Bayesian wavelet change detection using multiple data sets in urban areas

• Grant number: 25/27107-7
• Support Opportunities: Scholarships abroad - Research Internship - Post-doctor
• Start date: December 01, 2026
• End date: April 30, 2027
• Field of knowledge: Physical Sciences and Mathematics - Probability and Statistics - Statistics
• Principal Investigator: Aluísio de Souza Pinheiro
• Grantee: Giovanni Pastori Piccirilli
• Supervisor: Paolo Ettore Gamba
• Host Institution: Instituto de Matemática, Estatística e Computação Científica (IMECC). Universidade Estadual de Campinas (UNICAMP). Campinas , SP, Brazil
• Institution abroad: Università degli Studi di Pavia, Italy
• Associated to the scholarship: 24/22101-8 - Clustering and feature screening in Functional regression model by Variational Inference., BP.PD

Abstract

Change detection is an important task performed in remote sensing image that allows researchers and engineers to identify and evaluate modifications on land surfacescaptured by multi-temporal satellite images. Analyzing problems such as deforestation(Barreto et al., 2016), rapid urbanization (Ban and Yousif, 2012) and glacier melting(Scher et al., 2021) are of great importance to study the dynamics of regions sensi tive to climate changes and human activity. Furthermore, the increase on availabilityof satellite images in the past years raises the challenge of analyzing large imagesavailable over long periods. Kernel methods play a central role in modern MachineLearning and Deep Learning, thanks to their ability to model complex nonlinear relationships through functions that remain linear in their parameters. Their effectivenessdepends on the choice of kernel function and its hyperparameters, which are typicallyselected through cross-validation. Bayesian Kernel Learning has emerged as a promising alternative, offering a principled probabilistic framework that enables automatickernel selection, interpretability, and uncertainty quantification. These projects intend the student to spend seven months in the Laboratoire d'Informatique, Syst`emes,Traitement de l'Information et de la Connaissance, Universit´e of Savoie-Mont Blanc, atAnnecy, France (LISTIC-USMB), to work under the supervision of Prof. Abdou Atto,and five months in the Telecommunications & Remote Sensing Laboratory, Universityof Pavia (TCLAN-UNIPV) to work under the supervision of Prof. Paolo Gamba. Thework will focus on the development of wavelet statistical methodologies for changedetection on multi-temporal satellite images, specially under Bayesian approach. (AU)