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A Novel Aerial Dataset for Scene Classification Annotated Using OSM for Learning Deep CNNs

The result's identifiers

  • Result code in IS VaVaI

    <a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F18%3A00322143" target="_blank" >RIV/68407700:21230/18:00322143 - isvavai.cz</a>

  • Result on the web

    <a href="http://radio.feld.cvut.cz/conf/poster/proceedings/Poster_2018/Section_IC/IC_045_Kunc.pdf" target="_blank" >http://radio.feld.cvut.cz/conf/poster/proceedings/Poster_2018/Section_IC/IC_045_Kunc.pdf</a>

  • DOI - Digital Object Identifier

Alternative languages

  • Result language

    angličtina

  • Original language name

    A Novel Aerial Dataset for Scene Classification Annotated Using OSM for Learning Deep CNNs

  • Original language description

    Remote sensing data are getting cheaper and thus tools allowing analysis of large quantities of the data are needed. One of the commonly used tools for automation of remote sensing data are neural networks. However, despite the rising availability of the data, there is no suitable dataset for learning neural networks. This paper introduces novel aerial image datasets that were automatically annotated using labels from OpenStreetMap. The largest of the datasets contains 52,596 400x400 px images divided into 44 classes. These datasets were used for learning a deep state-of-the-art neural network for image classification as the size of the datasets allows to learn such network from scratch which was difficult with currently available datasets. The classification performance of the neural networks represents the baseline performance for the presented datasets and was further analyzed using the gradCAM visualization method.

  • Czech name

  • Czech description

Classification

  • Type

    D - Article in proceedings

  • CEP classification

  • OECD FORD branch

    10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Result continuities

  • Project

  • Continuities

    S - Specificky vyzkum na vysokych skolach

Others

  • Publication year

    2018

  • Confidentiality

    S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Data specific for result type

  • Article name in the collection

    Proceedings of the International Student Scientific Conference Poster – 22/2018

  • ISBN

    978-80-01-06428-3

  • ISSN

  • e-ISSN

  • Number of pages

    5

  • Pages from-to

    1-5

  • Publisher name

    Czech Technical University in Prague

  • Place of publication

    Praha

  • Event location

    Praha

  • Event date

    May 10, 2018

  • Type of event by nationality

    WRD - Celosvětová akce

  • UT code for WoS article