Deep Learning and Google Earth Engine Applied to Mapping Eucalyptus

The potential of integrating deep learning and Google Earth Engine (GEE) is a few explored in the literature. Here, we investigated their potential in the context of Eucalyptus mapping in Brazilian Savanah. Based on GEE API using python language, it is possible to integrate it with Google Colab. The experiments were conducted using the U-Net semantic segmentation method. A total of 704, 88, and 88 patches were used for training, validation, and test, respectively. The overall accuracy obtained in the test dataset was 96.88%, while the Jaccard index was 0.84. These results demonstrated the applicability of these platforms for using deep learning techniques for mapping based on satellite images.