As cities grow, demand for urban materials is set to rise. Meeting sustainability targets will require transformative changes to how cities are constructed. Yet, accurate information on embodied building materials and their environmental impacts at the city scale is still lacking. We use Light Detection and Ranging data, building archetype information, and statistical models to estimate the embodied materials in buildings in Canberra, Australia, and their energy, carbon, and water footprint. In 2015, 57 million tonnes (Mt) of materials were embodied in 140,805 buildings. By weight, concrete was the most used material (44%), followed by sand and stone (32%), and ceramics (11%). Current population growth and building construction trends indicate a need for 2.4 times the building materials stock of 2015 by 2060.
| Authors | Huang, W. and Olsson, P.-O. and Kanters, J. and Harrie, L. |
|---|---|
| Year | 2020 |
| Venue | ISPRS Annals of Photogrammetry, Remote Sensing & Spatial Information Sciences |
| DOI | 10.5194/isprs-annals-vi-4-w1-2020-93-2020 |
| Source Database | EBSCO (Applied Science & Technology) |
| Bridge-to-GNN | Category B |
| GNN Architecture | Knowledge Graph Embedding (inferred from title) |
| Graph Encoding | BIM Element Topology Graph (inferred from title) |
| AEC Task | Energy Estimation (inferred from title) |
| Cohort | Early Infrastructure (2020–2021) |
| Dataset Size | 140,805 buildings (heuristic — verify) |
|---|---|
| Primary Metric | Accuracy (heuristic — verify) |
| Primary Value | 83% (heuristic — verify) |
| Quality Assessment | 1/6 · rigor: Low · Baseline: ✓ (heuristic — verify) |
| GNN architectures detected | Knowledge Graph Embedding (TransE/RotatE/DistMult) |
|---|---|
| Benchmark datasets referenced | CityGML |
| Dataset stats found | 140,805 buildings · 2015 buildings |
| Reported metrics + values | Accuracy: 83% |
| Quality (heuristic, 6-flag) | 2/6 · rigor: Low Code/Data: —Benchmark: ✓Baseline: ✓CV/Split: —Ablation: —Reproducible: — |
| Bibliometrically related to other seeds | This paper appeared in the Connected Papers neighbourhood of these corpus seeds: [8] 2020 · Hu · Graph2Plan: Learning Floorplan Generation from Layout Graphs |
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Part of the GML/GNN in AEC Systematic Review (PRISMA 2020) — 112 papers, 2020–2026