# DeepLCZChange: A Remote Sensing Deep Learning Model Architecture for Urban Climate Resilience

arXiv: https://arxiv.org/abs/2306.06269 · HTML: https://mlsanity.com/paper/2306.06269/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 14 references that are in the map.
- First posted on arXiv 9 Jun 2023.

## Builds on (most cited first, 4 of 4)

- [A Cookbook of Self-Supervised Learning](https://mlsanity.com/paper/2304.12210/) (2023)
- [Self-supervised Learning in Remote Sensing: A Review](https://mlsanity.com/paper/2206.13188/) (2022)
- [AutoGeoLabel: Automated Label Generation for Geospatial Machine Learning](https://mlsanity.com/paper/2202.00067/) (2022)
- [Monitoring Urban Forests from Auto-Generated Segmentation Maps](https://mlsanity.com/paper/2206.06948/) (2022)

## Built on by (most cited first, 2 of 2)

- [Building Floor Number Estimation from Crowdsourced Street-Level Images: Munich Dataset and Baseline Method](https://mlsanity.com/paper/2505.18021/) (2025)
- [AutoLCZ: Towards Automatized Local Climate Zone Mapping from Rule-Based Remote Sensing](https://mlsanity.com/paper/2405.13993/) (2024)

## Abstract (arXiv)

Urban land use structures impact local climate conditions of metropolitan areas. To shed light on the mechanism of local climate wrt. urban land use, we present a novel, data-driven deep learning architecture and pipeline, DeepLCZChange, to correlate airborne LiDAR data statistics with the Landsat 8 satellite's surface temperature product. A proof-of-concept numerical experiment utilizes corresponding remote sensing data for the city of New York to verify the cooling effect of urban forests.