Deep Learning for House Categorisation, a Proposal Towards Automation in Land Registry

Garc­ía-Retuerta, David; Casado‐Vara, Roberto; Calvo-Rolle, José Luís; Quintián, Héctor; Prieto, Javier

doi:10.1007/978-3-030-61705-9_58

Cited by 3 publications

(1 citation statement)

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“…The Internet of Things is a basic element in the development of intelligent cities [6,29]. City data, especially if accessible in real-time, can be used to effectively transform and manage the city and promote urban planning and development [30]. Appropriate real-time solutions and systems capable of making decisions to solve run-time problems are elements that can improve the efficiency of smart cities [31].…”

Section: Smart Citiesmentioning

confidence: 99%

An Efficient Management Platform for Developing Smart Cities: Solution for Real-Time and Future Crowd Detection

García-Retuerta

Chamoso

Hernández³

et al. 2021

Electronics

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A smart city is an environment that uses innovative technologies to make networks and services more flexible, effective, and sustainable with the use of information, digital, and telecommunication technologies, improving the city’s operations for the benefit of its citizens. Most cities incorporate data acquisition elements from their own systems or those managed by subcontracted companies that can be used to optimise their resources: energy consumption, smart meters, lighting, irrigation water consumption, traffic data, camera images, waste collection, security systems, pollution meters, climate data, etc. The city-as-a-platform concept is becoming popular and it is increasingly evident that cities must have efficient management systems capable of deploying, for instance, IoT platforms, open data, etc., and of using artificial intelligence intensively. For many cities, data collection is not a problem, but managing and analysing data with the aim of optimising resources and improving the lives of citizens is. This article presents deepint.net, a platform for capturing, integrating, analysing, and creating dashboards, alert systems, optimisation models, etc. This article shows how deepint.net has been used to estimate pedestrian traffic on the streets of Melbourne (Australia) using the XGBoost algorithm. Given the current situation, it is advisable not to transit urban roads when overcrowded, thus, the model proposed in this paper (and implemented with deepint.net) facilitates the identification of areas with less pedestrian traffic. This use case is an example of an efficient crowd management system, implemented and operated via a platform that offers many possibilities for the management of the data collected in smart territories and cities.

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