A MULTI-HEIGHT LOD1 MODEL OF ALL BUILDINGS IN THE NETHERLANDS

12 citations as recorded by crossref.

1. Towards automatic reconstruction of 3D city models tailored for urban flow simulations I. Pađen et al. https://doi.org/10.3389/fbuil.2022.899332
2. Inferring the number of floors for residential buildings E. Roy et al. https://doi.org/10.1080/13658816.2022.2160454
3. Scalable 3D mapping of cities using computer vision and signals of opportunity A. Basiri et al. https://doi.org/10.1080/13658816.2023.2191674
4. The City of Tomorrow from… the Data of Today G. Agugiaro et al. https://doi.org/10.3390/ijgi9090554
5. 3D GIS modelling of road and building material stocks: A case study of Grenada L. Ye et al. https://doi.org/10.1016/j.resconrec.2025.108605
6. An Integrated Approach for 3D Solar Potential Assessment at the City Scale H. Waqas et al. https://doi.org/10.3390/rs15235616
7. Virtual 3D Campus for Universiti Teknologi Malaysia (UTM) S. Salleh et al. https://doi.org/10.3390/ijgi10060356
8. PLANES4LOD2: Reconstruction of LoD-2 building models using a depth attention-based fully convolutional neural network P. Schuegraf et al. https://doi.org/10.1016/j.isprsjprs.2024.04.015
9. ISSLIDE: A New InSAR Dataset for Slow SLIding Area DEtection With Machine Learning A. Bralet et al. https://doi.org/10.1109/LGRS.2024.3365299
10. Distributed processing of Dutch AHN laser altimetry changes of the built-up area M. Cserép & R. Lindenbergh https://doi.org/10.1016/j.jag.2022.103174
11. Building footprint data for countries in Africa: To what extent are existing data products comparable? H. Chamberlain et al. https://doi.org/10.1016/j.compenvurbsys.2024.102104
12. Reconstructing historical 3D city models C. Morlighem et al. https://doi.org/10.1007/s44212-022-00011-3