Geospatial data disaggregation through self-trained encoder–decoder convolutional models

Datasets collecting demographic and socio-economic statistics are widely available. Still, the data are often only released for highly aggregated geospatial areas, which can mask important local hotspots. When conducting spatial analysis, one often needs to disaggregate the source data, transforming...

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Bibliographic Details
Main Author: Monteiro, João (author)
Other Authors: Martins, Bruno (author), Costa, Miguel (author), Pires, João M. (author)
Format: article
Language:eng
Published: 2022
Subjects:
Convolutional neural networks
Dasymetric disaggregation
Deep learning
Encoder–decoder neural networks
Geospatial data disaggregation
Self-supervised learning
Geography, Planning and Development
Computers in Earth Sciences
Earth and Planetary Sciences (miscellaneous)
Online Access:http://hdl.handle.net/10362/142607
Country:Portugal
Oai:oai:run.unl.pt:10362/142607

Internet

http://hdl.handle.net/10362/142607

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