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Multimodal Data Fusion for Urban Environmental Monitoring and Management

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A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Radar Sensors".

Deadline for manuscript submissions: closed (20 June 2022) | Viewed by 7360

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Special Issue Editors

Dr. Yindan Zhang

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Guest Editor

Academy of Advanced Interdisciplinary Research, Xidian University, Xi’an 710071, China
Interests: object-based image analysis (GEOBIA); artificial intelligence (e.g., deep learning) for remote sensing image interpretation; geospatial data mining and understanding; remote sensing of urban environment

Dr. Gang Chen

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Guest Editor

Laboratory for Remote Sensing and Environmental Change, Department of Geography and Earth Sciences, University of North Carolina at Charlotte, Charlotte, NC 28277, USA
Interests: remote sensing; forest disturbances; GEOBIA; spatial ecology
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Prof. Dr. Shihong Du

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Guest Editor

School of Earth and Space Sciences, Peking University, Beijing 100871, China
Interests: image classification; spatial analysis; deep learning; sample learning; urban landscape
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Zhifeng Wu

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Guest Editor

School of Geographical Sciences, Guangzhou University, Guangzhou 510006, China
Interests: urban remote sensing; ecological remote sensing; GIS; extensive data analysis; natural resource remote sensing monitoring and assessment
Special Issues, Collections and Topics in MDPI journals

Dr. Kunwar K. Singh

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Guest Editor

1. AidData, Global Research Institute, College of William and Mary, Williamsburg, VA 23185, USA
2. Center for Geospatial Analysis, College of William and Mary, Williamsburg, VA 23185, USA
Interests: geospatial analysis of land and vegetation dynamics; vegetation (forest and crop) phenology; invasive plant light detection and ranging; drones

Special Issue Information

Dear Colleagues,

The causes and pressures of the majority of today's environmental problems can be traced back, directly or indirectly, to urban areas. The forces and processes that constitute "urban activity" have far-reaching and long-term effects on its immediate boundaries and the entire region in which it is positioned. Over recent decades, Earth Observation (E.O.) for urban areas has become an essential means of characterizing urban sprawl, monitoring the consequences of anthropogenic activities within cities, and offering critical findings to assist urban researchers or managers in making informed decisions. With enhanced E.O. capabilities, remote sensing data from multiple platforms, multiple sensors, and multiple dates are becoming ubiquitous. Advanced physical and machine learning models have facilitated multimodal data integration for urban environmental studies with promising results. However, the unification of information for urban applications remains challenging due to (i) finding appropriate information fusion strategies that can adapt to various urban environments being difficult and (ii) balancing big data analytics and information needs for dealing with specific environmental issues is tricky.

This Special Issue invites submissions on the latest advances in multimodal data fusion for mapping and monitoring the urban environment. The focus of the contributions to the Special Issue will be on reviewing current progress, highlighting the latest methodologies proposed to respond to the needs of multimodal data processing for urban environmental monitoring and management, and pointing out the strategies that may meet the requirements of potential applications.

The topics of interest include (but not limited to):

Novel, computationally efficient algorithms for the processing and fusion of data from multi-sensors, multi-sources, and multi-temporal acquisitions;
New methodologies, e.g., data registration, efficient processing for complex, big data, data quality assurance/pre-processing, deep learning, etc., for urban environment monitoring and management;
Innovative applications for urban change detection, LCLU mapping, disaster monitoring, responses, etc.;
Interdisciplinary and higher-level studies on various aspects of employing multimodal data fusion such as feasibility, strength, challenges, and effectiveness.

Dr. Yindan Zhang
Dr. Gang Chen
Prof. Dr. Shihong Du
Prof. Dr. Zhifeng Wu
Dr. Kunwar K. Singh
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. Sensors 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 2600 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

multimodal
data fusion
urban remote sensing
land-cove and land-use (LCLU) mapping
change detection
disaster monitoring and responses
data assurance/pre-processing
deep learning

Published Papers (3 papers)

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Research

15 pages, 2373 KiB

Open AccessArticle

Environmental Controls to Soil Heavy Metal Pollution Vary at Multiple Scales in a Highly Urbanizing Region in Southern China

by Cheng Li, Xinyu Jiang, Heng Jiang, Qinge Sha, Xiangdong Li, Guanglin Jia, Jiong Cheng and Junyu Zheng

Sensors 2022, 22(12), 4496; https://doi.org/10.3390/s22124496 - 14 Jun 2022

Cited by 7 | Viewed by 1613

Abstract

Natural and anthropogenic activities affect soil heavy metal pollution at different spatial scales. Quantifying the spatial variability of soil pollution and its driving forces at different scales is essential for pollution mitigation opportunities. This study applied a multivariate factorial kriging technique to investigate the spatial variability of soil heavy metal pollution and its relationship with environmental factors at multiple scales in a highly urbanized area of Guangzhou, South China. We collected 318 topsoil samples and used five types of environmental factors for the attribution analysis. By factorial kriging, we decomposed the total variance of soil pollution into a nugget effect, a short-range (3 km) variance and a long-range (12 km) variance. The distribution of patches with a high soil pollution level was scattered in the eastern and northwestern parts of the study domain at a short-range scale, while they were more clustered at a long-range scale. The correlations between the soil pollution and environmental factors were either enhanced or counteracted across the three distinct scales. The predictors of soil heavy metal pollution changed from the soil physiochemical properties to anthropogenic dominated factors with the studied scale increase. Our study results suggest that the soil physiochemical properties were a good proxy to soil pollution across the scales. Improving the soil physiochemical properties such as increasing the soil organic matter is essentially effective across scales while restoring vegetation around pollutant sources as a nature-based solution at a large scale would be beneficial for alleviating local soil pollution. Full article

(This article belongs to the Special Issue Multimodal Data Fusion for Urban Environmental Monitoring and Management)

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14 pages, 3855 KiB

Open AccessArticle

Urban Greening Effect on Land Surface Temperature

by Anita Zaitunah, Samsuri, Angelia Frecella Silitonga and Lailan Syaufina

Sensors 2022, 22(11), 4168; https://doi.org/10.3390/s22114168 - 30 May 2022

Cited by 7 | Viewed by 1872

Abstract

Urbanization has accelerated the conversion of vegetated land to built-up regions. The purpose of this study was to evaluate the effects of urban park configuration on the Land Surface Temperature of the park and adjacent areas. In urban parks, the study analyzed the Normalized Difference Vegetation Index (NDVI), the Normalized Difference Built-up Index (NDBI), and the Land Surface Temperature (LST). The NDVI categorization process resulted in the development of a vegetation density distribution. The majority of Medan’s urban areas were categorized as low density, as seen by their low NDVI values. The NDBI values were significantly higher in the majority of the area. This shows that the majority of places are experiencing a decline in vegetation cover. The density of vegetation varies according to the placement of park components such as trees, mixed plants, recreation, and sports areas. According to LST data, the temperature in the urban park was cooler than in the surrounding areas. Although the surrounding areas are densely populated, urban parks are dominated by trees. Additionally, there is a green space adjacent to the park, which is a green lane that runs alongside the main roadways. Full article

(This article belongs to the Special Issue Multimodal Data Fusion for Urban Environmental Monitoring and Management)

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21 pages, 7624 KiB

Open AccessArticle

AGs-Unet: Building Extraction Model for High Resolution Remote Sensing Images Based on Attention Gates U Network

by Mingyang Yu, Xiaoxian Chen, Wenzhuo Zhang and Yaohui Liu

Sensors 2022, 22(8), 2932; https://doi.org/10.3390/s22082932 - 11 Apr 2022

Cited by 24 | Viewed by 2810

Abstract

Building contour extraction from high-resolution remote sensing images is a basic task for the reasonable planning of regional construction. Recently, building segmentation methods based on the U-Net network have become popular as they largely improve the segmentation accuracy by applying ‘skip connection’ to combine high-level and low-level feature information more effectively. Meanwhile, researchers have demonstrated that introducing an attention mechanism into U-Net can enhance local feature expression and improve the performance of building extraction in remote sensing images. In this paper, we intend to explore the effectiveness of the primeval attention gate module and propose the novel Attention Gate Module (AG) based on adjusting the position of ‘Resampler’ in an attention gate to Sigmoid function for a building extraction task, and a novel Attention Gates U network (AGs-Unet) is further proposed based on AG, which can automatically learn different forms of building structures in high-resolution remote sensing images and realize efficient extraction of building contour. AGs-Unet integrates attention gates with a single U-Net network, in which a series of attention gate modules are added into the ‘skip connection’ for suppressing the irrelevant and noisy feature responses in the input image to highlight the dominant features of the buildings in the image. AGs-Unet improves the feature selection of the attention map to enhance the ability of feature learning, as well as paying attention to the feature information of small-scale buildings. We conducted the experiments on the WHU building dataset and the INRIA Aerial Image Labeling dataset, in which the proposed AGs-Unet model is compared with several classic models (such as FCN8s, SegNet, U-Net, and DANet) and two state-of-the-art models (such as PISANet, and ARC-Net). The extraction accuracy of each model is evaluated by using three evaluation indexes, namely, overall accuracy, precision, and intersection over union. Experimental results show that the proposed AGs-Unet model can improve the quality of building extraction from high-resolution remote sensing images effectively in terms of prediction performance and result accuracy. Full article

(This article belongs to the Special Issue Multimodal Data Fusion for Urban Environmental Monitoring and Management)

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