Community Morphology in Conservation Units in the Lower Rio Negro, Amazonas: Incipient Urbanization within the Regional Framework of Extensive Urbanization

• Grant number: 25/17276-6
• Support Opportunities: Regular Research Grants
• Start date: February 01, 2026
• End date: January 31, 2027
• Field of knowledge: Interdisciplinary Subjects
• Principal Investigator: Álvaro de Oliveira D'Antona
• Grantee: Álvaro de Oliveira D'Antona
• Host Institution: Faculdade de Ciências Aplicadas (FCA). Universidade Estadual de Campinas (UNICAMP). Limeira , SP, Brazil
• City of the host institution: Limeira
• Associated research grant: 20/09838-0 - BI0S - Brazilian Institute of Data Science, AP.PCPE

Abstract

Population densification in communities along the Lower Rio Negro (Amazonas) is part of the ongoing transformation in local land use and land cover. Understanding incipient urbanization in relation to socio-environmental dynamics is crucial for the management of Conservation Units (CU) within the broader context of extensive urbanization in the Amazon. This poses a theoretical and methodological challenge: starting from local case studies, gather data and information to enable the application of techniques (such as training for image processing) that contribute to understanding the regional context. This justifies linking the undergraduate research proposal to the BI0S - Brazilian Institute of Data Science (FAPESP 20/09838-0).The project aims to classify 32 communities according to their morphology (distribution of houses in relation to infrastructure and surrounding land uses). The goal is to contribute to the characterization of the local (proto-urban) urban network within the framework of extensive urbanization, with future applications of machine learning and artificial intelligence (AI) techniques for land use and land cover classification in other contexts. The data were collected during the project Traditional populations in protected areas (FAPESP 20/08242-7), through fieldwork in Lower Rio Negro communities in 2022, during which 81 drone flights were carried out. The processed orthophotos will serve as the basis for community classification. Through spatial analysis, we expect to gain a better understanding of territorial ordering processes influencing settlement patterns and community morphology. The results and the theoretical-methodological approach adopted will provide inputs for remote sensing image classification that accounts for the complexity of the urban network throughout the region - to be developed in the future within the BI0S context. (AU)