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Deep Learning for Remote Sensing images with R language.

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Palestrante
Dr. Ricardo Dal'Agnol

Ricardo Dalagnol is an Environmental Engineer (2011), Master (2014) and PhD (2020) in Remote Sensing by the National Institute for Space Research (INPE/Brazil). He currently works as a Post-Doctoral Researcher funded by FAPESP in the Division of Earth Observation and Geoinformatics at INPE. He has experience with several applications in the area of Biogeosciences, with a focus on tropical ecology, nature conservation and environmental monitoring in the Amazon rainforest. He specializes in conducting quantitative research using advanced data types such as high-resolution satellite optical and airborne LIDAR data, as well as methods such as deep learning convolutional neural networks. His current project is to develop methods and analyses based on remote sensing to understand tree mortality in the Amazon.

Palestrante
Dr. Ricardo Dal'Agnol

Ricardo Dalagnol is an Environmental Engineer (2011), Master (2014) and PhD (2020) in Remote Sensing by the National Institute for Space Research (INPE/Brazil). He currently works as a Post-Doctoral Researcher funded by FAPESP in the Division of Earth Observation and Geoinformatics at INPE. He has experience with several applications in the area of Biogeosciences, with a focus on tropical ecology, nature conservation and environmental monitoring in the Amazon rainforest. He specializes in conducting quantitative research using advanced data types such as high-resolution satellite optical and airborne LIDAR data, as well as methods such as deep learning convolutional neural networks. His current project is to develop methods and analyses based on remote sensing to understand tree mortality in the Amazon.

Sobre o conteúdo

It will cover basic concepts of deep learning for remote sensing images, the main steps for its application will be identified, and its practical application will be demonstrated. Based on R codes made available for the participants, they will perform some processing steps on their machines and/or in the Google Colab environment. A practical example will be conducted regarding crop mapping using the U-Net architecture. Requirements: R and RStudio (newest versions is fine), access to Google Colab (https://colab.research.google.com/), and basic R language knowledge. Deep Learning para imagens de Sensoriamento Remoto Serão abordados conceitos básicos de deep learning, identificados os principais passos para sua aplicação com sensoriamento remoto, e demonstrada uma aplicação na prática. A partir de códigos disponibilizados para teste, os participantes realizarão alguns passos dos processamentos em suas máquinas e/ou no ambiente Google Colab. Será realizado um exemplo prático de mapeamento agrícola com a arquitetura U-Net.

Sobre o evento

The IEEE GRSS Young Professionals (YP) & ISPRS Student Consortium (SC) Summer School (SS) are events organized jointly in Brazil and supported by the two main international organizations in the field of remote sensing: IEEE Geoscience and Remote Sensing Society: is a non-profit society, and its area of interest is the theory, concepts and scientific and engineering techniques applied to Remote Sensing of the Earth, ocean, atmosphere, and space. In addition, it is interested in the processing, interpretation, and dissemination of this spatial information. International Society for Photogrammetry and Remote Sensing: is a non-governmental organization that aims to develop international cooperation for the advancement of Photogrammetry and Remote Sensing and its applications.

O acesso ao conteúdo é 100% gratuito. A emissão do certificado custa R$ 12,90.

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