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Generalized Vertical Components of built-up areas from global Digital Elevation Models by multi-scale linear
Martino Pesaresi1, Christina Corbane1, Chao Ren2,3
1European Commission, Joint Research Centre (JRC), Directorate for Space, Security & Migration, Ispra, Italy.
This study estimates the vertical components of built-up areas using global Digital Elevation Models (DEMs), finding that gross building height estimates are more accurate than net estimates. The best results were achieved using a composite DEM and multivariate analysis for urban vertical characterization.
Area of Science:
- Remote Sensing and Geospatial Analysis
- Urban Morphology and Planning
- Earth Observation Data Applications
Background:
- Accurate estimation of built-up area vertical components is crucial for urban studies.
- Global Digital Elevation Models (DEMs) offer potential but require processing for urban features.
- Existing DEMs may filter out essential building height information.
Purpose of the Study:
- To generalize vertical components of built-up areas from free global DEM data.
- To evaluate various DEM sources, filtering techniques, and generalization methods.
- To introduce and assess four generalized vertical components: AGBH, ANBH, SGBH, SNBH.
Main Methods:
- Applied multi-scale convolutional, morphological, and textural transforms to global DEMs.
- Utilized linear least-squares regression techniques for generalization at 250m resolution.
- Evaluated five global DEMs and two composites across 60 filtering/generalization combinations.
Main Results:
- Net vertical component (ANBH, SNBH) estimations showed higher errors than gross estimations (AGBH, SGBH).
- A composite DEM (SRTM30 + AW3D30_U) was optimal for univariate regression.
- Multivariate models achieved RMSEs of 2.40m (AGBH), 3.25m (SGBH), 6.63m (ANBH), and 4.38m (SNBH).
Conclusions:
- Global DEMs can derive generalized vertical building characteristics at 250m scale.
- DEM horizontal resolution and processing filters limit net vertical component accuracy.
- Results are valuable for spatial population modeling, urban morphology, and climate studies.
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