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

Wenlu Sun, Yao Sun, Chenying Liu, Conrad M Albrecht
6/9/2023
cs.CVcs.AIcs.LG

No confident code implementation found for this paper. 1 possible match can be shown below.

Abstract

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.

View on arXivView PDF

Code Implementations

No confident code match yet

We couldn't find an author-owned or strongly-evidenced community implementation for this paper. 1 weaker match is hidden by default — verify before relying on them.

No code implementations found yet.

Know of an implementation? Let us know in the comments below!

Similar Papers

Planning for Cooler Cities: A Multimodal AI Framework for Predicting and Mitigating Urban Heat Stress through Urban Landscape Transformation

86% match2025

Machine Learning Framework for High-Resolution Air Temperature Downscaling Using LiDAR-Derived Urban Morphological Features

84% match1 repo32024

Detection and Simulation of Urban Heat Islands Using a Fine-Tuned Geospatial Foundation Model

84% match2025

Daily Land Surface Temperature Reconstruction in Landsat Cross-Track Areas Using Deep Ensemble Learning With Uncertainty Quantification

84% match2025

Detection and Simulation of Urban Heat Islands Using a Fine-Tuned Geospatial Foundation Model for Microclimate Impact Prediction

84% match2025

Tackling extreme urban heat: a machine learning approach to assess the impacts of climate change and the efficacy of climate adaptation strategies in urban microclimates

83% match2024

Cite this paper

@article{sun2023deeplczchange,
  title  = {DeepLCZChange: A Remote Sensing Deep Learning Model Architecture for Urban Climate Resilience},
  author = {Wenlu Sun and Yao Sun and Chenying Liu and Conrad M Albrecht},
  year   = {2023},
  eprint = {2306.06269},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2306.06269v1}
}

Add a badge to your README

Shows whether official code is linked for this paper.

Code of Paper badge
[![Code of Paper](https://codeofpaper.com/badge/2306.06269.svg)](https://codeofpaper.com/paper/2306.06269)

Discussion