Add to ORKGIn this paper, we use deep learning to estimate living conditions in India. We use both census and surveys to train the models. Our procedure achieves comparable results to those found in the literature, but for a wide range of outcomes
There is increasing interest in leveraging Earth Observation (EO) and geospatial data to predict and...
Previous studies have sought to use Convolutional Neural Networks for regional estimation of poverty...
Machine learning techniques have been frequently applied to map urban deprivation (commonly referred...
Using deep learning with satellite images enhances our understanding of human development at a granu...
High resolution poverty mapping supports evidence-based policy and research, yet about half of all c...
International audienceThe challenges of Reproducibility and Replicability (R & R) in computer scienc...
In many cities of the Global South, informal and deprived neighborhoods, also commonly called slums,...
Recent advances in artificial intelligence and deep machine learning have created a step change in h...
Abstract: As the universe finds it challenging to define poverty, the world bank views poverty as an...
This paper provides evidence on the usefulness of very high spatial resolution (VHR) imagery in gath...
Poverty is one of the most important determinants of adverse health outcomes globally, a major cause...
The lack of reliable data in developing countries is a major obstacle to sustainable development, fo...
As developing nations like South Africa chart a path of socio-economic development, the spatialisa...
In the cities of the Global South, slum settlements are growing in size and number, but their locati...
Poverty alleviation continues to be paramount for developing countries. This necessitates the need f...
There is increasing interest in leveraging Earth Observation (EO) and geospatial data to predict and...
Previous studies have sought to use Convolutional Neural Networks for regional estimation of poverty...
Machine learning techniques have been frequently applied to map urban deprivation (commonly referred...
Using deep learning with satellite images enhances our understanding of human development at a granu...
High resolution poverty mapping supports evidence-based policy and research, yet about half of all c...
International audienceThe challenges of Reproducibility and Replicability (R & R) in computer scienc...
In many cities of the Global South, informal and deprived neighborhoods, also commonly called slums,...
Recent advances in artificial intelligence and deep machine learning have created a step change in h...
Abstract: As the universe finds it challenging to define poverty, the world bank views poverty as an...
This paper provides evidence on the usefulness of very high spatial resolution (VHR) imagery in gath...
Poverty is one of the most important determinants of adverse health outcomes globally, a major cause...
The lack of reliable data in developing countries is a major obstacle to sustainable development, fo...
As developing nations like South Africa chart a path of socio-economic development, the spatialisa...
In the cities of the Global South, slum settlements are growing in size and number, but their locati...
Poverty alleviation continues to be paramount for developing countries. This necessitates the need f...
There is increasing interest in leveraging Earth Observation (EO) and geospatial data to predict and...
Previous studies have sought to use Convolutional Neural Networks for regional estimation of poverty...
Machine learning techniques have been frequently applied to map urban deprivation (commonly referred...