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
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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
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
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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
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e-ISSN
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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
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