Assessment of Local Climate Zone Products via Simplified Classification Rule with 3D Building Maps (2309.15978v1)
Abstract: This study assesses the performance of a global Local Climate Zone (LCZ) product. We examined the built-type classes of LCZs in three major metropolitan areas within the U.S. A reference LCZ was constructed using a simple rule-based method based on high-resolution 3D building maps. Our evaluation demonstrated that the global LCZ product struggles to differentiate classes that demand precise building footprint information (Classes 6 and 9), and classes that necessitate the identification of subtle differences in building elevation (Classes 4-6). Additionally, we identified inconsistent tendencies, where the distribution of classes skews differently across different cities, suggesting the presence of a data distribution shift problem in the machine learning-based LCZ classifier. Our findings shed light on the uncertainties in global LCZ maps, help identify the LCZ classes that are the most challenging to distinguish, and offer insight into future plans for LCZ development and validation.
- “A global map of local climate zones to support earth system modelling and urban-scale environmental science,” Earth System Science Data, vol. 14, no. 8, pp. 3835–3873, 2022.
- “Combining expert and crowd-sourced training data to map urban form and functions for the continental us,” Scientific data, vol. 7, no. 1, pp. 1–13, 2020.
- “Local climate zones for urban temperature studies,” Bulletin of the American Meteorological Society, vol. 93, no. 12, pp. 1879–1900, 2012.
- “Conterminous united states land cover change patterns 2001–2016 from the 2016 national land cover database,” ISPRS Journal of Photogrammetry and Remote Sensing, vol. 162, pp. 184–199, 2020.
- “Urbanwatch: A 1-meter resolution land cover and land use database for 22 major cities in the united states,” Remote Sensing of Environment, vol. 278, pp. 113106, 2022.
- “Dynamic world, near real-time global 10 m land use land cover mapping,” Scientific Data, vol. 9, no. 1, pp. 1–17, 2022.
- “Lcz generator: A web application to create local climate zone maps,” Frontiers in Environmental Science, vol. 9, pp. 637455, 2021.
- “An unsupervised, open-source workflow for 2d and 3d building mapping from airborne lidar data,” arXiv preprint arXiv:2205.14585, 2022.
- “Challenges in building extraction from airborne lidar data: ground-truth, building boundaries, and evaluation metrics,” in Proceedings of the 30th International Conference on Advances in Geographic Information Systems, 2022, pp. 1–4.
- “The accuracy and consistency of 3d elevation program data: A systematic analysis,” Remote Sensing, vol. 14, no. 4, pp. 940, 2022.
- “Scalable surface water mapping up to fine-scale using geometric features of water from topographic airborne lidar data,” arXiv preprint arXiv:2301.06567, 2023.
- Hunsoo Song (5 papers)
- Gaia Cervini (1 paper)
- Jinha Jung (4 papers)