AI/ML applied to improve sanitary conditions

AI/ML applied to improve sanitary conditions

Also related to livelihood and health conditions, over a billion people in the world are living in urban areas lacking basic sanitation services, water, and/or electricity. It is expected that as the global population grows by the millions, 1 in 4 people on the planet will live in an urban settlement by 2030, without access to essential services.

Bangalore is one of the most crowded cities in India. Home to more than 8 million people, around 8% of the city’s population lives in slums. This reality inspired deep learning research that strives to segment and detect those geographical movements.

In this study, researchers found that the first step in rehabilitating crowded areas is by mapping and monitoring field dynamics. Previously, those tasks were carried out manually by human annotators and consumed a vast amount of time and effort. The focus of this application was to automate an inefficient process used to identify changes in satellite images. Doing so will make it easier to monitor how those geographical areas evolve. The study explored the potential of fully convolutional networks (FCNs) to analyze the temporal dynamics of small clusters of temporary slums using very high resolution (VHR) imagery.