Predicting Land Sensing Indicators With Geolocalized Complex Network

Autor(es)

Nespolo, Renan Guilherme ; Valejo, Alan Demetrius Baria ; Lopes, Alneu de Andrade

• Tipo de documento: Artigo Científico
• Fonte: IEEE Geoscience and Remote Sensing Letters; v. 22, p. 5-pg., 2025-01-01.

Resumo

Sensing a land area and collecting the indicators data is a task that can require investment resources. However, the possibility of making a prediction based on a few indicators means a considerable saving of resources. Here, we present a technique to predict indicators using a semi-supervised graph-based regression. We present the geolocalized network based on Pearson correlation (GNB-PC) for this task. To create the network, we combine two different topologies of graphs, and each vertex stores information such as the Pearson coefficient and the discretized value of the indicator. We propose a combination of random walks with linear regression and semi-supervised Pearson correlation for the inference. We adapted the Gibbs sampling for the regression task to evaluate the method. The experiments show that our solution outperforms the baselines when using information from the dataset with geolocalization data in the graph construction step and comparing the proposed framework with state-of-the-art baselines. (AU)

• Processo FAPESP: 21/06210-3 - Serviços cientes dos espaços urbanos via federated learning em sistemas de transporte inteligente
• Beneficiário: Geraldo Pereira Rocha Filho
• Modalidade de apoio: Auxílio à Pesquisa - Regular
• Processo FAPESP: 22/03090-0 - Análise de grandes volumes de dados políticos e redes complexas: mineração, modelagens e aplicações em Ciência Política Computacional
• Beneficiário: Sylvia Iasulaitis
• Modalidade de apoio: Auxílio à Pesquisa - Projeto Inicial
• Processo FAPESP: 22/09091-8 - Criminalidade, insegurança e legitimidade: uma abordagem transdisciplinar
• Beneficiário: Luis Gustavo Nonato
• Modalidade de apoio: Auxílio à Pesquisa - Programa eScience e Data Science - Temático
• Processo FAPESP: 20/09835-1 - IARA - Inteligência Artificial Recriando Ambientes
• Beneficiário: André Carlos Ponce de Leon Ferreira de Carvalho
• Modalidade de apoio: Auxílio à Pesquisa - Programa Centros de Pesquisa Aplicada