*Artigos cientĂficos* 2/2
Uma metodologia de #aprendizadoprofundo #deeplearning de baixo custo que integra o #OpenStreetMap para tarefas de #urbanismo em mĂșltiplas ĂĄreas. Vetores #OSM servem como mĂĄscaras de referĂȘncia para #imagens de satĂ©lite para previsĂ”es via #CNN e #GNN. PrecisĂŁo >90%.
Os novos produtos de #sensoriamentoremoto precisam de #geodados #vetoriais. DaĂ o interesse em investir na completude + homogeneidade dos dados #OSM
https://www.nature.com/articles/s41598-026-45927-x
*Scientific papers* 2/2
A #lowcost #deeplearning methodology integrating #OpenStreetMap for #urban planning tasks across multiple domains. #OSM #vectors serve as reference masks for #satelliteimagery to enable predictions via #CNN and #GNN. Accuracy >90%.
New #remotesensing products require #vector #geodata. Hence the value of investing in the completeness and consistency of #OSM data
https://www.
nature.com/articles/s41598-026
This model uses six filters, then sixteen in the next layer.
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