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Remote Sensing Applied in Urban Environment Monitoring

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

Deadline for manuscript submissions: 30 November 2026 | Viewed by 3011

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Editors

Dr. Peixiao Wang

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Guest Editor

State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Nature Resources Research, Chinese Academy of Sciences, Beijing 100101, China
Interests: geospatial big data analytics; geospatial artificial intelligence

Dr. Yan Zhang

E-Mail Website
Guest Editor

Institute of Space and Earth Information Science (ISEIS), The Chinese University of Hong Kong, Hong Kong
Interests: urban sensing and computing; environmental exposure; GeoAI
Special Issues, Collections and Topics in MDPI journals

Dr. Tao Hu

E-Mail Website
Guest Editor

Department of Geography, Oklahoma State University, Stillwater, OK 74074, USA
Interests: GIS; geospatial big data; health geography; health disparities
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The world is witnessing an unprecedented rate of urbanization, which brings forth complex challenges in environmental management, public health, and sustainable development. Remote sensing technology, with its capability for synoptic, repetitive, and objective observations, has become an indispensable tool for monitoring and understanding the intricate dynamics of urban environments. The advent of massive amounts of multi-source, multi-temporal data, coupled with breakthroughs in Geospatial Artificial Intelligence (GeoAI), is revolutionizing this field. These advancements enable us to move beyond traditional mapping towards modeling complex urban processes, predicting future scenarios, and assessing human exposure to environmental risks with unprecedented accuracy and detail.

This Special Issue, “Remote Sensing Applied in Urban Environment Monitoring,” aims to compile the latest innovative research and practical applications that address the multifaceted challenges of contemporary cities. It seeks to highlight how modern remote sensing data—from very-high-resolution optical and SAR satellites to UAVs and LiDAR—combined with advanced analytical methods, such as machine learning, deep learning, and data fusion, are transforming our understanding of urban systems. This topic is central to the scope of Remote Sensing, which is dedicated to the science and application of remote sensing technology, fostering the dissemination of cutting-edge research featuring data collection, analysis, and interpretation.

We encourage submissions on topics including, but not limited to, the following:

GeoAI for urban land use/cover mapping and change detection.
Monitoring urban redevelopment and renewal processes.
Assessing environmental exposure (e.g., heat, air pollution, green space).
Urban infrastructure and settlement analysis.
Social sensing and remote sensing data fusion for urban studies.
Sustainable Development Goal (SDG) 11 monitoring.

Dr. Peixiao Wang
Dr. Yan Zhang
Dr. Tao Hu
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 remote sensing
GeoAI
environmental exposure
urban redevelopment
sustainable cities
geospatial big data
machine learning
SDG 11

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Published Papers (4 papers)

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Research

21 pages, 107753 KB

Open AccessArticle

Individual Urban Tree Detection from Multispectral Satellite Imagery via Point-Supervised Deep Learning

by Thomas Martinoli, Luca Morandini and Piero Fraternali

Remote Sens. 2026, 18(12), 2021; https://doi.org/10.3390/rs18122021 - 17 Jun 2026

Viewed by 291

Abstract

Monitoring urban biodiversity is essential for designing resilient and sustainable cities. Urban trees provide a wide range of ecosystem services (ESs), including air pollution reduction, urban heat island mitigation, and psychological benefits for citizens. Accurate and updated tree inventories are therefore essential tools for urban environmental monitoring. However, existing urban tree inventories are often incomplete or outdated, especially in private areas, limiting accurate ES assessment and urban planning. Earth observation satellite missions, particularly very-high-resolution multispectral (VHR-MS) imagery, offer a valuable alternative to field surveys for gathering information on urban environments. This work proposes a deep learning (DL) framework based on VHR-MS satellite imagery for the automatic generation of accurate urban tree inventories. DL models reduce human effort and save operational time by automatically learning complex representations and patterns from satellite imagery. The proposed encoder–decoder architecture extends prior point-based detection approaches by integrating a ResNet-50 backbone and a percentile-based threshold calibration procedure. Given the lack of suitable training data covering heterogeneous and densely vegetated urban environments, a dedicated dataset was constructed from VHR-MS satellite imagery acquired over the Lombardy region (Italy). The dataset encompasses a wide range of land uses and land covers, including residential and industrial zones, public parks, private gardens, and agricultural areas. Through the photointerpretation of more than 2800 images, precise coordinates for more than 50,000 manually annotated trees were obtained. The DL model is trained with point-level annotations, enabling precise localization of individual trees while reducing annotation ambiguity in dense urban contexts. On the Lombardy dataset at 30 cm/px resolution, the proposed framework achieves 86.72% Precision, 66.92% Recall, an F1-score of 75.54%, and a localization error of 1.473 m. Full article

(This article belongs to the Special Issue Remote Sensing Applied in Urban Environment Monitoring)

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25 pages, 16527 KB

Open AccessArticle

UGDMoE: An Uncertainty-Guided Mixture-of-Experts Decoder for Open-Vocabulary Remote Sensing Segmentation

by Wenqiu Qu, Guifei Jing, Qiang Yuan, Zhushenyu Guo and Jianfeng Zhang

Remote Sens. 2026, 18(9), 1349; https://doi.org/10.3390/rs18091349 - 28 Apr 2026

Viewed by 599

Abstract

Rapid urbanization and the rapid accumulation of multi-source and multi-temporal Earth observation data are creating an increasing demand for remote sensing models that can flexibly support fine-grained monitoring beyond fixed label taxonomies. Open-vocabulary remote sensing image semantic segmentation (OVRSIS) aims to segment text-specified categories beyond a fixed label space with vision–language foundation models. However, dense remote sensing scenes make pixel–text matching highly vulnerable to semantic confusion and misalignment, owing to extreme scale variation, thin structures, repetitive textures, and prompt sensitivity. To address these challenges, we propose UGDMoE, an uncertainty-guided mixture-of-experts framework for OVRSIS. First, we design a domain-specific MoE decoder with three geometrically specialized experts—for slender structures, mid-scale objects, and large-region context—routed by the alignment-risk cue

U_{0}

. Second, we introduce a lightweight prompt–response estimation strategy that quantifies prediction dispersion across semantically equivalent prompts to derive

U_{0}

in an annotation-free manner. Third, we develop prompt ensemble-based likelihood calibration (PELC), which takes the shared alignment-risk cue

U_{0}

as input to calibrate prompt-specific logits before refinement. Finally, we design a lightweight uncertainty-aware structure refinement module that, guided by

U_{0}

, selectively fuses early visual features with segmentation logits to restore boundary continuity and connectivity of thin structures. We conduct extensive experiments on eight OVRSIS benchmarks under cross-dataset evaluation protocols. Trained on DLRSD, it achieves 46.97 m-mIoU and 63.31 m-mACC, surpassing the strongest baseline by 0.76 and 0.62 points; trained on iSAID, it reaches 37.47 m-mIoU and 58.52 m-mACC, improving over the strongest competitor by 0.71 and 0.61 points. UGDMoE consistently achieves state-of-the-art performance and remains robust under training-source changes. Full article

(This article belongs to the Special Issue Remote Sensing Applied in Urban Environment Monitoring)

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26 pages, 8133 KB

Open AccessArticle

Morphological and Entropy Analysis of Urban Change in Six European Metropolitan Areas Based on Copernicus Land Monitoring Service Products

by Ines Marinosci, Angela Cimini, Luca Congedo, Benedetta Cucca, Paolo De Fioravante, Pasquale Dichicco, Annalisa Minelli, Michele Munafò, Nicola Riitano, Michał Krupiński, Stanisław Lewiński, Szymon Sala, Kamil Drejer, Krzysztof Gryguc, Marek Ruciński, Agris Brauns, Dainis Jakovels, Zlatomir Dimitrov, Lachezar Filchev, Mariana Zaharinova, Daniela Avetisyan, Kamelia Radeva, Georgi Jelev, Lyubomir Filipov, Juan Manuel López Torralbo, Ana Silió Calzada, Jose M. Álvarez-Martínez, David López Trullén, Hugo Costa, Pedro Benevides and Mário Caetano Show full author list Hide full author list

Remote Sens. 2026, 18(8), 1149; https://doi.org/10.3390/rs18081149 - 12 Apr 2026

Viewed by 750

Abstract

Urban areas across Europe are undergoing rapid morphological transformations driven by densification, redevelopment, and infrastructure expansion. Monitoring these urban changes requires operational, harmonized, and reproducible approaches grounded in Earth Observation. This study presents a Copernicus use case demonstrating how the High-Resolution Layer Imperviousness Change (2015–2018) and Urban Atlas datasets can be integrated with the Guidos Toolbox (GTB) to quantify structural urban change across six metropolitan areas (Milan, Sofia, Riga, Warsaw, Viseu, Santander). Morphological Spatial Pattern Analysis (MSPA) and entropy-based indicators were applied to characterize land take, fragmentation, compaction, and internal reorganization of impervious surfaces. The combined framework captured both configurational morphology and spatial disorder, revealing divergent development patterns: pronounced heterogeneity and fragmentation in Sofia, stabilization or compact growth in Milan, Warsaw, and Santander, controlled densification in Riga, and localized intensification without outward expansion in Viseu. All analyses rely on openly accessible Copernicus data and open-source tools, ensuring full reproducibility and transferability. Outputs were disseminated through a FAIR-compliant geoportal developed within a Copernicus FPCUP project, supporting transparency and reuse. The findings underscore the value of Copernicus services for operational urban monitoring and provide a scalable methodology to support European land-use policies, including the Zero Net Land Take 2050 target and the EU Soil Strategy. Full article

(This article belongs to the Special Issue Remote Sensing Applied in Urban Environment Monitoring)

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20 pages, 8653 KB

Open AccessArticle

Spatiotemporal Prediction of Wind Fields in Coastal Urban Environments Using Multi-Source Satellite Data: A GeoAI Approach

by Yifan Shi, Tianqiang Huang, Liqing Huang, Wei Huang, Shaoyu Liu and Riqing Chen

Remote Sens. 2026, 18(5), 716; https://doi.org/10.3390/rs18050716 - 27 Feb 2026

Viewed by 585

Abstract

Rapid urbanization in coastal regions presents complex challenges for environmental management and public safety. Accurate, high-resolution wind field monitoring is critical for urban disaster mitigation, infrastructure resilience, and pollutant dispersion analysis in these densely populated areas. However, utilizing massive multi-source satellite remote sensing data for precise prediction remains difficult due to the spatiotemporal heterogeneity caused by the land–sea interface. To address this, this study proposes a novel lightweight Geospatial Artificial Intelligence (GeoAI) framework (DA-DSC-UNet) designed to predict wind fields in coastal urban environments (e.g., Fujian, China). We constructed a dataset by integrating multi-source satellite scatterometer products (including Advanced Scatterometer (ASCAT), Fengyun-3E (FY-3E), and Quick Scatterometer (QuickSCAT)) and buoy observations. The framework employs a UNet architecture enhanced with dual attention mechanisms (Efficient Channel Attention (ECA) and Convolutional Block Attention Module (CBAM)) to adaptively extract features from remote sensing signals, focusing on critical spatial regions like urban coastlines. Additionally, depthwise separable convolutions (DSCs) are introduced to ensure the model is lightweight and efficient for potential deployment in urban monitoring systems. Results demonstrate that our approach significantly outperforms existing deep learning models (reducing Mean Absolute Error (MAE) by 14–25.8%) and exhibits exceptional robustness against observational noise. This work demonstrates the potential of deep learning in enhancing the value of remote sensing data for urban resilience, sustainable development (SDG 11), and environmental monitoring in complex coastal zones. Full article

(This article belongs to the Special Issue Remote Sensing Applied in Urban Environment Monitoring)

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