quantified • An original analytical approach is proposed to estimate CDW at regional level • Changes in settlement areas are calculated using Landsat-8 imagery and GIS tools • Correlations between CDW quantity and socio-economic factors are examined • The proposed approach assists in the development of data-driven waste policies
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".