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Applications of Remote Sensing Imagery for Urban Areas (Second Edition)

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Remote Sensing Image Processing".

Deadline for manuscript submissions: 31 August 2025 | Viewed by 724

Special Issue Editors


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Guest Editor
School of Geography and Tourism, Shaanxi Normal University, Xi’an 710119, China
Interests: remote sensing; image processing; geometric correction; image stitching
Special Issues, Collections and Topics in MDPI journals
Faculty of Computer and Software Engineering, Huaiyin Institute of Technology, No. 1 Meicheng Road East, Huaian 223003, China
Interests: point cloud data processing; remote sensing image processing; object detection; object segmentation; deep learning
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Urban areas are the center of human settlement with intensive anthropic activities and dense built-up infrastructures. Urban areas have undergone, and are still undergoing, great evolution through population shift, land-use change, high-rise buildings, industrial production, etc. Urbanization-induced environmental pollution, climate change, and ecosystem degradation are the research hotpots that not only highly relate to human lives but are also the main causes of global change. Moreover, urban planning, public health management, and human security policy are crucial research subjects across the globe with regard to human sustainable development.

Remote sensing imagery provides essential information for these applications in urban areas. In particular, continually improved spatial resolution can satisfy the description of the complex urban geographical system. Data from different platforms (drone, airborne, and spaceborne) and different sensors (optical, thermal, SAR, and LiDAR) have various characteristics and spatiotemporal resolutions, and therefore they are applicable for resolving numerous natural and anthropogenic issues in urban areas at different scales. Furthermore, the development of big data mining, machine learning, and cloud computing technologies has also advanced the applications of remote sensing data and presented new opportunities and challenges.

The main objective of this Special Issue is to amass a collection of cutting-edge research works that explore the multifaceted applications of remote sensing imagery in urban settings. In today's rapidly urbanizing world, cities face varied complex challenges and opportunities. Remote sensing, with its unique capabilities for providing synoptic, repetitive, and detailed views of the Earth's surface, has emerged as a powerful tool. We will showcase studies that utilize various remote sensing techniques, such as optical, radar, or LiDAR data, to address urban issues such as urban sprawl mapping and monitoring. By precisely delineating the expanding boundaries of cities over time, policymakers can make informed decisions regarding land-use planning and infrastructure development, ensuring sustainable growth.

Regarding the journal's scope, Remote Sensing is dedicated to the dissemination of high-quality research across the entire spectrum of remote sensing science and technology. Our Special Issue on urban applications fits within this framework as it epitomizes the practical utilization of remote sensing principles. The journal emphasizes innovation in data acquisition, processing, and interpretation, which is exactly what urban remote sensing demands. Whether developing novel image classification methods tailored for complex urban landscapes filled with diverse land cover types or advancing change detection algorithms to track urban transformations, the research presented here not only adheres to but also enriches the journal's core mission of advancing the field of remote sensing. It bridges the gap between theoretical advancements in this discipline and real-world urban challenges, making it an essential addition that broadens the journal's scope while deepening its impact in an area of growing global importance.

This Special issue will address recent thematic outcomes and advances related to urban applications based on remote sensing imagery. Topics of interest include, but are not limited to, the following:

  • Phenomena and evolution of urban ecosystem and environments: urban climate, atmosphere, soil, water bodies, vegetation, and thermal environment;
  • Main applications on urban monitoring: urban sprawl, urban planning, classification of urban functional areas, spatial configuration, anthropic activities, public health, and emergency management;
  • Urban visualization and 3D/4D urban modeling from remote sending datasets;
  • Urban classification and object-analysis, including the identification of damaged infrastructures, land subsidence, pollution, and garbage;
  • Applications of new-generation sensors and high-resolution remote sensing data in urban areas;
  • Urban remote sensing data-processing: image registration, mosaic, data fusion, quality improvements, machine learning, cloud computing, and data mining.

This is the second edition of this Special Issue, titled “Applications of Remote Sensing Imagery for Urban Areas” (https://www.mdpi.com/journal/remotesensing/special_issues/urban_imagery); experts and scholars in related fields are welcome to submit their original works to this Special Issue.

Dr. Ruitao Feng
Dr. Yongtao Yu
Dr. Xinghua Li
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 100 words) can be sent to the Editorial Office for announcement on this website.

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-blind 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

  • remote sensing
  • urban climate
  • urban pollution
  • urban thermal environment
  • urban ecosystem
  • land-use change
  • urban object analysis
  • urban planning
  • urban spatial configuration

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Related Special Issue

Published Papers (2 papers)

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19 pages, 4512 KiB  
Article
AD-Det: Boosting Object Detection in UAV Images with Focused Small Objects and Balanced Tail Classes
by Zhenteng Li, Sheng Lian, Dengfeng Pan, Youlin Wang and Wei Liu
Remote Sens. 2025, 17(9), 1556; https://doi.org/10.3390/rs17091556 - 27 Apr 2025
Viewed by 129
Abstract
Object detection in unmanned aerial vehicle (UAV) images poses significant challenges due to complex scale variations and class imbalance among objects. Existing methods often address these challenges separately, overlooking the intricate nature of UAV images and the potential synergy between them. In response, [...] Read more.
Object detection in unmanned aerial vehicle (UAV) images poses significant challenges due to complex scale variations and class imbalance among objects. Existing methods often address these challenges separately, overlooking the intricate nature of UAV images and the potential synergy between them. In response, this paper proposes AD-Det, a novel framework employing a coherent coarse-to-fine strategy that seamlessly integrates two pivotal components: adaptive small object enhancement (ASOE) and dynamic class-balanced copy–paste (DCC). ASOE utilizes a high-resolution feature map to identify and cluster regions containing small objects. These regions are subsequently enlarged and processed by a fine-grained detector. On the other hand, DCC conducts object-level resampling by dynamically pasting tail classes around the cluster centers obtained by ASOE, maintaining a dynamic memory bank for each tail class. This approach enables AD-Det to not only extract regions with small objects for precise detection but also dynamically perform reasonable resampling for tail-class objects. Consequently, AD-Det enhances the overall detection performance by addressing the challenges of scale variations and class imbalance in UAV images through a synergistic and adaptive framework. We extensively evaluate our approach on two public datasets, i.e., VisDrone and UAVDT, and demonstrate that AD-Det significantly outperforms existing competitive alternatives. Notably, AD-Det achieves a 37.5% average precision (AP) on the VisDrone dataset, surpassing its counterparts by at least 3.1%. Full article
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23 pages, 5957 KiB  
Article
Urban Functional Zone Identification Based on Multimodal Data Fusion: A Case Study of Chongqing’s Central Urban Area
by Yongchuan Zhang, Yuhong Xu, Jie Gao, Zunya Zhao, Jing Sun and Fengyun Mu
Remote Sens. 2025, 17(6), 990; https://doi.org/10.3390/rs17060990 - 12 Mar 2025
Viewed by 449
Abstract
Urban Functional Zones (UFZs) are spatial units of the city divided according to specific functional activities. Detailed identification of UFZs is vital for optimizing urban management, guiding planning and design, and promoting sustainable development. However, existing UFZ recognition methods face significant challenges, such [...] Read more.
Urban Functional Zones (UFZs) are spatial units of the city divided according to specific functional activities. Detailed identification of UFZs is vital for optimizing urban management, guiding planning and design, and promoting sustainable development. However, existing UFZ recognition methods face significant challenges, such as difficulties in effectively integrating multi-source heterogeneous data, capturing dynamic spatiotemporal patterns, and addressing the complex interrelationships among various data types. These issues significantly limit the applicability of UFZ mapping in complex urban scenarios. To address these challenges, this paper proposes a tripartite neural network (TriNet) for multimodal data processing, including Remote Sensing (RS) images, Point of Interest (POI) data, and Origin–Destination (OD) data, fully utilizing the complementarity of different data types. TriNet comprises three specialized branches: ImgNet for spatial features extraction from images, POINet for functional density distribution features extraction from POI data, and TrajNet for spatiotemporal pattern features extraction from OD data. Finally, the method deeply fuses these features through a feature fusion module, which utilizes a two-layer fully connected network for deep fusion, allowing the model to fully utilize the interdependencies among the data types, significantly improving the UFZ classification accuracy. The experimental data are generated by mapping OpenStreetMap (OSM) vector into conceptual representations, integrating images with social sensing data to create a comprehensive UFZ classification benchmark. The method achieved an overall accuracy of 84.13% on the test set of Chongqing’s main urban area, demonstrating high accuracy and robustness in UFZ classification tasks. The experimental results show that the TriNet model performs effectively in UFZ classification. Full article
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