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Remote Sensing
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Urban Sensing Methods and Technologies II
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Urban Sensing Methods and Technologies II

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 2025 | Viewed by 9368

Special Issue Editors

Dr. Paolo Santi

E-Mail Website
Guest Editor
Senseable City Laboratory, Massachusetts Institute of Technology, Cambridge, MA, USA
Interests: complex systems; wireless network; sensor and vehicular networks; smart mobility; intelligent transportation systems
Dr. Amin Anjomshoaa

E-Mail Website
Guest Editor
Institute for Data, Process, and Knowledge Management, Vienna University of Economics and Business, and the Complexity Science Hub Vienna Research Center, Vienna, Austria
Interests: knowledge graphs; data analytics; smart city; IoT; machine learning
Special Issues, Collections and Topics in MDPI journals
Dr. Priyanka Nadia DeSouza

E-Mail Website
Guest Editor
Department of Urban and Regional Planning, University of Colorado Denver, Denver, CO, USA
Interests: air pollution; climate change; public health; environmental policy and planning

Special Issue Information

Dear Colleagues,

With over 55% of the world’s population residing in urban areas, cities now account for 70% of the world’s energy consumption and 80% of its CO2 emissions. As a result, cities are facing substantial environmental, societal, and economic issues. Many cities have developed policies and initiatives to combat these challenges. Cities need to develop new mechanisms of monitoring in order to evaluate the efficacy of existing efforts and undertake planning to meet urban sustainability and resilience goals.

In recent years, the emergence of the Internet of Things (IoT) and advancements in sensing technologies have led to a huge quantity of data that flow into the digital twins of our built environment and urban systems. Today, cities across the globe have deployed a myriad of sensors, enabling us to better understand urban environments and empowering advanced analytics solutions for decision makers and urban managers. Furthermore, there are various sensor deployment strategies, such as drive-by sensing for creating dense spatial and temporal datasets, remote sensing, stationary sensing, and hybrid sensing approaches.

This Special Issue aims to explore the state-of-the-art methods, technologies, and systems in urban sensing applications, alongside advancements in modeling and analysis of urban systems for various smart city use cases.

Dr. Paolo Santi
Dr. Amin Anjomshoaa
Dr. Priyanka Nadia DeSouza
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

  • environmental monitoring
  • smart city
  • sensor networks
  • intelligent systems

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Published Papers (4 papers)

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Research

27 pages, 23880 KiB  
Article
Urban Greening Analysis: A Multimodal Large Language Model for Pinpointing Vegetation Areas in Adverse Weather Conditions
by Hanzhang Liu, Shijie Yang, Chengwu Long, Jiateng Yuan, Qirui Yang, Jiahua Fan, Bingnan Meng, Zhibo Chen, Fu Xu and Chao Mou
Remote Sens. 2025, 17(12), 2058; https://doi.org/10.3390/rs17122058 - 14 Jun 2025
Viewed by 320
Abstract
Urban green spaces are an important part of the urban ecosystem and hold significant ecological value. To effectively protect these green spaces, urban managers urgently need to identify them and monitor their changes. Common urban vegetation positioning methods use deep learning segmentation models [...] Read more.
Urban green spaces are an important part of the urban ecosystem and hold significant ecological value. To effectively protect these green spaces, urban managers urgently need to identify them and monitor their changes. Common urban vegetation positioning methods use deep learning segmentation models to process street view data in urban areas, but this is usually inefficient and inaccurate. The main reason is that they are not applicable to the variable climate of urban scenarios, especially performing poorly in adverse weather conditions such as heavy fog that are common in cities. Additionally, these algorithms also have performance limitations such as inaccurate boundary area positioning. To address these challenges, we propose the UGSAM method that utilizes the high-performance multimodal large language model, the Segment Anything Model (i.e., SAM). In the UGSAM, a dual-branch defogging network WRPM is incorporated, which consists of the dense fog network FFA-Net, the light fog network LS-UNet, and the feature fusion network FIM, achieving precise identification of vegetation areas in adverse urban weather conditions. Moreover, we have designed a micro-correction network SCP-Net suitable for specific urban scenarios to further improve the accuracy of urban vegetation positioning. The UGSAM was compared with three classic deep learning algorithms and the SAM. Experimental results show that under adverse weather conditions, the UGSAM performs best in OA (0.8615), mIoU (0.8490), recall (0.9345), and precision (0.9027), surpassing the baseline model FCN (OA improvement 28.19%) and PointNet++ (OA improvement 30.02%). Compared with the SAM, the UGSAM improves the segmentation accuracy by 16.29% under adverse weather conditions and by 1.03% under good weather conditions. This method is expected to play a key role in the analysis of urban green spaces under adverse weather conditions and provide innovative insights for urban development. Full article
(This article belongs to the Special Issue Urban Sensing Methods and Technologies II)
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38 pages, 47930 KiB  
Article
Estimation of Urban Tree Chlorophyll Content and Leaf Area Index Using Sentinel-2 Images and 3D Radiative Transfer Model Inversion
by Théo Le Saint, Jean Nabucet, Laurence Hubert-Moy and Karine Adeline
Remote Sens. 2024, 16(20), 3867; https://doi.org/10.3390/rs16203867 - 18 Oct 2024
Cited by 1 | Viewed by 2120
Abstract
Urban trees play an important role in mitigating effects of climate change and provide essential ecosystem services. However, the urban environment can stress trees, requiring the use of effective monitoring methods to assess their health and functionality. The objective of this study, which [...] Read more.
Urban trees play an important role in mitigating effects of climate change and provide essential ecosystem services. However, the urban environment can stress trees, requiring the use of effective monitoring methods to assess their health and functionality. The objective of this study, which focused on four deciduous tree species in Rennes, France, was to evaluate the ability of hybrid inversion models to estimate leaf chlorophyll content (LCC), leaf area index (LAI), and canopy chlorophyll content (CCC) of urban trees using eight Sentinel-2 (S2) images acquired in 2021. Simulations were performed using the 3D radiative transfer model DART, and the hybrid inversion models were developed using machine-learning regression algorithms (random forest (RF) and gaussian process regression). Model performance was assessed using in situ measurements, and relations between satellite data and in situ measurements were investigated using spatial allocation (SA) methods at the pixel and tree scales. The influence of including environment features (EFs) as model inputs was also assessed. The results indicated that random forest models that included EFs and used the pixel-scale SA method were the most accurate with R2 values of 0.33, 0.29, and 0.46 for LCC, LAI, and CCC, respectively, with notable variability among species. Full article
(This article belongs to the Special Issue Urban Sensing Methods and Technologies II)
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23 pages, 8942 KiB  
Article
Predicting Neighborhood-Level Residential Carbon Emissions from Street View Images Using Computer Vision and Machine Learning
by Wanqi Shi, Yeyu Xiang, Yuxuan Ying, Yuqin Jiao, Rui Zhao and Waishan Qiu
Remote Sens. 2024, 16(8), 1312; https://doi.org/10.3390/rs16081312 - 9 Apr 2024
Cited by 5 | Viewed by 3750
Abstract
Predicting urban-scale carbon emissions (CEs) is crucial in drawing implications for various urgent environmental issues, including global warming. However, prior studies have overlooked the impact of the micro-level street environment, which might lead to biased prediction. To fill this gap, we developed an [...] Read more.
Predicting urban-scale carbon emissions (CEs) is crucial in drawing implications for various urgent environmental issues, including global warming. However, prior studies have overlooked the impact of the micro-level street environment, which might lead to biased prediction. To fill this gap, we developed an effective machine learning (ML) framework to predict neighborhood-level residential CEs based on a single data source, street view images (SVIs), which are publicly available worldwide. Specifically, more than 30 streetscape elements were classified from SVIs using semantic segmentation to describe the micro-level street environment, whose visual features can indicate major socioeconomic activities that significantly affect residential CEs. A ten-fold cross-validation was deployed to train ML models to predict the residential CEs at the 1 km grid level. We found, first, that random forest (R2 = 0.8) outperforms many traditional models, confirming that visual features are non-negligible in explaining CEs. Second, more building, wall, and fence views indicate higher CEs. Third, the presence of trees and grass is inversely related to CEs. Our findings justify the feasibility of using SVIs as a single data source to effectively predict neighborhood-level residential CEs. The framework is applicable to large regions across diverse urban forms, informing urban planners of sustainable urban form strategies to achieve carbon-neutral goals, especially for the development of new towns. Full article
(This article belongs to the Special Issue Urban Sensing Methods and Technologies II)
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25 pages, 14196 KiB  
Article
Sensing Human Activity of the Guangdong–Hong Kong–Macao Greater Bay Area by Ambient Seismic Noise
by Lihui Chen and Shaohong Xia
Remote Sens. 2023, 15(22), 5340; https://doi.org/10.3390/rs15225340 - 13 Nov 2023
Cited by 2 | Viewed by 2009
Abstract
Effective monitoring of human activity in urban areas is essential for social stability and urban development. Traditional monitoring methods include wearable devices, survey sensor networks, and satellite remote sensing, which may be affected by privacy and weather conditions. Ambient seismic noise recorded by [...] Read more.
Effective monitoring of human activity in urban areas is essential for social stability and urban development. Traditional monitoring methods include wearable devices, survey sensor networks, and satellite remote sensing, which may be affected by privacy and weather conditions. Ambient seismic noise recorded by seismometers contains rich information about human activity and exhibits significant temporal and spatial variations, which provides valuable insights into social mobility. In this study, we investigated the correlation between human activity and ambient seismic noise in the Guangdong–Hong Kong–Macao Greater Bay Area (GBA) using the data recorded by 138 seismometers. Our results indicate that ambient seismic noise produced by human activity in the GBA is mainly concentrated between 2 and 20 Hz. The spatial distribution of ambient seismic noise exhibits a strong correlation with population and economy. Our results show that the analysis of ambient seismic noise can reveal the spatial and temporal impacts of different factors on human activity in the GBA, such as day and night, holidays, weather changes, national policies, and the coronavirus disease 2019 (COVID-19) pandemic. Furthermore, the analysis of 12-year-long ambient seismic noise at the Hong Kong seismic station shows a close connection between long-term changes in ambient seismic noise and local social development. This study suggests that the analysis of ambient seismic noise represents a novel method to gather critical information about human activity. Seismometers, which are widely deployed worldwide, have great potential as innovative tools for sensing human activity. Full article
(This article belongs to the Special Issue Urban Sensing Methods and Technologies II)
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