{"id":2824,"date":"2020-09-03T10:44:30","date_gmt":"2020-09-03T10:44:30","guid":{"rendered":"https:\/\/futurecities.iaac.net\/expo2020\/?page_id=2824"},"modified":"2020-09-03T10:46:26","modified_gmt":"2020-09-03T10:46:26","slug":"healing-city","status":"publish","type":"page","link":"https:\/\/futurecities.iaac.net\/expo2020\/healing-city\/","title":{"rendered":"Healing City"},"content":{"rendered":"
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Healing City<\/h1>\t\t\t\t\t
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MaCT 02 2018\/2020<\/h6><\/div><\/div><\/div><\/div>
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Faculty: Areti Markopoulou and Mathilde Marengo<\/h1><\/div><\/div><\/div><\/div>
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Student: Luyang Zhang<\/h1>\t\t\t\t\t
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Healing City is a project that explores the positive applications of surveillance technology for improving the urban environment and living following emotion\u2019s data of citizens. The project explores the use of facial recognition technology in order to collect emotional information of people interacting with different typologies of urban environment. Intense green spaces, parks or low density areas for instance are connected with emotions of calmness or satisfaction while high dense areas, lack of urban furniture or bad shading conditions in public spaces are connected with highly stressful emotions and discontent. The project focuses on collecting emotional \u2013 urban environment crowdsourced data by building a system operating in facial recognition. This way a catalogue of different typologies of urban space in relation to different emotions is created.<\/p>\n<\/div>\n<\/div><\/div><\/div><\/div><\/div><\/div>

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Identifying risk areas and urban problems through image analytics and computer vision to propose improvements for the urban environment in order to enhance people\u2019s experience in cities.<\/span><\/strong><\/h1>\n<\/div>\n<\/div><\/div><\/div><\/div><\/div><\/div>