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      Eyes in the Sky: Drones Applications in the Built Environment under Climate Change Challenges

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          Abstract

          This paper reviews the diverse applications of drone technologies in the built environment and their role in climate change research. Drones, or unmanned aerial vehicles (UAVs), have emerged as valuable tools for environmental scientists, offering new possibilities for data collection, monitoring, and analysis in the urban environment. The paper begins by providing an overview of the different types of drones used in the built environment, including quadcopters, fixed-wing drones, and hybrid models. It explores their capabilities and features, such as high-resolution cameras, LiDAR sensors, and thermal imaging, which enable detailed data acquisition for studying climate change impacts in urban areas. The paper then examines the specific applications of drones in the built environment and their contribution to climate change research. These applications include mapping urban heat islands, assessing the energy efficiency of buildings, monitoring air quality, and identifying sources of greenhouse gas emissions. UAVs enable researchers to collect spatially and temporally rich data, allowing for a detailed analysis and identifying trends and patterns. Furthermore, the paper discusses integrating UAVs with artificial intelligence (AI) to derive insights and develop predictive models for climate change mitigation and adaptation in urban environments. Finally, the paper addresses drone technologies’ challenges and the future directions in the built environment. These challenges encompass regulatory frameworks, privacy concerns, data management, and the need for an interdisciplinary collaboration. By harnessing the potential of drones, environmental scientists can enhance their understanding of climate change impacts in urban areas and contribute to developing sustainable strategies for resilient cities.

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          Deep learning and process understanding for data-driven Earth system science

          Machine learning approaches are increasingly used to extract patterns and insights from the ever-increasing stream of geospatial data, but current approaches may not be optimal when system behaviour is dominated by spatial or temporal context. Here, rather than amending classical machine learning, we argue that these contextual cues should be used as part of deep learning (an approach that is able to extract spatio-temporal features automatically) to gain further process understanding of Earth system science problems, improving the predictive ability of seasonal forecasting and modelling of long-range spatial connections across multiple timescales, for example. The next step will be a hybrid modelling approach, coupling physical process models with the versatility of data-driven machine learning.
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            Unmanned aerial systems for photogrammetry and remote sensing: A review

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              Fluorescence, temperature and narrow-band indices acquired from a UAV platform for water stress detection using a micro-hyperspectral imager and a thermal camera

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                Author and article information

                Contributors
                (View ORCID Profile)
                Journal
                DRONAP
                Drones
                Drones
                2504-446X
                October 2023
                October 16 2023
                : 7
                : 10
                : 637
                Article
                10.3390/drones7100637
                ee03f725-6977-4498-9f85-b7b822dcf412
                © 2023

                https://creativecommons.org/licenses/by/4.0/

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