AI-Enhanced 4D Geospatial Monitoring for Healthy and Resilient Cities
A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Environmental Sciences".
Deadline for manuscript submissions: 20 September 2025 | Viewed by 4498
Special Issue Editor
Interests: remote sensing; machine learning; ecology; plant communities
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Four-dimensional geospatial data, including the dimension of time, provide critical information for the deeper understanding of our ever-changing urban landscapes. This technology, coupled with the power of AI, is revolutionizing the way we approach urban sustainability. Four-dimensional data and AI allow us to monitor urban heat islands and flood risks with higher accuracy. This empowers us to develop targeted strategies like green infrastructure and water management, safeguarding public health and fostering climate adaptation. Leveraging 4D geospatial data, AI algorithms can be used to unravel the complex relationships between urban morphology, building density, and micro-climatic cluster formation. This empowers us to develop sustainable urban strategies by considering local micro-climates, urban heat islands, and flood risks, leading to the development of healthier and resilient cities. Four-dimensional monitoring exposes the facets of urban expansion, environmental impacts, and the evolution of infrastructure. The application of AI in analyzing these data yields profound insights into the dynamics of environmental change and ecosystem sustainability. These insights are instrumental in directing practical actions towards fostering healthy and resilient urban environments. The topics include, but are not limited to:
Generating 4D geospatial data: utilizing lidar and photogrammetry to capture precise 3D snapshots of urban landscapes, including building structures and vegetation canopies, to reveal interactions between urban design, health impacts, and climate resilience across various timescales.
Urban biodiversity and ecosystem services: utilizing advanced algorithms and 4D data, this approach maps urban green spaces, tracks biodiversity hotspots, and evaluates ecosystem services, focusing on temporal changes in urban biodiversity and integrating spectral analysis and biodiversity indices to assess urbanization impacts.
Green infrastructure for flood resilience: investigating the effectiveness of urban green spaces in flood mitigation using simulations and temporal analysis for resilient urban ecosystems.
Micro-climatic analysis of urban areas: this involves grasping the small-scale climatic variations within urban environments, focusing on examining localized differences in weather, temperature, and air quality and considering both natural and man-made features.
Climate change adaptation and public health: this involves modeling the effects of extreme weather events on urban infrastructure, assisting the assessments of how green spaces impact air quality and mitigate urban heat islands, which directly impacts public health and reduces heat-stroke risks.
We look forward to receiving your contributions in the form of reviews, articles, or letters.
Dr. Ram C. Sharma
Guest Editor
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Keywords
- urban sustainability
- remote sensing and geospatial technology
- urban geomorphology
- vegetation and public health
- urban ecosystem analysis
- AI, machine learning, and deep learning
- urban design and micro-climatic conditions
- green infrastructures and water management systems
- lidar and photogrammetry
- extreme weather events and heat stroke
- air quality and public health
- spectral analysis and biodiversity indices
- social issues and urban resilience strategies
- optical, radar, and lidar sensing
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