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Accounting for the unaccounted (ACCOUNT): A machine-learning based framework for estimating the population of invisible spaces in support of the SDG Slum Indicator

assignment_turned_inproject29 Jan 2020(Started)-31 Dec 9999(Ending)On going (M68)
Funder: Netherlands Organisation for Scientific Research (NWO)Project code: VI.Veni.194.025

Accounting for the unaccounted (ACCOUNT): A machine-learning based framework for estimating the population of invisible spaces in support of the SDG Slum Indicator

Description

In global south cities, population statistics about poor neighbourhoods are often unavailable or ignore large proportions of poor inhabitants. However, such statistics are urgently needed to support slum improvement, disaster response and health interventions. This research utilizes satellite images, machine learning and local data to estimate the number of poor inhabitants.

Partners
Universiteit Twente, Faculty of Geo-Information Science and Earth Observation (ITC) , Universiteit Twente, Faculty of Geo-Information Science and Earth Observation (ITC), Department of Urban and Regional Planning and Geo-Information Management , Universiteit Twente
Data Management Plans
argos Start a new DMP in Argos
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