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Iz stranih časopisa

U tekstu je dan popis radova koji su objavljeni u stranim časopisima

Volunteered Geographic Information: a 10-year bibliometric investigation

Volunteered Geographic Information (VGI) has become more evident at the same time as open-source platforms become worldwide popular, both resulting from people easily accessing geographic information on their smartphones. Aiming to investigate the main aspects of this research field, a bibliometric investigation was developed focusing on 10-year period (2011-2020). The analyses were performed based on Scopus database, VOS Viewer and Bibliometrix softwares, approaching: publications over years, document types, subject areas, core sources, main papers, countries, authors and most recurrent keywords. The initial results indicated that: publications have increased at an annual rate of 21.69%, the most published document type was article and only 16 journals were responsible for 33.33% of those 1200 articles published. USA, Germany and UK are major countries researching VGI and the last two are also host countries of the main authors. Although the term VGI has been defined among Citizen Science, the network of keywords occurrence showed that GIS (Geographic Information Systems) is an outstanding study field. However, the network visualization based on average publication per year revealed Citizen Science as a research field still moving forward. Keywords such as OpenStreetMap, data quality, accuracy assessment, social media and crowdsourcing showed to be more widespread among the field, the opposite occurs with applications in urban areas, land use and ecosystem services. Overall, the bibliometric indicators have revealed to be effective in order to access VGI as a research topic and indicated a promising trend in themes involving social media, remote sensing, urban area, crowdsourcing and PPGIS.A Informação Geográfica Voluntária (VGI) tornou-se mais evidente ao mesmo tempo em que as plataformas de código aberto se tornaram populares em todo o mundo, ambas resultantes do fácil acesso das pessoas às informações geográficas em seus smartphones. Com o objetivo de investigar os principais aspectos deste campo de pesquisa, foi desenvolvida uma investigação bibliométrica com foco num período de 10 anos (2011-2020).  A análise foi realizada com base no banco de dados Scopus e nos softwares VOS Viewer e Bibliometrix, abordando: publicações ao longo dos anos, tipos de documentos, campos de estudo, principais periódicos, principais artigos, países, autores e palavras-chave mais recorrentes. Os resultados iniciais indicaram que: as publicações aumentaram a uma taxa anual de 21.69%, o tipo de documento mais publicado foi artigo e apenas 16 periódicos foram responsáveis por 33.33% dos 1200 artigos publicados. EUA, Alemanha e Reino Unido são os principais países que pesquisam VGI e os dois últimos também são países-sede dos principais autores. Apesar do termo VGI ter sido definido em meio a Ciência Cidadã, a rede de ocorrência de palavras-chave mostrou que SIG (Sistema de Informação Geográfica) é um campo de estudo de destaque. Contudo, a rede de visualização com base em média de publicações por ano revelou a Ciência Cidadã como um campo de pesquisa ainda em avanço. Palavras-chave como OpenStreetMap, qualidade dos dados, avaliação da precisão, mídias sociais e coletividade mostraram-se mais difundidas no campo, o oposto ocorre com aplicações em áreas urbanas, uso do solo e serviços ecossistêmicos. No geral, os indicadores bibliométricos revelaram-se eficazes para acessar a VGI como tópico de pesquisa e indicaram uma tendência promissora em temas envolvendo redes sociais, sensoriamento remoto, área urbana, colaboração coletiva e PPGIS

Can linguistic features extracted from geo-referenced tweets help building function classification in remote sensing?

The fusion of two or more different data sources is a widely accepted technique in remote sensing while becoming increasingly important due to the availability of big Earth Observation satellite data. As a complementary source of geo-information to satellite data, massive text messages from social media form a temporally quasi-seamless, spatially multi-perspective stream, but with unknown and diverse quality. Despite the uncontrolled quality: can linguistic features extracted from geo-referenced tweets support remote sensing tasks? This work presents a straightforward decision fusion framework for very high-resolution remote sensing images and Twitter text messages. We apply our proposed fusion framework to a land-use classification task - the building function classification task - in which we classify building functions like commercial or residential based on linguistic features derived from tweets and remote sensing images. Using building tags from OpenStreetMap (OSM), we labeled tweets and very high-resolution (VHR) images from Google Maps. We collected English tweets from San Francisco, New York City, Los Angeles, and Washington D.C. and trained a stacked bi-directional LSTM neural network with these tweets. For the aerial images, we predicted building functions with state-of-the-art Convolutional Neural Network (CNN) architectures fine-tuned from ImageNet on the given task. After predicting each modality separately, we combined the prediction probabilities of both models building-wise at a decision level. We show that the proposed fusion framework can improve the classification results of the building type classification task. To the best of our knowledge, we are the first to use semantic contents of Twitter messages and fusing them with remote sensing images to classify building functions at a single building level