Analysis of digital classifications of land use by multisensors and organization of geospatial databases

• Grant number: 23/04008-8
• Support Opportunities: Scholarships in Brazil - Post-Doctoral
• Start date: July 01, 2023
• End date: February 29, 2024
• Field of knowledge: Physical Sciences and Mathematics - Geosciences - Physical Geography
• Agreement: MCTI/MC
• Principal Investigator: Édson Luis Bolfe
• Grantee: Claudinei Oliveira dos Santos
• Host Institution: Embrapa Agricultura Digital. Empresa Brasileira de Pesquisa Agropecuária (EMBRAPA). Ministério da Agricultura, Pecuária e Abastecimento (Brasil). Campinas , SP, Brazil
• Associated research grant: 22/09319-9 - Center of Science for Development in Digital Agriculture - CCD-AD/SemeAr, AP.CCD

Abstract

Different agricultural mapping initiatives have been carried out to support rural planning and crop prediction. However, due to the complex dynamics and large scale of Brazilian agriculture, it is necessary to generate methodologies with greater detail, frequency and precision to discriminate between agricultural crops, pastures and forestry. The growing increase in spatial, temporal and spectral resolution of available satellite images, the high computational capacity for processing large volumes of data, and the development of artificial intelligence associated with classifiers based on machine learning algorithms, enhance research in sensing agricultural remote for classification, monitoring and agricultural management. With the implementation of this postdoctoral fellowship, the objective is to analyze the accuracy of different machine learning algorithms in the process of digital classification of data obtained by the individual and/or combined use of multisensor images and to establish a geospatial database in the regions of the Agrotechnological Districts (DAT s) defined by the Center. Among the results, it is expected to generate new methodologies applied to agricultural monitoring, qualified scientific publications, and to promote postgraduate professional training.