FOREST PLANTATION DETECTION THROUGH DEEP SEMANTIC SEGMENTATION

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Dallaqua, F. B. J. R. ; Rosa, R. A. S. ; Schultz, B. ; Faria, L. R. ; Rodrigues, T. G. ; Oliveira, C. G. ; Kieser, M. E. J. ; Malhotra, V ; Dwyer, T. ; Wolfe, D. S. ; Jiang, J ; Shaker, A ; Zhang, H

• Tipo de documento: Artigo Científico
• Fonte: XXIV ISPRS CONGRESS: IMAGING TODAY, FORESEEING TOMORROW, COMMISSION III; v. 43-B3, p. 8-pg., 2022-01-01.

Resumo

Forest plantations play an important role ecologically, contribute to carbon sequestration and support billions of dollars of economic activity each year through sustainable forest management and forest sector value chains. As the global demand for forest products and services increases, the marketplace is seeking more reliable data on forest plantations. Remote sensing technologies allied with machine learning, and most recently deep learning techniques, provide valuable data for inventorying forest plantations and related valuation products. In this work, deep semantic segmentation with U-net architecture was used to detect forest plantation areas using Sentinel-2 and CBERS-4A images of different areas of Brazil. First, the U-net models were built from an area of the Centre-East of Parana State, and then the best models were tested in 3 new areas that present different characteristics. The U-net models built with Sentinel-2 images achieved promising results for areas similar to the ones used in the training set, with F1-score ranging from 0.9171 to 0.9499 and with Kappa values between 0.8712 to 0.9272, demonstrating the feasibility of deep semantic segmentation to detect forest plantations. (AU)

• Processo FAPESP: 20/01468-0 - Solução para avaliação de florestas por sensoriamento remoto
• Beneficiário: Rafael Antonio da Silva Rosa
• Modalidade de apoio: Auxílio à Pesquisa - Pesquisa Inovativa em Pequenas Empresas - PIPE