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Remote Sensing
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Remote Sensing-Driven Digital Twins for Climate-Adaptive Cities
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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 2027 | Viewed by 2646

Editor

Dr. Hossein M. Rizeei

E-Mail Website
Guest Editor
BioUrbanism Lab, McGregor Coxall Australia Pty Ltd., Sydney, NSW 2095, Australia
Interests: GeoAI; machine learning; geospatial 3D analysis; geospatial intelligence; airborne/spaceborne image processing; feature extraction; predictive modelling
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 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-anonymized peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

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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Published Papers (1 paper)

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Research

28 pages, 7633 KB  
Article
Physics-Informed Transformer Networks for Interpretable GNSS-R Wind Speed Retrieval
by Zao Zhang, Jingru Xu, Guifei Jing, Dongkai Yang and Yue Zhang
Remote Sens. 2025, 17(23), 3805; https://doi.org/10.3390/rs17233805 - 24 Nov 2025
Cited by 5 | Viewed by 2042
Abstract
Global Navigation Satellite System Reflectometry (GNSS-R) provides all-weather, high-resolution ocean wind speed monitoring that offers additional benefits for forecasting tropical cyclones and severe weather events. However, existing GNSS-R wind retrieval models often lack interpretability and suffer accuracy degradation during high wind conditions. To [...] Read more.
Global Navigation Satellite System Reflectometry (GNSS-R) provides all-weather, high-resolution ocean wind speed monitoring that offers additional benefits for forecasting tropical cyclones and severe weather events. However, existing GNSS-R wind retrieval models often lack interpretability and suffer accuracy degradation during high wind conditions. To address these limitations, we leverage a mathematical equivalence between Transformers and graph neural networks (GNNs) on complete graphs, which provides a physically grounded interpretation of self-attention as spatiotemporal influence propagation in GNSS-R data. In our model, each GNSS-R footprint is treated as a graph node whose multi-head self-attention weights quantify localized interactions across space and time. This aligns physical influence propagation with the computational efficiency of GPU-accelerated Transformers. Multi-head attention disentangles processes at multiple scales—capturing local (25–100 km), mesoscale (100 km–500 km), and synoptic (>500 km) circulation patterns. When applied to Level 1 Version 3.2 data (2023–2024) from four Asian sea regions, our Transformer–GNN achieves an overall wind speed RMSE reduction of 32% (to 1.35 m s−1 from 1.98 m s−1) and substantial gains in high-wind regimes (winds >25 m s−1: 3.2 m s−1 RMSE). The model is trained on ERA5 reanalysis 10 m equivalent-neutral wind fields, which serve as the primary reference dataset, with independent validation performed against Stepped Frequency Microwave Radiometer (SFMR) aircraft observations during tropical cyclone events and moored buoy measurements where spatiotemporally coincident data are available. Interpretability analysis with SHAP reveals condition-dependent feature attributions and suggests coupling mechanisms between ocean surface currents and wind fields. These results demonstrate that our model advances both predictive accuracy and interpretability in GNSS-R wind retrieval. With operationally viable inference performance, our framework offers a promising approach toward interpretable, physics-aware Earth system AI applications. Full article
(This article belongs to the Special Issue Remote Sensing-Driven Digital Twins for Climate-Adaptive Cities)
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