Understanding the What, Why, and How of Becoming a Smart City: Experiences from Kakinada and Kanpur

Gupta, Khushboo; Hall, Ralph P.

doi:10.3390/smartcities3020014

Cited by 16 publications

(8 citation statements)

References 30 publications

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“…A wide range of sensors and IoT based applications data is being stored and processed in the digital infrastructure of the cities to support decision making [26]. With the change in tools and techniques on the digital platform, a variety of data from apps and other real-time data sources are combined to analyse and make a decision to address specific issues of the city [27].…”

Section: Literature Reviewmentioning

confidence: 99%

Explainable Artificial Intelligence for Developing Smart Cities Solutions

et al. 2020

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Traditional Artificial Intelligence (AI) technologies used in developing smart cities solutions, Machine Learning (ML) and recently Deep Learning (DL), rely more on utilising best representative training datasets and features engineering and less on the available domain expertise. We argue that such an approach to solution development makes the outcome of solutions less explainable, i.e., it is often not possible to explain the results of the model. There is a growing concern among policymakers in cities with this lack of explainability of AI solutions, and this is considered a major hindrance in the wider acceptability and trust in such AI-based solutions. In this work, we survey the concept of ‘explainable deep learning’ as a subset of the ‘explainable AI’ problem and propose a new solution using Semantic Web technologies, demonstrated with a smart cities flood monitoring application in the context of a European Commission-funded project. Monitoring of gullies and drainage in crucial geographical areas susceptible to flooding issues is an important aspect of any flood monitoring solution. Typical solutions for this problem involve the use of cameras to capture images showing the affected areas in real-time with different objects such as leaves, plastic bottles etc., and building a DL-based classifier to detect such objects and classify blockages based on the presence and coverage of these objects in the images. In this work, we uniquely propose an Explainable AI solution using DL and Semantic Web technologies to build a hybrid classifier. In this hybrid classifier, the DL component detects object presence and coverage level and semantic rules designed with close consultation with experts carry out the classification. By using the expert knowledge in the flooding context, our hybrid classifier provides the flexibility on categorising the image using objects and their coverage relationships. The experimental results demonstrated with a real-world use case showed that this hybrid approach of image classification has on average 11% improvement (F-Measure) in image classification performance compared to DL-only classifier. It also has the distinct advantage of integrating experts’ knowledge on defining the decision-making rules to represent the complex circumstances and using such knowledge to explain the results.

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Section: Literature Reviewmentioning

confidence: 99%

Explainable Artificial Intelligence for Developing Smart Cities Solutions

et al. 2020

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“…The present study, similar to a study by Gupta and Hall [58], chose the city level, given the focus of the Smart City Monitor on transforming cities across the Visegrád countries. The examined cities do not belong to the larger group of metropolitan cities, which have better access to smart city expertise and resources.…”

Section: Research Areamentioning

confidence: 99%

Smart Economy and Startup Enterprises in the Visegrád Countries—A Comparative Analysis Based on the Crunchbase Database

2020

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The present study seeks to explore the concept of “smart economy” through the definition of the smart city. It also presents smart city subsystems and the smart city model. It focuses on smart and creative startups within the smart city model. The research examines medium-sized cities in the Visegrád countries (Czech Republic, Slovakia, Poland, Hungary) with a population ranging from 100,000 to 1 million inhabitants for startups. The research question is: Where are the medium-sized cities in the Visegrád countries that are both startup centers and smart cities? In the course of the research, the term “smart cities” was based on the definition set by the European Commission and the definition of startup centers was made using data analysis of the American Crunchbase database. As a result of the two studies, it can be concluded that there are no cities in the Visegrád countries with an above average level of both startup presence and smart cities.

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“…The implementation of SCM at city level is being done through the establishment of Special Purpose Vehicles (SPVs), which comprise the nominees of Central and State Government, and the Urban local bodies on its board. It is important to highlight that SCM is also complemented with several other urban development programs such as the Atal Mission for Rejuvenation and Urban Transformation (AMRUT), Swachh Bharat Abhiyan (Clean India Mission) and Heritage City Development and Augmentation Yojana [5,29,32].…”

Section: Context Of India's Smart Cities Missionmentioning

confidence: 99%

“…These issues are particularly severe in developing countries, due to factors like high population density, poverty, lack of basic infrastructure services, etc. [5].…”

Section: Introductionmentioning

confidence: 99%

Role of Smart Cities in Optimizing Water-Energy-Food Nexus: Opportunities in Nagpur, India

et al. 2020

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The evolving concept of ‘Smart Cities’ (SC) is today gaining global momentum in scientific and policy arenas. With the rising potential for jobs and improved lifestyle, these fast-growing cities are witnessing an ever-increasing concentration of populations and economic activities. However, the core aspect of sustainability is often overshadowed in SC by the components of economic growth. In view of the rising water, energy, and food (collectively referred to as WEF) demands in cities and the interlinkages between WEF systems, this study aims to highlight the role of SC in enhancing WEF resource efficiency from a nexus perspective. To study the current trends of SC developments first, a specific case of proposed Nagpur smart city (India) is reviewed based on document analysis and preliminary discussions with local government officials. Thereafter, bibliometric analysis (based on Scopus data) is conducted to establish a comprehensive understanding of existing SC research in varying domains of interlinked WEF systems. While the proposed Nagpur smart city is observably focused on city-centric goals with a high emphasis on infrastructure development, this study underlines the need for environmental resource conservation at a transboundary level. The key opportunities for optimizing the WEF nexus in Nagpur are then highlighted in reference to the bibliometric analysis.

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Understanding the What, Why, and How of Becoming a Smart City: Experiences from Kakinada and Kanpur

Cited by 16 publications

References 30 publications

Explainable Artificial Intelligence for Developing Smart Cities Solutions

Explainable Artificial Intelligence for Developing Smart Cities Solutions

Smart Economy and Startup Enterprises in the Visegrád Countries—A Comparative Analysis Based on the Crunchbase Database

Role of Smart Cities in Optimizing Water-Energy-Food Nexus: Opportunities in Nagpur, India

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