Deep Learning Applications with Remote Sensing Data
Dr. Ricardo Dalagnol discusses lessons learned and future opportunities based on applications of deep learning with remote sensing data in the last several years by the research group TREES in Brazil and CTREES in California.
Title: Deep Learning applications with Remote Sensing data: lessons learned and opportunities
Speaker: Ricardo Dal'Agnol da Silva (UCLA/JPL)
Abstract: We’ll go through applications of deep learning with remote sensing data conducted in the last years by the research group TREES in Brazil and CTREES in California/US. The main focus will be on forest applications (tree crown, tree cover, species, degradation) but also covering some applications for urban environments. We’ll cover lessons learned from those studies and give some insight into future opportunities.
Speaker Bio: Ricardo Dalagnol is a remote sensing scientist, specialized in conducting quantitative research with high-resolution satellite optical and airborne lidar data, as well as methods such as deep learning convolutional neural networks. His current project consists in studying tropical forests degradation.
