ArticlePublisher preview available

Integrative Remote Sensing Approaches Using Generative Adversarial Networks for Urban Heat Island Analysis and Mitigation

Authors:
  • Sindhi College
To read the full-text of this research, you can request a copy directly from the authors.

Abstract and Figures

The phenomenon of urban heat islands (UHI) presents a critical challenge for urban sustainability, exacerbating local temperatures, increasing energy demands, and impairing public health. Traditional methods for addressing UHI are often resource-intensive and slow. This study introduces a novel approach, utilizing a Hybrid CycleGAN-SVM (HCGS) model that leverages the synergy of Generative Adversarial Networks (GANs) and Support Vector Machines (SVMs) to efficiently analyze and mitigate UHI effects through high-resolution satellite imagery and temperature data. The model incorporates Enhanced Vision Transformers (EViTs) for superior feature extraction, adept at capturing intricate spatial and spectral urban patterns. The CycleGAN component of the model generates high-quality synthetic imagery, enhancing the dataset and addressing class imbalances, thereby bolstering the SVM classifier’s ability to precisely pinpoint heat-prone urban areas. Implemented in Google Colab, the HCGS model demonstrated exceptional performance, achieving a classification accuracy of 0.98. This indicates its potential as an effective tool for urban heat mitigation, offering actionable insights for urban planning and policy-making. By integrating advanced machine learning techniques with remote sensing data, the HCGS model paves the way for innovative climate adaptation strategies, fostering more sustainable and resilient urban environments.
This content is subject to copyright. Terms and conditions apply.
Vol.:(0123456789)
Remote Sensing in Earth Systems Sciences (2024) 7:681–698
https://doi.org/10.1007/s41976-024-00156-6
RESEARCH
Integrative Remote Sensing Approaches Using Generative Adversarial
Networks forUrban Heat Island Analysis andMitigation
GanapathySundar1· PitchaimuthuPatchaiammal1· BalajiVijayanVenkateshwarulu2· ThangavelPradeeshKumar3·
KesavamoorthyRajamannar4· RajeshKumarTripathi5
Received: 3 September 2024 / Revised: 1 October 2024 / Accepted: 19 October 2024 / Published online: 28 October 2024
© The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024
Abstract
The phenomenon of urban heat islands (UHI) presents a critical challenge for urban sustainability, exacerbating local
temperatures, increasing energy demands, and impairing public health. Traditional methods for addressing UHI are often
resource-intensive and slow. This study introduces a novel approach, utilizing a Hybrid CycleGAN-SVM (HCGS) model
that leverages the synergy of Generative Adversarial Networks (GANs) and Support Vector Machines (SVMs) to efficiently
analyze and mitigate UHI effects through high-resolution satellite imagery and temperature data. The model incorporates
Enhanced Vision Transformers (EViTs) for superior feature extraction, adept at capturing intricate spatial and spectral urban
patterns. The CycleGAN component of the model generates high-quality synthetic imagery, enhancing the dataset and
addressing class imbalances, thereby bolstering the SVM classifier’s ability to precisely pinpoint heat-prone urban areas.
Implemented in Google Colab, the HCGS model demonstrated exceptional performance, achieving a classification accuracy
of 0.98. This indicates its potential as an effective tool for urban heat mitigation, offering actionable insights for urban plan-
ning and policy-making. By integrating advanced machine learning techniques with remote sensing data, the HCGS model
paves the way for innovative climate adaptation strategies, fostering more sustainable and resilient urban environments.
Keywords Urban heat island· UHI effect· Hybrid CycleGAN-SVM· HCGS model· Generative Adversarial Networks·
GANs· Support Vector Machines· SVM· Enhanced Vision Transformers· EViTs· Satellite imagery
1 Introduction
The urban heat island (UHI) is an increase in temperature
over the urban area compared to rural areas, originated
and amplified by human activities and artificial salts. This
is primarily driven by the high use of impervious surfaces
that store and release heat, as well lack of vegetation that
typically provides cooling via evapotranspiration [1, 2]. The
UHI effect has troubling consequences: more energy must be
consumed, emissions of air pollutants and greenhouse gases
become higher in urban areas than surrounding regions,
and human health and comfort are increasingly compro-
mised while water resources will get limited. Reducing the
urban heat island effect is critical for making cities more
* Ganapathy Sundar
sundarganapathy66@gmail.com
Pitchaimuthu Patchaiammal
sarandsk1@gmail.com
Balaji Vijayan Venkateshwarulu
psgbala.vijayan@gmail.com
Thangavel Pradeesh Kumar
pradeeshkumar.t@vit.ac.in
Kesavamoorthy Rajamannar
Kesavamoorthycse@gmail.com
Rajesh Kumar Tripathi
rajesh.tripathi@gla.ac.in
1 Department ofComputer Applications, Sindhi College,
Chennai, India
2 Department ofInformation Science andEngineering,
HKBK College ofEngineering, VTU, Belagum, Nagawara,
Bengaluru, India
3 VIT School ofAgricultural Innovations andAdvanced
Learning (VAIAL), VIT, Vellore, India
4 Department ofComputer Science andEngineering, CMR
Institute ofTechnology, Bengaluru, India
5 Department ofComputer Engineering andApplications,
GLA University, Uttar Pradesh, Mathura, India
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
ResearchGate has not been able to resolve any citations for this publication.
Article
With the growing interest in mitigating urban heat islands (UHIs), the cooling effects of green infrastructure (GI) have been extensively studied. Despite its importance, there is limited exploration of how GI improves street-level outdoor thermal comfort (OTC) for pedestrians. This study addresses this gap by examining how urban greenery and water bodies help alleviate Outdoor Thermal Comfort derived Urban Heat Islands (OTC-UHIs). Our study results indicate that GI significantly mitigates the OTC-UHIs in hot-humid summer conditions. Highly developed urban areas exhibit smaller land surface temperature differences between day and night due to high heat capacity in buildings and pavement, creating a tropical nighttime phenomenon. GI's cooling effects are greater in areas with higher vegetation cover, leading to a decrease in the OTC-UHI's intensity, frequency, and duration. The relationship between OTC-UHIs and urban morphology varies throughout the day, with vegetation cover ratio and sky view factors impacting cooling significantly. Additionally, cooling impact distance of GI follows a power curve relationship, with parks having a longer cooling impact distance compared to rivers, particularly at night. Our findings highlight that the effective usage of GI can significantly improve the street-level OTC of pedestrians by tackling the climate-induced UHIs.