Semantic Segmentation is a technique in Computer Sciences(CS) to extract information from images. Recent advances in Artificial Intelligence, particularly in Deep Learning, Semantic Segmentation combined with techniques such as convolutional neural networks, have presented better results and exciting results. Due to its power and better results than classical approaches, there has been an increase in research articles in Remote Sensing that propose using deep learning-based semantic Segmentation to extract information from satellite or airborne imagery. In this paper, we surveyed the state-of-the-art of Semantic Segmentation in Remote Sensing from 2010 until 2020 by identifying the research topics and the number of publications and citations. Furthermore, we also pointed out the fundamental algorithms, the main convolutional neural network architectures, backbones, and the most used evaluation metrics. In addition, some datasets were highlighted, as well as some frameworks that can be used to train semantic segmentation deep neural networks. Finally, we have shown some applications of the showcased techniques and concluded the paper by pointing out some research opportunities of Remote Sensing Semantic Segmentation, concerning some bleeding-edge scientific papers published in 2020 in CS.
A utilização da Especificação Técnica para Estruturação de Dados Geoespaciais Vetoriais (ET-EDGV) já é uma realidade para todos os órgãos produtores de Dados Cartográficos no Brasil. O Decreto 6.666/08 institui a Infraestrutura Nacional de Dados Geoespaciais (INDE) e determina que todos os órgãos e entidades do Poder Executivo Federal devem seguir as normas e padrões homologados pela INDE. Neste sentido, a Diretoria de Serviço Geográfico do Exército Brasileiro (DSG) desenvolveu uma solução baseada em software livre para prover, de forma interoperável, a criação, edição e remoção de agregações hierárquicas de geo-objetos. Tais agregações são chamadas de Elementos Complexos. Os Elementos Complexos estendem a noção de feição geográfica, permitindo uma agregação semântica de geo-objetos com diferentes primitivas geométricas. Neste trabalho é mostrada a solução desenvolvida para o software livre QGIS utilizando o PostGIS e o Spatialite como bancos de dados.
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