Urban Sprawl and Urban Sustainability: High-Resolution Remote-Sensing Image Analytics

Dear Colleagues,

Rapid urbanization is reshaping cities worldwide, often leading to extensive urban sprawl, fragmented landscapes, environmental degradation and unequal access to infrastructure and services. Understanding these spatial patterns is essential for promoting sustainable urban development and achieving SDG 11. High-resolution remote-sensing images have emerged as a powerful data source for observing, quantifying and modeling urban growth and sustainability indicators at fine spatial and temporal scales. Advanced image analytics, including machine learning, deep learning, and multi-modal deep learning, now enable detailed assessments of built-up expansion, land-use transitions, ecological conditions and urban form. This Special Issue aims to showcase cutting-edge research that leverages high-resolution remote sensing and image analytics to monitor urban sprawl, identify SDG-related elements and indicators, assess urban sustainability and support smart-city planning. We welcome contributions that demonstrate methodological innovations, novel geospatial data fusions or applications for understanding urban dynamics and guiding sustainable-city planning.

Topics of interest include, but are not limited to:

(1) High-resolution remote sensing for fine-scaled urban sprawl detection and monitoring;

(2) Urban sustainability assessment, SDG-related elements and SDG indicators derived from remote sensing;

(3) Urban land-use/land-cover/functional-zones mapping, change analysis and spatiotemporal urban dynamics monitoring;

(4) GeoAI, machine learning, multimodal deep learning and advanced image analytics for urban studies;

(5) Multi-sensor or multi-modal geospatial data fusion (e.g., optical, SAR, LiDAR, nighttime light data, geospatial data);

(6) Environmental impacts of urban sprawl/ urban change;

We invite original research articles, innovative methodologies, reviews and case studies that advance the understanding of urban growth and urban sustainability through high-resolution remote sensing and multi-modal geospatial data.

Dr. Runyu Fan
Prof. Dr. Xuecao Li
Dr. Yan Zhang
Guest Editors

Manuscript Submission Information

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