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Multi-Sensor Remote Sensing for Urban Land Use and Land Cover Mapping

A Special Issue of Remote Sensing (ISSN 2072-4292) belonging to the section "Urban Remote Sensing".

Deadline for manuscript submissions: 30 September 2026 | Viewed by 1044

Editors


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Guest Editor
School of Geography and Planning, Sun Yat-sen University, Guangzhou, China
Interests: urban remote sensing; land use and land cover change
Special Issues, Collections and Topics in MDPI journals
Center for Spatial Analysis, University of Oklahoma, Norman, OK 73019, USA
Interests: machine learning- and deep learning-based multi-source remote sensing algorithms and applications with a focus on the integration of Optical, SAR, and nightlight remote sensing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Urban land use and land cover mapping has long been a core topic in remote sensing research, driven by the increasing availability of high-resolution Earth observation data and the growing demand for detailed urban environmental information. Early urban mapping studies mainly relied on optical imagery, which provided valuable insights but often faced limitations in complex urban environments due to spectral confusion, shadow effects, and limited sensitivity to urban structure. In recent decades, the growing availability of complementary sensors such as synthetic aperture radar (SAR), LiDAR, and thermal sensors has substantially expanded the capability to observe urban surfaces from multiple perspectives.

The integration of data acquired from different sensors and platforms has enabled improved characterization of urban land cover types, three-dimensional structures, and surface thermal properties across spatial and temporal scales. At the same time, multi-sensor urban studies raise new challenges related to data consistency, spatial resolution differences, temporal alignment, and the transferability of analysis methods across cities and acquisition conditions. Addressing these challenges is essential for advancing reliable and operational urban land use and land cover mapping.

This Special Issue aims to present studies that explore the use of multi-sensor remote sensing data for urban land use and land cover mapping. Contributions may focus on methodological developments, comparative analyses, or application-oriented studies at different spatial and temporal scales. Multisource data integration, multiscale analysis, and studies linking land use and land cover patterns with urban environmental processes are particularly encouraged. Research addressing urban functional zones, thermal environments, or sustainability-related indicators is welcome when it is based on remote sensing observations of the urban land surface.

This Special Issue’s scope includes, but is not limited to, the following themes:

  • Urban land use and land cover mapping using multi-sensor remote sensing data
  • Integration of optical, SAR, LiDAR, and thermal data for urban analysis
  • Urban land cover change detection and spatio-temporal dynamics
  • Mapping of urban functional areas using remote sensing and geospatial data
  • Urban thermal environment characterization in relation to land cover patterns
  • Remote sensing approaches for monitoring urban sustainability indicators
  • Object-based and pixel-based image analysis in urban environments

Dr. Zhixin Qi
Dr. Di Liu
Guest Editors

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

  • urban land use and land cover (LULC)
  • multi-sensor data fusion
  • synthetic aperture radar (SAR)
  • LiDAR
  • thermal remote sensing
  • urban sustainability

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Published Papers (1 paper)

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Research

27 pages, 4126 KB  
Article
A Dual-Modal Framework Integrating SAR-Based Change Screening and Optical-Scene-Informed Identification for High-Frequency Monitoring of Construction-Ready Bare Land
by Wenxuan Song, Qianwen Lv, Zihao Ding, Shishu Hong and Zhixin Qi
Remote Sens. 2026, 18(8), 1103; https://doi.org/10.3390/rs18081103 - 8 Apr 2026
Viewed by 706
Abstract
Rapid urbanization necessitates high-frequency monitoring of construction-ready bare land to timely detect and prevent illegal construction. However, the utility of optical imagery is often compromised in cloud-prone regions. While Synthetic Aperture Radar (SAR) offers all-weather capabilities, it struggles to distinguish construction-ready bare land [...] Read more.
Rapid urbanization necessitates high-frequency monitoring of construction-ready bare land to timely detect and prevent illegal construction. However, the utility of optical imagery is often compromised in cloud-prone regions. While Synthetic Aperture Radar (SAR) offers all-weather capabilities, it struggles to distinguish construction-ready bare land from recently harvested agricultural land, leading to severe false alarms. To address the conflict between high-frequency monitoring and semantic identification, this study proposes the SAR-based Change Screening and Optical-Scene-Informed Identification (SCS-OI) framework. The first stage performs high-recall candidate screening based on SAR backscattering changes, while the second stage incorporates historical cloud-free optical imagery as semantic guidance, enabling refined identification without requiring synchronous optical data. Experiments in Guangzhou demonstrate that the framework achieves a False Alarm Rate of 13.31%, Recall of 90.63%, Precision of 74.81%, F1-score of 81.95%, and IoU of 69.43%. Compared with the SAR-only baseline (FR = 22.4%), the two-stage design reduces false alarms while maintaining high recall. Other deep learning baselines exhibit lower F1-scores (59–73%), highlighting the effectiveness of the overall framework. These results show that the proposed two-stage framework effectively integrates high-recall candidate screening and semantic-guided refinement, providing a robust solution for high-frequency monitoring of construction-ready bare land in cloud-prone regions of Guangzhou. Full article
(This article belongs to the Special Issue Multi-Sensor Remote Sensing for Urban Land Use and Land Cover Mapping)
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