Maximum Temperature Prediction Using Remote Sensing Data Via Convolutional Neural Network
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.
Code (0)
등록된 구현이 없습니다.
Tasks
Data IntegrationSimilar Papers 제목 키워드 기반
Novel Machine Learning Approach for Predicting Poverty using Temperature and Remote Sensing Data in Ethiopia
In many developing nations, a lack of poverty data prevents critical humanitarian organizations from responding to large-scale crises. Currently, socioeconomic surveys are the only method implemented on a large scale for…
HumanitarianSurveyTransfer LearningEstimating the Impact of Weather on Agriculture
This paper quantifies the significance and magnitude of the effect of measurement error in remote sensing weather data in the analysis of smallholder agricultural productivity. The analysis leverages 17 rounds of nationa…
SurveyPrediction of fish location by combining fisheries data and sea bottom temperature forecasting
This paper combines fisheries dependent data and environmental data to be used in a machine learning pipeline to predict the spatio-temporal abundance of two species (plaice and sole) commonly caught by the Belgian fishe…
An landcover fuzzy logic classification by maximumlikelihood
In present days remote sensing is most used application in many sectors. This remote sensing uses different images like multispectral, hyper spectral or ultra spectral. The remote sensing image classification is one of t…
ClassificationGeneral Classificationimage-classificationImage Classification+1Breaking the Limits of Remote Sensing by Simulation and Deep Learning for Flood and Debris Flow Mapping
We propose a framework that estimates inundation depth (maximum water level) and debris-flow-induced topographic deformation from remote sensing imagery by integrating deep learning and numerical simulation. A water and …
Change Detection