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Data Mining and Machine Learning in Urban Applications

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

Deadline for manuscript submissions: closed (30 June 2021) | Viewed by 27323

Special Issue Editors


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Guest Editor
Faculty of Engineering & Information Technology, University of Technology Sydney, Sydney, Australia
Interests: (big) data mining; evolutionary computation; operation research; artificial intelligence; smart cities and systems; reliability & stochastic analysis
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
Department of Civil Engineering, NIT Silchar, Cachar, Assam, India
Interests: reliability and risk analysis; optimization under uncertainty; uncertainty quantification; infrastructure and community resilience; decision theory; numerical methods; model order reduction; machine/deep learning; data-driven modeling
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
Hanson Center for Space Sciences, University of Texas at Dallas, 800 W. Campbell Rd, Richardson, TX 75080, USA
Interests: service of society using machine learning; remote sensing; smart cities; IOT; remote control vehicles (aerial, water and ground); data driven scientific discovery; data driven insights and decision support
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Driven by the challenges of rapid urbanization, urban cities are determined to implement advanced sociotechnical changes and be transformed into smart cities. The success of such a transformation, however, greatly depends on a thorough understanding of the spatiotemporal dynamics. With the advancement of computer vision technology, there is much discussion in the engineering and science community about the application of artificial intelligence (AI) and machine learning towards big data analytics and digitization.

The aim of this Special Issue is to present the state of the art and practices in “Data Mining and Machine Learning in Urban Applications”. This collection will provide the reader with a wide range of computer vision-based theoretical research along with practical developments. Some of the prospective/encouraged topics for this issue include (but are not limited to):

List of Topics (papers must show relevance to AI and machine learning)

  • Data analytics in urban applications;
  • Big data perspective on digitization;
  • Smart city digital twins;
  • Remote sensing applications in urban disaster management;
  • Probabilistic methods in machine/deep learning;
  • Data-driven methods in urban applications;
  • Sustainable and resilient urban infrastructure;
  • Data-driven methods in decision-making.

We encourage papers that demonstrate new research that show the added value of machine learning compared to traditional methods, preferably demonstrated with case studies.

Dr. Amir H. Gandomi
Dr. Subhrajit Dutta
Prof. David J. Lary
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

  • big data analytics
  • artificial intelligence and machine learning
  • urban infrastructure
  • smart and sustainable cities

Published Papers (5 papers)

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24 pages, 10589 KiB  
Article
Identifying Streetscape Features Using VHR Imagery and Deep Learning Applications
by Deepank Verma, Olaf Mumm and Vanessa Miriam Carlow
Remote Sens. 2021, 13(17), 3363; https://doi.org/10.3390/rs13173363 - 25 Aug 2021
Cited by 5 | Viewed by 3618
Abstract
Deep Learning (DL) based identification and detection of elements in urban spaces through Earth Observation (EO) datasets have been widely researched and discussed. Such studies have developed state-of-the-art methods to map urban features like building footprint or roads in detail. This study delves [...] Read more.
Deep Learning (DL) based identification and detection of elements in urban spaces through Earth Observation (EO) datasets have been widely researched and discussed. Such studies have developed state-of-the-art methods to map urban features like building footprint or roads in detail. This study delves deeper into combining multiple such studies to identify fine-grained urban features which define streetscapes. Specifically, the research focuses on employing object detection and semantic segmentation models and other computer vision methods to identify ten streetscape features such as movement corridors, roadways, sidewalks, bike paths, on-street parking, vehicles, trees, vegetation, road markings, and buildings. The training data for identifying and classifying all the elements except road markings are collected from open sources and finetuned to fit the study’s context. The training dataset is manually created and employed to delineate road markings. Apart from the model-specific evaluation on the test-set of the data, the study creates its own test dataset from the study area to analyze these models’ performance. The outputs from these models are further integrated to develop a geospatial dataset, which is additionally utilized to generate 3D views and street cross-sections for the city. The trained models and data sources are discussed in the research and are made available for urban researchers to exploit. Full article
(This article belongs to the Special Issue Data Mining and Machine Learning in Urban Applications)
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20 pages, 1939 KiB  
Article
Hybrid Spatial–Temporal Graph Convolutional Networks for On-Street Parking Availability Prediction
by Xiao Xiao, Zhiling Jin, Yilong Hui, Yueshen Xu and Wei Shao
Remote Sens. 2021, 13(16), 3338; https://doi.org/10.3390/rs13163338 - 23 Aug 2021
Cited by 19 | Viewed by 3315
Abstract
With the development of sensors and of the Internet of Things (IoT), smart cities can provide people with a variety of information for a more convenient life. Effective on-street parking availability prediction can improve parking efficiency and, at times, alleviate city congestion. Conventional [...] Read more.
With the development of sensors and of the Internet of Things (IoT), smart cities can provide people with a variety of information for a more convenient life. Effective on-street parking availability prediction can improve parking efficiency and, at times, alleviate city congestion. Conventional methods of parking availability prediction often do not consider the spatial–temporal features of parking duration distributions. To this end, we propose a parking space prediction scheme called the hybrid spatial–temporal graph convolution networks (HST-GCNs). We use graph convolutional networks and gated linear units (GLUs) with a 1D convolutional neural network to obtain the spatial features and the temporal features, respectively. Then, we construct a spatial–temporal convolutional block to obtain the instantaneous spatial–temporal correlations. Based on the similarity of the parking duration distributions, we propose an attention mechanism called distAtt to measure the similarity of parking duration distributions. Through the distAtt mechanism, we add the long-term spatial–temporal correlations to our spatial–temporal convolutional block, and thus, we can capture complex hybrid spatial–temporal correlations to achieve a higher accuracy of parking availability prediction. Based on real-world datasets, we compare the proposed scheme with the benchmark models. The experimental results show that the proposed scheme has the best performance in predicting the parking occupancy rate. Full article
(This article belongs to the Special Issue Data Mining and Machine Learning in Urban Applications)
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21 pages, 4471 KiB  
Article
Permeable Breakwaters Performance Modeling: A Comparative Study of Machine Learning Techniques
by Mostafa Gandomi, Moharram Dolatshahi Pirooz, Iman Varjavand and Mohammad Reza Nikoo
Remote Sens. 2020, 12(11), 1856; https://doi.org/10.3390/rs12111856 - 8 Jun 2020
Cited by 10 | Viewed by 2408
Abstract
The advantage of permeable breakwaters over more traditional types has attracted great interest in the behavior of these structures in the field of engineering. The main objective of this study is to apply 19 well-known machine learning regressors to derive the best model [...] Read more.
The advantage of permeable breakwaters over more traditional types has attracted great interest in the behavior of these structures in the field of engineering. The main objective of this study is to apply 19 well-known machine learning regressors to derive the best model of innovative breakwater hydrodynamic behavior with reflection and transmission coefficients as the target parameters. A database of 360 laboratory tests on the low-scale breakwater is used to establish the model. The proposed models link the reflection and transmission coefficients to seven dimensionless parameters, including relative chamber width, relative rockfill height, relative chamber width in terms of wavelength, wave steepness, wave number multiplied by water depth, and relative wave height in terms of rockfill height. For the validation of the models, the cross-validation method was used for all models except the multilayer perceptron neural network (MLP) and genetic programming (GP) models. To validate the MLP and GP, the database is divided into three categories: training, validation, and testing. Furthermore, two explicit functional relationships are developed by utilizing the GP for each target. The exponential Gaussian process regression (GPR) model in predicting the reflection coefficient (R2 = 0.95, OBJ function = 0.0273), and similarly, the exponential GPR model in predicting the transmission coefficient (R2 = 0.98, OBJ function = 0.0267) showed the best performance and the highest correlation with the actual records and can further be used as a reference for engineers in practical work. Also, the sensitivity analysis of the proposed models determined that the relative height parameter of the rockfill material has the greatest contribution to the introduced breakwater behavior. Full article
(This article belongs to the Special Issue Data Mining and Machine Learning in Urban Applications)
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18 pages, 3809 KiB  
Article
A Machine Learning-Based Classification System for Urban Built-Up Areas Using Multiple Classifiers and Data Sources
by Lang Sun, Lina Tang, Guofan Shao, Quanyi Qiu, Ting Lan and Jinyuan Shao
Remote Sens. 2020, 12(1), 91; https://doi.org/10.3390/rs12010091 - 25 Dec 2019
Cited by 25 | Viewed by 4597
Abstract
Information about urban built-up areas is important for urban planning and management. However, obtaining accurate information about urban built-up areas is a challenge. This study developed a general-purpose built-up area intelligent classification (BAIC) system that supports various types of data and classifiers. All [...] Read more.
Information about urban built-up areas is important for urban planning and management. However, obtaining accurate information about urban built-up areas is a challenge. This study developed a general-purpose built-up area intelligent classification (BAIC) system that supports various types of data and classifiers. All of the steps in the BAIC were implemented using Python modules including Numpy, Pandas, matplotlib, and scikit-learn. We used the BAIC to conduct a classification experiment that involved seven types of input data; namely, Point of Interest (POI), Road Network (RN), nighttime light (NTL), a combination of POI and RN data (POI_RN), a combination of POI and NTL data (POI_NTL), a combination of RN and NTL data (RN_NTL), and a combination of POI, RN, and NTL data (POI_RN_NTL), and five classifiers, namely, Logistic Regression (LR), Decision Tree (DT), Random Forests (RF), Gradient Boosted Decision Trees (GBDT), and AdaBoost. The results show the following: (1) among the 35 combinations of the five classifiers and seven types of input data, the overall accuracy (OA) ranged from 76 to 89%, F1 values ranged from 0.73 to 0.86, and the area under the receiver operating characteristic (ROC) curve (AUC) ranged from 0.83 to 0.95. The largest F1 value and OA were obtained using the POI_RN_NTL data and AdaBoost, while the largest AUC was obtained using POI_RN_NTL and POI_NTL data against AdaBoost, LR, and RF; and (2) the advantages of the BAIC include its support for multi-source input data, its objective accuracy assessment, and its robust classifiers. The BAIC can quickly and efficiently realize the automatic classification of urban built-up areas at a reasonably low cost and can be readily applied to other urban areas in the world where any kind of POI, RN, or NTL data coverage is available. The results of this study are expected to provide timely and effective reference information for urban planning and urban management departments, and could also potentially be used to develop large-scale maps of urban built-up areas in the future. Full article
(This article belongs to the Special Issue Data Mining and Machine Learning in Urban Applications)
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14 pages, 4648 KiB  
Letter
U-Net-Id, an Instance Segmentation Model for Building Extraction from Satellite Images—Case Study in the Joanópolis City, Brazil
by Fabien H. Wagner, Ricardo Dalagnol, Yuliya Tarabalka, Tassiana Y. F. Segantine, Rogério Thomé and Mayumi C. M. Hirye
Remote Sens. 2020, 12(10), 1544; https://doi.org/10.3390/rs12101544 - 12 May 2020
Cited by 44 | Viewed by 11670
Abstract
Currently, there exists a growing demand for individual building mapping in regions of rapid urban growth in less-developed countries. Most existing methods can segment buildings but cannot discriminate adjacent buildings. Here, we present a new convolutional neural network architecture (CNN) called U-net-id that [...] Read more.
Currently, there exists a growing demand for individual building mapping in regions of rapid urban growth in less-developed countries. Most existing methods can segment buildings but cannot discriminate adjacent buildings. Here, we present a new convolutional neural network architecture (CNN) called U-net-id that performs building instance segmentation. The proposed network is trained with WorldView-3 satellite RGB images (0.3 m) and three different labeled masks. The first is the building mask; the second is the border mask, which is the border of the building segment with 4 pixels added outside and 3 pixels inside; and the third is the inner segment mask, which is the segment of the building diminished by 2 pixels. The architecture consists of three parallel paths, one for each mask, all starting with a U-net model. To accurately capture the overlap between the masks, all activation layers of the U-nets are copied and concatenated on each path and sent to two additional convolutional layers before the output activation layers. The method was tested with a dataset of 7563 manually delineated individual buildings of the city of Joanópolis-SP, Brazil. On this dataset, the semantic segmentation showed an overall accuracy of 97.67% and an F1-Score of 0.937 and the building individual instance segmentation showed good performance with a mean intersection over union (IoU) of 0.582 (median IoU = 0.694). Full article
(This article belongs to the Special Issue Data Mining and Machine Learning in Urban Applications)
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