Remote Sensing-Driven Digital Twins for Climate-Adaptive Cities
A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Urban Remote Sensing".
Deadline for manuscript submissions: 31 January 2026 | Viewed by 15
Special Issue Editor
2. McGregor Coxall Australia Pty. Ltd., Sydney, NSW, Australia
Interests: machine learning; geospatial 3D analysis; geospatial database querying; web GIS; airborne/spaceborne image processing; feature extraction; time-series analysis in forecasting modelling and domain adaptation in various environmental applications
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Cities today face mounting challenges from rapid urbanization and a changing climate, requiring planning tools that combine accuracy, timeliness, and actionable intelligence. This Special Issue calls for research that leverages remote sensing, from high-resolution optical and LiDAR airborne surveys to multi-temporal satellite constellations, and integrates these data streams into AI-enhanced digital twins. We welcome contributions demonstrating how neural networks, transformer architectures, and graph-based models can extract urban form and function (e.g., building footprints, impervious surfaces, and green corridors) and derive environmental indicators (surface temperature, vegetation health, and soil moisture) at scales ranging from blocks to metropolitan regions.
Beyond static mapping, this collection emphasizes dynamic, near-real-time monitoring, fusing satellite imagery and UAV LiDAR into deep-learning pipelines that track urban heat islands, air-quality proxies, and land-cover changes. The core focus is on embedding these remotely sensed layers within pedestrian-route-planning workflows, optimizing walkability, accessibility, and safety under evolving climate and infrastructure scenarios.
These case studies should illustrate how high-resolution remote sensing data, from satellites, drones, and ground sensors, fuel AI-driven digital twins, transforming them into real-time decision‑support platforms for planners, infrastructure managers, and emergency responders, and ultimately shaping smarter, more walkable, climate‑resilient cities.
Dr. Hossein M. Rizeei
Guest Editor
Manuscript Submission Information
Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.
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Keywords
- digital twin frameworks
- deep learning and transformer models
- multi-sensor data fusion (optical, SAR, and LiDAR)
- urban heat island and environmental indicator mapping
- feature extraction (buildings, impervious surfaces, and green spaces)
- spatiotemporal change detection
- AI-powered decision-support systems
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