Census data provide detailed information about population characteristics at a coarse resolution. Nevertheless, fine-grained, high-resolution mappings of population counts are increasingly needed to characterize population dynamics and to assess the consequences of climate shocks, natural disasters, investments in infrastructure, development policies, etc. Dissagregating these census is a complex machine learning, and multiple solutions have been proposed in past research. We propose in this paper to view the problem in the context of the aggregate learning paradigm, where the output value for all training points is not known, but where it is only known for aggregates of the points (i.e. in this context, for regions of pixels where a census is available). We demonstrate with a very simple and interpretable model that this method is on par, and even outperforms on some metrics, the state-of-the-art, despite its simplicity.
|titre||Machine Learning and Knowledge Discovery in Databases. ECML PKDD 2019|
|Sous-titre||Würzburg, Germany, September 16–20, 2019, Proceedings, Part III|
|rédacteurs en chef||Ulf Brefeld, Elisa Fromont, Andreas Hotho, Arno Knobbe, Marloes Maathuis, Céline Robardet|
|état||Publié - 30 avr. 2020|
|Nom||Lecture Notes in Computer Science book series (LNCS)|
Accepted at ECML-PKDD 2019 Data on Zenodo: https://zenodo.org/record/3260713