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Distributed and Remote Sensing of the Urban Environment

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

Deadline for manuscript submissions: closed (30 September 2020) | Viewed by 25058

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Special Issue Editors

Assoc. Prof. Barak Fishbain

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Guest Editor

Technion Enviromatics Lab (TechEL) Dept. of Environmental, Water and Agricultural Engineering Faculty of Civil & Environmental Engineering Technion - Israel Institute of Technology Haifa, Israel
Interests: Enviromatics—devising machine learning methods; optimization and physical models for better understanding of the environment

Dr. Shai Kendler

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Guest Editor

Technion Enviromatics Lab (TechEL) Department of Environmental, Water and Agricultural Engineering Faculty of Civil & Environmental Engineering, Technion Israel Institute of Technology, Haifa 3200003, Israel
Interests: sensing, sensor design and operation; spectroscopy; hyperspectral imaging; data analysis; network deployment; artificial intelligence
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Urban areas are highly populated areas that combine residential, commercial, industrial, business areas with an extensive infrastructure to support city life. This infrastructure includes water supply, sewage, electricity, communication, and transportation. While this elaborated infrastructure makes our cities attractive, it may also harm humans due to emissions of toxic materials, noise, and radiation to the environment (air, water, and soil). Recent advances in wireless distributed sensor networks (WDSN) and remote sensing technologies as well as supporting technologies (communication, energy sources, drone) provide an excellent opportunity for early detection of these emissions; hence, timely and well-focused regulation and mitigating acts can be applied.

This Special Issue aims to provide a comprehensive view of state-of-the-art systems employing innovative sensing schemes for the urban environment.

Topics of interest include but are not limited to the following:

Wireless distributed sensors for air, water, and soil;
Deployment strategies for sensors;
Heterogenous sensor networks data processing and fusion;
Mobile autonomous sensing platforms;
Remote sensing in complex scenes;
In-doors air quality monitoring;
Monitoring the integrity of the infrastructure (sewage, water, and electricity);
Development of new tools for decision-makers to process multimodal sensor data for evidence-based regulation;
Sensors for first responders;
WDSN strategies for catastrophic events in an urban complex environment;
Algorithms for signal interpretation.

Assoc. Prof. Barak Fishbain
Assoc. Prof. Shai Kendler
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.

Published Papers (7 papers)

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Editorial

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7 pages, 821 KiB

Open AccessEditorial

The Challenges of Prolonged Gas Sensing in the Modern Urban Environment

by Shai Kendler and Asaf Zuck

Sensors 2020, 20(18), 5189; https://doi.org/10.3390/s20185189 - 11 Sep 2020

Cited by 4 | Viewed by 1756

Abstract

The increase in the urban population is impacting the environment in several ways, including air pollution due to emissions from automobiles and industry. The reduction of air pollution requires reliable and detailed information regarding air pollution levels. Broad deployment of sensors can provide such information that, in turn, can be used for the establishment of mitigating and regulating acts. However, a prerequisite of such a deployment strategy is using highly durable sensors. The sensors must be able to operate for long periods of time under severe conditions such as high humidity, solar radiation, and dust. In recent years, there has been an ongoing effort to ruggedize sensors for industrial applications with an emphasis on elevated temperature, humidity, and pressure. Some of these developments are adapted for urban air sensing applications. However, protection from dust is based on filters that have not been modified in the last few decades. Such filters clog over time, thus requiring frequent replacement. This editorial presents the need for a consumable-free dust removal device that provides consistent performance without affecting the sensing process. A specific solution for removing dust using a cyclone dust separator is presented. The cyclone dust separator is implemented as an add-on module to protect commercially available sensors. Full article

(This article belongs to the Special Issue Distributed and Remote Sensing of the Urban Environment)

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Research

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14 pages, 4258 KiB

Open AccessArticle

An Improved Method of Determining Human Population Distribution Based on Luojia 1-01 Nighttime Light Imagery and Road Network Data—A Case Study of the City of Shenzhen

by Qiang Zhou, Yuanmao Zheng, Jinyuan Shao, Yinglun Lin and Haowei Wang

Sensors 2020, 20(18), 5032; https://doi.org/10.3390/s20185032 - 04 Sep 2020

Cited by 6 | Viewed by 3821

Abstract

Previously published studies on population distribution were based on the provincial level, while the number of urban-level studies is more limited. In addition, the rough spatial resolution of traditional nighttime light (NTL) data has limited their fine application in current small-scale population distribution research. For the purpose of studying the spatial distribution of populations at the urban scale, we proposed a new index (i.e., the road network adjusted human settlement index, RNAHSI) by integrating Luojia 1-01 (LJ 1-01) NTL data, the enhanced vegetation index (EVI), and road network density (RND) data based on population density relationships to depict the spatial distribution of urban human settlements. The RNAHSI updated the high-resolution NTL data and combined the RND data on the basis of human settlement index (HSI) data to refine the spatial pattern of urban population distribution. The results indicated that the mean relative error (MRE) between the population estimation data based on the RNAHSI and the demographic data was 34.80%, which was lower than that in the HSI and WorldPop dataset. This index is suitable primarily for the study of urban population distribution, as the RNAHSI can clearly highlight human activities in areas with dense urban road networks and can refine the spatial heterogeneity of impervious areas. In addition, we also drew a population density map of the city of Shenzhen with a 100 m spatial resolution for 2018 based on the RNAHSI, which has great reference significance for urban management and urban resource allocation. Full article

(This article belongs to the Special Issue Distributed and Remote Sensing of the Urban Environment)

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12 pages, 3281 KiB

Open AccessArticle

Street-Scale Analysis of Population Exposure to Light Pollution Based on Remote Sensing and Mobile Big Data—Shenzhen City as a Case

by Bo Sun, Yang Zhang, Qiming Zhou and Duo Gao

Sensors 2020, 20(9), 2728; https://doi.org/10.3390/s20092728 - 11 May 2020

Cited by 5 | Viewed by 3407

Abstract

Most studies on light pollution are based on light intensity retrieved from nighttime light (NTL) remote sensing with less consideration of the population factors. Furthermore, the coarse spatial resolution of traditional NTL remote sensing data limits the refined applications in current smart city studies. In order to analyze the influence of light pollution on populated areas, this study proposes an index named population exposure to light pollution (PELP) and conducts a street-scale analysis to illustrate spatial variation of PELP among residential areas in cites. By taking Shenzhen city as a case, multi-source data were combined including high resolution NTL remote sensing data from the Luojia 1-01 satellite sensor, high-precision mobile big data for visualizing human activities and population distribution as well as point of interest (POI) data. Results show that the main influenced areas of light pollution are concentrated in the downtown and core areas of newly expanded areas with obvious deviation corrected like traditional serious light polluted regions (e.g., ports). In comparison, commercial–residential mixed areas and village-in-city show a high level of PELP. The proposed method better presents the extent of population exposure to light pollution at a fine-grid scale and the regional difference between different types of residential areas in a city. Full article

(This article belongs to the Special Issue Distributed and Remote Sensing of the Urban Environment)

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29 pages, 6783 KiB

Open AccessArticle

Evaluation of the Use of Compressed Sensing in Data Harvesting for Vehicular Sensor Networks

by Juan Antonio Martinez, Pedro Miguel Ruiz and Antonio F. Skarmeta

Sensors 2020, 20(5), 1434; https://doi.org/10.3390/s20051434 - 06 Mar 2020

Cited by 2 | Viewed by 2059

Abstract

We propose a new harvesting approach for Vehicular Sensor Networks based on compressed sensing (CS) technology called Compressed Sensing-based Vehicular Data Harvesting (CS-VDH). This compression technology allows for the reduction of the information volume that nodes must send back to the fusion center and also an accurate recovery of the original data, even in absence of several original measurements. Our proposed method, thanks to a proper design of a delay function, orders the transmission of these measurements, being the nodes farther from the fusion center, the ones starting this transmission. This way, intermediate nodes are more likely to introduce their measurements in a packet traversing the network and to apply the CS technology. This way the contribution is twofold, adding different measurements to traversing packets, we reduce the total overload of the network, and also reducing the size of the packets thanks to the applied compression technology. We evaluate our solution by using ns-2 simulations in a realistic vehicular environment generated by SUMO, a well-known traffic simulator tool in the Vehicular Network domain. Our simulations show that CS-VDH outperforms Delay-Bounded Vehicular Data Gathering (DB-VDG), a well-known protocol for data gathering in vehicular sensor networks which considers a specific delay bound. We also evaluated the proper design of our delay function, as well as the accuracy in the reconstruction of the original data. Regarding this latter topic, our experiments proved that our proposed solution can recover sampled data with little error while still reducing the amount of information traveling through the vehicular network. Full article

(This article belongs to the Special Issue Distributed and Remote Sensing of the Urban Environment)

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Other

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14 pages, 9305 KiB

Open AccessLetter

Mapping Utility Poles in Aerial Orthoimages Using ATSS Deep Learning Method

by Matheus Gomes, Jonathan Silva, Diogo Gonçalves, Pedro Zamboni, Jader Perez, Edson Batista, Ana Ramos, Lucas Osco, Edson Matsubara, Jonathan Li, José Marcato Junior and Wesley Gonçalves

Sensors 2020, 20(21), 6070; https://doi.org/10.3390/s20216070 - 26 Oct 2020

Cited by 15 | Viewed by 5023

Abstract

Mapping utility poles using side-view images acquired with car-mounted cameras is a time-consuming task, mainly in larger areas due to the need for street-by-street surveying. Aerial images cover larger areas and can be feasible alternatives although the detection and mapping of the utility poles in urban environments using top-view images is challenging. Thus, we propose the use of Adaptive Training Sample Selection (ATSS) for detecting utility poles in urban areas since it is a novel method and has not yet investigated in remote sensing applications. Here, we compared ATSS with Faster Region-based Convolutional Neural Networks (Faster R-CNN) and Focal Loss for Dense Object Detection (RetinaNet ), currently used in remote sensing applications, to assess the performance of the proposed methodology. We used 99,473 patches of 256 × 256 pixels with ground sample distance (GSD) of 10 cm. The patches were divided into training, validation and test datasets in approximate proportions of 60%, 20% and 20%, respectively. As the utility pole labels are point coordinates and the object detection methods require a bounding box, we assessed the influence of the bounding box size on the ATSS method by varying the dimensions from

30 \times 30

70 \times 70

pixels. For the proposal task, our findings show that ATSS is, on average, 5% more accurate than Faster R-CNN and RetinaNet. For a bounding box size of

40 \times 40

, we achieved Average Precision with intersection over union of 50% (

A P_{50}

) of 0.913 for ATSS, 0.875 for Faster R-CNN and 0.874 for RetinaNet. Regarding the influence of the bounding box size on ATSS, our results indicate that the

A P_{50}

is about 6.5% higher for

60 \times 60

compared to

30 \times 30

. For

A P_{75}

, this margin reaches 23.1% in favor of the

60 \times 60

bounding box size. In terms of computational costs, all the methods tested remain at the same level, with an average processing time around of 0.048 s per patch. Our findings show that ATSS outperforms other methodologies and is suitable for developing operation tools that can automatically detect and map utility poles. Full article

(This article belongs to the Special Issue Distributed and Remote Sensing of the Urban Environment)

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12 pages, 1766 KiB

Open AccessLetter

Storm-Drain and Manhole Detection Using the RetinaNet Method

by Anderson Santos, José Marcato Junior, Jonathan de Andrade Silva, Rodrigo Pereira, Daniel Matos, Geazy Menezes, Leandro Higa, Anette Eltner, Ana Paula Ramos, Lucas Osco and Wesley Gonçalves

Sensors 2020, 20(16), 4450; https://doi.org/10.3390/s20164450 - 10 Aug 2020

Cited by 25 | Viewed by 4153

Abstract

As key-components of the urban-drainage system, storm-drains and manholes are essential to the hydrological modeling of urban basins. Accurately mapping of these objects can help to improve the storm-drain systems for the prevention and mitigation of urban floods. Novel Deep Learning (DL) methods have been proposed to aid the mapping of these urban features. The main aim of this paper is to evaluate the state-of-the-art object detection method RetinaNet to identify storm-drain and manhole in urban areas in street-level RGB images. The experimental assessment was performed using 297 mobile mapping images captured in 2019 in the streets in six regions in Campo Grande city, located in Mato Grosso do Sul state, Brazil. Two configurations of training, validation, and test images were considered. ResNet-50 and ResNet-101 were adopted in the experimental assessment as the two distinct feature extractor networks (i.e., backbones) for the RetinaNet method. The results were compared with the Faster R-CNN method. The results showed a higher detection accuracy when using RetinaNet with ResNet-50. In conclusion, the assessed DL method is adequate to detect storm-drain and manhole from mobile mapping RGB images, outperforming the Faster R-CNN method. The labeled dataset used in this study is available for future research. Full article

(This article belongs to the Special Issue Distributed and Remote Sensing of the Urban Environment)

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24 pages, 7305 KiB

Open AccessCase Report

Evaluation of LoRa Technology in Flooding Prevention Scenarios

by José Cecílio, Pedro M. Ferreira and António Casimiro

Sensors 2020, 20(14), 4034; https://doi.org/10.3390/s20144034 - 20 Jul 2020

Cited by 23 | Viewed by 4270

Abstract

Global climate change originates frequent floods that may cause severe damage, justifying the need for real-time remote monitoring and alerting systems. Several works deal with LoRa (Long Range) communications over land and in the presence of obstacles, but little is known about LoRa communication reliability over water, as it may happen in real flooding scenarios. One aspect that is known to influence the communication quality is the height at which nodes are placed. However, its impact in water environments is unknown. This is an important aspect that may influence the location of sensor nodes and the network topology. To fill this gap, we conducted several experiments using a real LoRa deployment to evaluate several features related to data communication. We considered two deployment scenarios corresponding to countryside and estuary environments. The nodes were placed at low heights, communicating, respectively, over the ground and over the water. Measurements for packet loss, received signal strength indicator (RSSI), signal-to-noise ratio (SNR) and round-trip time (RTT) were collected during a period of several weeks. Results for both scenarios are presented and compared in this paper. One important conclusion is that the communication distance and reliability are significantly affected by tides when the communication is done over the water and nodes are placed at low heights. Based on the RTT measurements and on the characteristics of the hardware, we also derive a battery lifetime estimation model that may be helpful for the definition of an adequate maintenance plan. Full article

(This article belongs to the Special Issue Distributed and Remote Sensing of the Urban Environment)

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