Výsledky vyhledávání - "Matthias Häberle"

Akademický článek

Geo-spatial text-mining from Twitter – a feature space analysis with a view toward building classification in urban regions

Autor: Matthias Häberle, Martin Werner, Xiao Xiang Zhu

Publikováno v: European Journal of Remote Sensing, Vol 52, Iss 0, Pp 2-11 (2019)

By the year 2050, it is expected that about 68% of global population will live in cities. To understand the emerging changes in urban structures, new data sources like social media must be taken into account. In this work, we conduct a feature space

Externí odkaz: https://doaj.org/article/359c7ccac014462c8f398ebf4098d5cc

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

Autor: Matthias Häberle, Eike Jens Hoffmann, Xiao Xiang Zhu

Publikováno v: ISPRS Journal of Photogrammetry and Remote Sensing

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=r3c4b2081b22::73edbec74418ca2649f2d024c781240e

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Assessment of effective mitigation and prediction of the spread of SARS-CoV-2 in Germany using demographic information and spatial resolution

Publikováno v: Mathematical Biosciences
Mathematical biosciences
United States

Non-pharmaceutical interventions (NPIs) are important to mitigate the spread of infectious diseases as long as no vaccination or outstanding medical treatments are available. We assess the effectiveness of the sets of non-pharmaceutical interventions

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::a2d91537e45dc5f0ed0709c01f44cebc
https://elib.dlr.de/146707/

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Building Type Classification from Social Media Texts via geo-spatial Textmining

Autor: Martin Werner, Xiao Xiang Zhu, Matthias Häberle

Publikováno v: IGARSS 2019-2019 IEEE International Geoscience and Remote Sensing Symposium
IGARSS
IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 2019

In this work, we present a model for building type classifica tion from Twitter text messages (tweets) by employing geo-spatial text mining methods. First, we apply standard text pre-processing methods and convert the tweets into sentence vectors usi

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::cdc70c984ef8b8f42f59b22ac29c6f26
https://elib.dlr.de/127637/

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