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The scope of the present paper is to promote social, cultural and environmental sustainability in cities by establishing a conceptual framework and the relationship amongst safety in urban public space (UPS), lighting and Information and Communication Technology (ICT)-based surveillance. This framework uses available technologies and tools, as these can be found in urban equipment such as lighting posts, to enhance security and safety in UPS, ensuring protection against attempted criminal activity. Through detailed literary research, publications on security and safety concerning crime and lighting can be divided into two periods, the first one pre-1994, and the second one from 2004–2008. Since then, a significant reduction in the number of publications dealing with lighting and crime is observed, while at the same time, the urban nightscape has been reshaped with the immersion of light-emitting diode (LED) technologies. Especially in the last decade, where most municipalities in the EU28 (European Union of all the member states from the accession of Croatia in 2013 to the withdrawal of the United Kingdom in 2020) are refurbishing their road lighting with LED technology and the consideration of smart networks and surveillance is under development, the use of lighting to deter possible attempted felonies in UPS is not addressed. To capitalize on the potential of lighting as a deterrent, this paper proposes a framework that uses existing technology, namely, dimmable LED light sources, presence sensors, security cameras, as well as emerging techniques such as artificial intelligence (AI)-enabled image recognition algorithms and big data analytics and presents a possible system that could be developed as a stand-alone product to alert possible dangerous situations, deter criminal activity and promote the perception of safety thus linking lighting and ICT-based surveillance towards safety and security in UPS.

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A genetic algorithm and an artificial neural network are deployed for the design of a dynamic multi-layered façade system that adapts in real-time to different weather and occupants’ needs scenarios. The outputs are a set of different performances of the façade insulation cushions, optimized by the previous run of the genetic algorithm. A façade system of ETFE cushions is considered for them to learn from environmental data models. Each façade cushion is set up as an artificial neuron that is linked to the behavior and temperature of the others. The proposed outputs are a set of different performances of the façade system that are optimized through running the genetic algorithm. Façade neurons are configured as genes of the system that is abstractly represented on a digital model. The computational model manages cushion patterns’ performances through several phenotypical adaptations, suggesting that the proposed facade system maximizes its thermal efficiency in different scenarios.

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