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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: closed (31 July 2022) | Viewed by 24908

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Special Issue Editor

Prof. Dr. Rafal Tomasz Dlugosz

E-Mail Website1 Website2
Guest Editor

1. Faculty of Telecommunication, Computer Science and Electrical Engineering, UTP University of Science and Technology, Bydgoszcz, Poland
2. Active Safety Algorithm Development, Aptiv Services Poland, Kraków, Poland
Interests: algorithm development; artificial intelligence; signal processing; programming and software development; advanced driver assistance systems (ADAS); intelligent transportation system; smart cities; application specific integration circuits (ASIC); low-power CMOS circuits; hardware level data processing

Special Issue Information

Dear Colleagues,

Technological development has for centuries strongly influenced the shaping of urbanized areas. On one hand, it undoubtedly brings many benefits to their residents. However, technology also has a negative impact on urban areas and their surroundings. Many technological solutions lead, for example, to increased levels of pollution, noise, and electromagnetic fields in cities. In extreme cases, technology causes adverse changes in the infrastructure of cities, leading to the degradation of their space.

In recent years, there has been a growing tendency to combine research efforts of specialists from various fields of science, especially engineering, in order to diagnose and counteract urban problems. The development of the concept of smart cities is largely the result of these activities.

Cities and larger urban agglomerations can be treated as a source of various types of data. These data tell a lot about the processes that are taking place in cities. Through their proper analysis, it is possible to outline effective action strategies that will enable the development of cities in a properly understood sustainable direction. Modern urban areas occupy large spaces, which in many cases means very large amounts of data needed for analysis. Downloading data from many often very distant city points increasingly requires the use of remote sensing technologies. At the same time, remote communication technologies are necessary for feedback on modern systems in cities.

This Special Issue will report the latest achievements and developments in the field of applying widely understood remote sensing technologies for collecting data from urbanized areas. On the other hand, in the scope of this Special Issue are various methods of processing and analysis the collected data, to enable problem diagnosis and positive feedback on urbanized areas. This Special Issue aims at joining together two fields. One of them is urban development itself. The topics in this field include but are not limited to the following:

Spatial changes and development in urbanized areas;
Changes of green areas in an urban environment;
Demographic and population changes;
Implementation of new technologies toward smart cities;
Mobility development/intelligent transportation system in urbanized areas;
Climate changes affecting urban development;
Quality of life changes;
Interdisciplinary issues in urban development.

In the area of technical and engineering aspects, the topics include:

Remote data collection;
Remote control systems;
Data sources and sensors: cameras, radars, lidars, IoT, crowdsourcing;
Multidomain data fusion;
Data and signal processing and analysis;
Wireless sensor networks;
Edge computing;
Artificial intelligent systems: artificial neural networks, fuzzy logic system, expert systems, etc.;
Dedicated hardware and software solutions.

Prof. Dr. Rafal Tomasz Dlugosz
Guest Editor

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

Urban development
Remote access
Edge computing
Multidomain data fusion
Signal processing and data mining
Artificial intelligence
Smart/Intelligent cities
Transportation system
Quality of city life

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Published Papers (7 papers)

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Research

19 pages, 7217 KiB

Open AccessArticle

DIC-ST: A Hybrid Prediction Framework Based on Causal Structure Learning for Cellular Traffic and Its Application in Urban Computing

by Kaisa Zhang, Gang Chuai, Jinxi Zhang, Xiangyu Chen, Zhiwei Si and Saidiwaerdi Maimaiti

Remote Sens. 2022, 14(6), 1439; https://doi.org/10.3390/rs14061439 - 16 Mar 2022

Cited by 12 | Viewed by 2862

Abstract

The development of technology has strongly affected regional urbanization. With development of mobile communication technology, intelligent devices have become increasingly widely used in people’s lives. The application of big data in urban computing is multidimensional; it has been involved in different fields, such as urban planning, network optimization, intelligent transportation, energy consumption and so on. Data analysis becomes particularly important for wireless networks. In this paper, a method for analyzing cellular traffic data was proposed. Firstly, a method to extract trend components, periodic components and essential components from complex traffic time series was proposed. Secondly, we introduced causality data mining. Different from traditional time series causality analysis, the depth of causal mining was increased. We conducted causality verification on different components of time series and the results showed that the causal relationship between base stations is different in trend component, periodic component and essential component in urban wireless network. This is crucial for urban planning and network management. Thirdly, DIC-ST: a spatial temporal time series prediction based on decomposition and integration system with causal structure learning was proposed by combining GCN. Final results showed that the proposed method significantly improves the accuracy of cellular traffic prediction. At the same time, this method can play a crucial role for urban computing in network management, intelligent transportation, base station siting and energy consumption when combined with remote sensing map information. Full article

(This article belongs to the Special Issue Remote Sensing for Urban Development and Sustainability)

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23 pages, 22147 KiB

Open AccessArticle

Automatic Generation of Urban Road 3D Models for Pedestrian Studies from LiDAR Data

by David Fernández-Arango, Francisco-Alberto Varela-García, Diego González-Aguilera and Susana Lagüela-López

Remote Sens. 2022, 14(5), 1102; https://doi.org/10.3390/rs14051102 - 24 Feb 2022

Cited by 16 | Viewed by 4038

Abstract

The point clouds acquired with a mobile LiDAR scanner (MLS) have high density and accuracy, which allows one to identify different elements of the road in them, as can be found in many scientific references, especially in the last decade. This study presents a methodology to characterize the urban space available for walking, by segmenting point clouds from data acquired with MLS and automatically generating impedance surfaces to be used in pedestrian accessibility studies. Common problems in the automatic segmentation of the LiDAR point cloud were corrected, achieving a very accurate segmentation of the points belonging to the ground. In addition, problems caused by occlusions caused mainly by parked vehicles and that prevent the availability of LiDAR points in spaces normally intended for pedestrian circulation, such as sidewalks, were solved in the proposed methodology. The innovation of this method lies, therefore, in the high definition of the generated 3D model of the pedestrian space to model pedestrian mobility, which allowed us to apply it in the search for shorter and safer pedestrian paths between the homes and schools of students in urban areas within the Big-Geomove project. Both the developed algorithms and the LiDAR data used are freely licensed for their use in further research. Full article

(This article belongs to the Special Issue Remote Sensing for Urban Development and Sustainability)

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22 pages, 5191 KiB

Open AccessArticle

Air Pollution Monitoring System with Prediction Abilities Based on Smart Autonomous Sensors Equipped with ANNs with Novel Training Scheme

by Marzena Banach, Rafał Długosz, Tomasz Talaśka and Witold Pedrycz

Remote Sens. 2022, 14(2), 413; https://doi.org/10.3390/rs14020413 - 17 Jan 2022

Cited by 6 | Viewed by 2912

Abstract

The paper presents a concept of an air pollution monitoring system with prediction abilities, based on wireless smart sensors, that takes into account local conditions (microclimate) prevailing in particular areas of the city. In most cases reported in the literature, artificial neural networks (ANNs) are used to predict future pollution levels. In existing solutions of this type, ANNs are trained with generalized datasets common for larger areas, e.g., cities. Our investigations show, however, that conditions may strongly differ even between particular streets in the city, which may impact prediction quality. This results from varying density of urban development, different levels of insolation, airiness, amounts of greenery, etc. As a result, with similar values of ANN input signals, such as current pollution levels, temperature, pressure, etc., the results of the prediction may differ significantly from reality. For this reason, we propose an innovative solution, in which particular sensors are equipped with miniaturized low-power ANNs, trained with datasets gathered directly from their closest environment, without a need for the obtaining of such data from a base station. This may simplify the installation and maintenance process of a network of such sensors. In a further part of this work, we dealt with solutions that enable the reduction of the computational complexity of ANNs in the case of their implementation on specialized integrated circuits. We propose replacing the most complex mathematical operations used in the learning algorithm with simpler solutions. A prototype chip containing the main blocks of such an ANN was also designed. Full article

(This article belongs to the Special Issue Remote Sensing for Urban Development and Sustainability)

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27 pages, 16459 KiB

Open AccessArticle

Spatial Dimension of Transport Exclusion Related to Statutory Trade Restriction—The Use of ITS Tools in Studies of Sustainable Urban Development

by Marta Borowska-Stefańska, Michał Kowalski, Szymon Wiśniewski and Paulina Kurzyk

Remote Sens. 2021, 13(23), 4804; https://doi.org/10.3390/rs13234804 - 26 Nov 2021

Cited by 2 | Viewed by 2347

Abstract

The problem of statutory restrictions of the freedom to conduct business activities is a subject addressed by many researchers. On the other hand, there is little research into the spatial aspect of this phenomenon and its impact on the quality of life of the inhabitants of urban centres in terms of their exclusion from one of the key motivations for travelling, namely shopping trips. The main purpose of the article is to determine the impact of the introduction of a statutory restriction on Sunday trading on sustainable urban development in terms of identifying areas excluded from free access to such services within a large urban settlement in Poland. Our studies on accessibility by car utilised data from ITS systems, the assumptions of the probabilistic Huff Model, and methods to determine market catchment areas. The data used in the study were based on the results of a questionnaire survey. The research procedure was conducted for eight scenarios that covered two periods (March 2019 and November 2020) on trading and non-trading Sundays. The conducted research shows that changes in the temporal accessibility of grocery shops in Łódź within the analysed periods are noticeable for trading and non-trading Sundays. In both cases, accessibility by private car is decidedly worse on non-trading Sundays. Transport exclusion from accessibility to grocery shops applies, in particular, to residents of peripheral areas of the city and is further compounded by the statutory Sunday retail restrictions implemented nationwide. Full article

(This article belongs to the Special Issue Remote Sensing for Urban Development and Sustainability)

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25 pages, 10600 KiB

Open AccessArticle

Changes in Urban Mobility Related to the Public Bike System with Regard to Weather Conditions and Statutory Retail Restrictions

by Marta Borowska-Stefańska, Miroslava Mikusova, Michał Kowalski, Paulina Kurzyk and Szymon Wiśniewski

Remote Sens. 2021, 13(18), 3597; https://doi.org/10.3390/rs13183597 - 9 Sep 2021

Cited by 11 | Viewed by 3220

Abstract

The main purpose of the paper is to determine changes in transport behaviour of users of the public bike-share (PBS) scheme in a large Polish city, Łódź. By tracking GPS signals for individual trips taken by PBS users, it was possible to analyse their changeability (time and spatial) for periods before the implementation of statutory Sunday retail restrictions (2017) and after their partial introduction (2018). The study also took into account weather conditions, namely maximum and minimum daily temperatures and daily totals of precipitation recorded by a weather station in Łodź. In order to determine the correlations between certain weather conditions and PBS trips, the authors applied regression analysis. The results of the study showed that weekend cycling is less susceptible to the impact of weather than cycling on weekdays. At the same time, a comparative analysis of trading and non-trading Sundays proved that, during Sundays with retail restrictions, public bikes were used for longer, farther, and slower trips. These observations were confirmed by analyses of maps of traffic structure. Full article

(This article belongs to the Special Issue Remote Sensing for Urban Development and Sustainability)

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25 pages, 26383 KiB

Open AccessArticle

FSRSS-Net: High-Resolution Mapping of Buildings from Middle-Resolution Satellite Images Using a Super-Resolution Semantic Segmentation Network

by Tao Zhang, Hong Tang, Yi Ding, Penglong Li, Chao Ji and Penglei Xu

Remote Sens. 2021, 13(12), 2290; https://doi.org/10.3390/rs13122290 - 11 Jun 2021

Cited by 34 | Viewed by 4112

Abstract

Satellite mapping of buildings and built-up areas used to be delineated from high spatial resolution (e.g., meters or sub-meters) and middle spatial resolution (e.g., tens of meters or hundreds of meters) satellite images, respectively. To the best of our knowledge, it is important to explore a deep-learning approach to delineate high-resolution semantic maps of buildings from middle-resolution satellite images. The approach is termed as super-resolution semantic segmentation in this paper. Specifically, we design a neural network with integrated low-level image features of super-resolution and high-level semantic features of super-resolution, which is trained with Sentinel-2A images (i.e., 10 m) and higher-resolution semantic maps (i.e., 2.5 m). The network, based on super-resolution semantic segmentation features is called FSRSS-Net. In China, the 35 cities are partitioned into three groups, i.e., 19 cities for model training, four cities for quantitative testing and the other 12 cities for qualitative generalization ability analysis of the learned networks. A large-scale sample dataset is created and utilized to train and validate the performance of the FSRSS-Net, which includes 8597 training samples and 766 quantitative accuracy evaluation samples. Quantitative evaluation results show that: (1) based on the 10 m Sentinel-2A image, the FSRSS-Net can achieve super-resolution semantic segmentation and produce 2.5 m building recognition results, and there is little difference between the accuracy of 2.5 m results by FSRSS-Net and 10 m results by U-Net. More importantly, the 2.5 m building recognition results by FSRSS-Net have higher accuracy than the 2.5 m results by U-Net 10 m building recognition results interpolation up-sampling; (2) from the spatial visualization of the results, the building recognition results of 2.5 m are more precise than those of 10 m, and the outline of the building is better depicted. Qualitative analysis shows that: (1) the learned FSRSS-Net can be also well generalized to other cities that are far from training regions; (2) the FSRSS-Net can still achieve comparable results to the U-Net 2 m building recognition results, even when the U-Net is directly trained using both 2-meter resolution GF2 satellite images and corresponding semantic labels. Full article

(This article belongs to the Special Issue Remote Sensing for Urban Development and Sustainability)

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21 pages, 13424 KiB

Open AccessArticle

Topological Space Knowledge Distillation for Compact Road Extraction in Optical Remote Sensing Images

by Kai Geng, Xian Sun, Zhiyuan Yan, Wenhui Diao and Xin Gao

Remote Sens. 2020, 12(19), 3175; https://doi.org/10.3390/rs12193175 - 28 Sep 2020

Cited by 9 | Viewed by 3230

Abstract

Road extraction from optical remote sensing images has drawn much attention in recent decades and has a wide range of applications. Most of the previous studies rarely take into account the unique topological characteristics of the road. It is the most apparent feature of linear structure that describes the variety of connection relationships of the road. However, designing a particular topological feature extraction network usually results in a model that is too heavy and impractical. To address the problems mentioned above, in this paper, we propose a lightweight topological space network for road extraction based on knowledge distillation (TSKD-Road). Specifically, (1) narrow and short roads easily influence topological features extracted directly in optical remote sensing images. Therefore, we propose a denser teacher network for extracting road structures; (2) to enhance the weight of topological features, we propose a topological space loss calculation model with multiple widths and depths; (3) based on the above innovations, a topological space knowledge distillation framework is proposed, which aims to transfer different kinds of knowledge acquired in a heavy net to a lightweight net, while significantly improving the lightweight net’s accuracy. Experiments were conducted on two publicly available benchmark datasets, which show the obvious superiority and effectiveness of our network. Full article

(This article belongs to the Special Issue Remote Sensing for Urban Development and Sustainability)

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