HeatWatch: Identification of Parameters Influencing the Urban Heat Island Effect through Deep Learning Techniques

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dc.contributor.authorKılınç M.
dc.contributor.authorAydın C.
dc.contributor.authorAydın G.E.
dc.contributor.authorBalcı D.
dc.date.accessioned2024-07-22T08:01:25Z
dc.date.available2024-07-22T08:01:25Z
dc.date.issued2024
dc.description.abstractGlobal climate change (GCC) is accelerated by factors such as greenhouse gas emissions from human activities and the urban heat island (UHI) effect, particularly in densely urbanized areas. According to the WMO's 2023 data, GCC warming effects increased by 49% from 1990s to 2021, 80% of this increase was due to CO2. Furthermore, average global temperatures have increased by 1.1°C since the early 1900s. In this respect, the urban heat island (UHI) effect has gained importance with global temperatures. Environmental problems that cause cities to be warmer than rural areas, especially due to hard ground surfaces, building density and decreasing green areas, have the potential to create negative impacts on human health. Therefore, it is important to identify and manage the impact of UHI. This is because traditional methods are limited to fixed station data, but technologies such as remote sensing and geographic information systems (GIS) provide more comprehensive results for this management. In an innovative approach, deep learning and artificial intelligence techniques can provide more accurate analysis by processing large datasets. In this context, this research proposes a conceptual framework for using deep learning techniques to detect the UHI effect with data obtained from street images and unmanned aerial vehicles. With the proposal, the UHI value will be calculated by detecting objects such as trees, air conditioners, vehicles and building cladding with the YOLO-Real-Time Object Detection algorithm. With this approach, it is aimed to obtaining more precise and accurate results in determining the UHI effect. In addition, a web-based management panel will be designed for managers to review the results and use them in decision-making mechanisms. It is aimed at disseminating this model and making it an important tool in the planning of urban areas. © Author(s) 2024. CC BY 4.0 License.
dc.identifier.DOI-ID10.5194/isprs-archives-XLVIII-4-W10-2024-107-2024
dc.identifier.issn16821750
dc.identifier.urihttp://akademikarsiv.cbu.edu.tr:4000/handle/123456789/11450
dc.language.isoEnglish
dc.publisherInternational Society for Photogrammetry and Remote Sensing
dc.rightsAll Open Access; Gold Open Access
dc.subjectAntennas
dc.subjectAtmospheric temperature
dc.subjectClimate change
dc.subjectDecision making
dc.subjectDeep learning
dc.subjectGas emissions
dc.subjectGreenhouse gases
dc.subjectInformation management
dc.subjectInformation systems
dc.subjectInformation use
dc.subjectLarge datasets
dc.subjectLearning algorithms
dc.subjectLearning systems
dc.subjectObject detection
dc.subjectRemote sensing
dc.subjectDeep learning
dc.subjectEnvironmental problems
dc.subjectGlobal climate changes
dc.subjectGlobal temperatures
dc.subjectGreenhouse gas emissions
dc.subjectHuman activities
dc.subjectLearning techniques
dc.subjectUrban heat island
dc.subjectUrban Heat Island Effects
dc.subjectUrbanized area
dc.subjectGeographic information systems
dc.titleHeatWatch: Identification of Parameters Influencing the Urban Heat Island Effect through Deep Learning Techniques
dc.typeConference paper

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