Maximum Temperature Prediction Using Remote Sensing Data Via Convolutional Neural Network

TL;DR

This paper presents a convolutional neural network model that integrates remote sensing and meteorological data to accurately predict urban temperature peaks, aiding climate management in cities.

Contribution

It introduces a novel deep learning approach combining satellite and weather data for high-resolution urban temperature forecasting.

Findings

Achieved MAE of 2.09°C in temperature prediction

Validated model effectiveness on 2023 data in Turin

Enhanced understanding of urban microclimate patterns

Abstract

Urban heat islands, defined as specific zones exhibiting substantially higher temperatures than their immediate environs, pose significant threats to environmental sustainability and public health. This study introduces a novel machine-learning model that amalgamates data from the Sentinel-3 satellite, meteorological predictions, and additional remote sensing inputs. The primary aim is to generate detailed spatiotemporal maps that forecast the peak temperatures within a 24-hour period in Turin. Experimental results validate the model's proficiency in predicting temperature patterns, achieving a Mean Absolute Error (MAE) of 2.09 degrees Celsius for the year 2023 at a resolution of 20 meters per pixel, thereby enriching our knowledge of urban climatic behavior. This investigation enhances the understanding of urban microclimates, emphasizing the importance of cross-disciplinary data integration, and laying the groundwork for informed policy-making aimed at alleviating the negative impacts of extreme urban temperatures.