From satellite imagery to material outflows: master plan and deep learning-based quantification of demolition materials to support circular construction.
- DOI
- 10.1007/s40201-026-00978-0
- Published
- 2026 Jun
- Container
- Journal of environmental health science & engineering
- Publisher
- Not recorded
- Open access
- yes
Credibility signals
limited evidence Score 45/100 under policy 1.0.0. This is a metadata assessment, not a judgment of the paper's conclusions.
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Cite this work
BibTeX
@article{allodium:10.1007/s40201-026-00978-0,
title = {From satellite imagery to material outflows: master plan and deep learning-based quantification of demolition materials to support circular construction.},
author = {Kumisbek A and Nauyryzbay A and Karaca F and Guney M},
year = {2026},
journal = {Journal of environmental health science \& engineering},
doi = {10.1007/s40201-026-00978-0},
url = {https://doi.org/10.1007/s40201-026-00978-0}
}RIS
TY - JOUR TI - From satellite imagery to material outflows: master plan and deep learning-based quantification of demolition materials to support circular construction. AU - Kumisbek A AU - Nauyryzbay A AU - Karaca F AU - Guney M PY - 2026 JO - Journal of environmental health science & engineering DO - 10.1007/s40201-026-00978-0 UR - https://doi.org/10.1007/s40201-026-00978-0 ER -
APA
A, K., A, N., F, K., & M, G. (2026). From satellite imagery to material outflows: master plan and deep learning-based quantification of demolition materials to support circular construction.. Journal of environmental health science & engineering. https://doi.org/10.1007/s40201-026-00978-0
Source records
- pubmed · retrieved 2026-09-25T06:12:00.074Z