Image-based Semantic Segmentation Network for the Brazilian Cerrado based on Public Databases - Coimbra, Daniel C. de; Oliveira, Silas P. W. de; Lemes, Dimas A. M.; Picolo, José G.; Sales, Guilherme Ribeiro; Corso, Valentino; Bezerra, Cides S. (Anais do Workshop de Visão Computacional (WVC); 2024: Anais do XIX Workshop de Visão ComputacionalDO - 10.5753/wvc.2024.34008, 2024)
We have developed fully convolutional networks (FCN) for semantic segmentation of satellite imagery based on five Land Use and Land Cover (LULC) categories: native vegetation, agriculture, pasture, urban region, and waterbody. To this end, we gathered and preprocessed public Brazilian data into an annotated dataset with 26,000 segmented 224 × 224 image patches. We obtained images from the Sino-Brazilian CBERS-04A satellite program and segmentation masks from the Terra Class project (INPE/Embrapa). We performed transfer learning on four backbone models: DeepLabV3+, MobileNet, ResNet-50, and VGG-16. We evaluated their performance with IoU, with the respective scores of 45.96%, 34.40%, 45.58%, and 62.78%. However, our dataset is unbalanced, and a balanced IoU yields scores lower than 20% for all models, indicating specialization on majority classes. Despite these shortcomings, our models’ masks have a 16-times higher pixel-density than previously available masks, and taking only images as input, without external data or expert curation.