Thomas BLASCHKE - Josef STROBL - Julia WEGMAYR (Eds.)


GI_Forum 2021, Volume 9, Issue 1

12th International Symposium on Digital Earth


ISSN 2308-1708
Online Edition

ISBN 978-3-7001-8947-3
Online Edition
doi:10.1553/giscience2021_01_
GI_Forum 2021,  Volume 9,  Issue 1 
 
Open access


Verlag der Österreichischen Akademie der Wissenschaften
Austrian Academy of Sciences Press
A-1011 Wien, Dr. Ignaz Seipel-Platz 2
Tel. +43-1-515 81/DW 3420, Fax +43-1-515 81/DW 3400
https://verlag.oeaw.ac.at, e-mail: verlag@oeaw.ac.at

GI_Forum publishes high quality original research across the transdisciplinary field of Geographic Information Science (GIScience). The journal provides a platform for dialogue among GI-Scientists and educators, technologists and critical thinkers in an ongoing effort to advance the field and ultimately contribute to the creation of an informed GISociety. Submissions concentrate on innovation in education, science, methodology and technologies in the spatial domain and their role towards a more just, ethical and sustainable science and society. GI_Forum implements the policy of open access publication after a double-blind peer review process through a highly international team of seasoned scientists for quality assurance. Special emphasis is put on actively supporting young scientists through formative reviews of their submissions. Only English language contributions are published.


Starting 2016, GI_Forum publishes two issues a Year.
Joumal Information is available at: GI-Forum

GI_Forum is listed on the Directory of Open Access Journals (DOAJ)

Bestellung/Order


GI_Forum 2021, Volume 9, Issue 1

ISSN 2308-1708
Online Edition

ISBN 978-3-7001-8947-3
Online Edition



Send or fax to your local bookseller or to:

Verlag der Österreichischen Akademie der Wissenschaften
Austrian Academy of Sciences Press
A-1011 Wien, Dr. Ignaz Seipel-Platz 2,
Tel. +43-1-515 81/DW 3420, Fax +43-1-515 81/DW 3400
https://verlag.oeaw.ac.at, e-mail: bestellung.verlag@oeaw.ac.at
UID-Nr.: ATU 16251605, FN 71839x Handelsgericht Wien, DVR: 0096385

Bitte senden Sie mir
Please send me
 
Exemplar(e) der genannten Publikation
copy(ies) of the publication overleaf


NAME


ADRESSE / ADDRESS


ORT / CITY


LAND / COUNTRY


ZAHLUNGSMETHODE / METHOD OF PAYMENT
    Visa     Euro / Master     American Express


NUMMER

Ablaufdatum / Expiry date:  

    I will send a cheque           Vorausrechnung / Send me a proforma invoice
 
DATUM, UNTERSCHRIFT / DATE, SIGNATURE

BANK AUSTRIA CREDITANSTALT, WIEN (IBAN AT04 1100 0006 2280 0100, BIC BKAUATWW), DEUTSCHE BANK MÜNCHEN (IBAN DE16 7007 0024 0238 8270 00, BIC DEUTDEDBMUC)
X
BibTEX-Export:

X
EndNote/Zotero-Export:

X
RIS-Export:

X 
Researchgate-Export (COinS)

Permanent QR-Code

doi:10.1553/giscience2021_01_s209



doi:10.1553/giscience2021_01_s209



Thema: geography
Thomas BLASCHKE - Josef STROBL - Julia WEGMAYR (Eds.)


GI_Forum 2021, Volume 9, Issue 1

12th International Symposium on Digital Earth


ISSN 2308-1708
Online Edition

ISBN 978-3-7001-8947-3
Online Edition
doi:10.1553/giscience2021_01_
GI_Forum 2021,  Volume 9,  Issue 1 
 
Open access


Stefan Lang, Lorenz Wendt, Dirk Tiede, Yunya Gao, Vanessa Streifender, Hira Zafar, Adebowale Adebayo, Gina Schwendemann, Peter Jeremias
S.  209 - 219
doi:10.1553/giscience2021_01_s209

Open access

Verlag der Österreichischen Akademie der Wissenschaften


doi:10.1553/giscience2021_01_s209
Abstract:
Amongst the many benefits of remote sensing techniques in disaster- or conflict-related applications, timeliness and objectivity may be the most critical assets. Recently, increasing sensor quality and data availability have shifted the attention more towards the information extraction process itself. With promising results obtained by deep learning (DL), the notion arises that DL is not agnostic to input errors or biases introduced, in particular in sample-scarce situations. The present work seeks to understand the influence of different sample quality aspects propagating through network layers in automated image analysis. In this paper, we broadly discuss the conceptualisation of such a sample database in an early stage of realisation: (1) inherited properties (quality parameters of the underlying image such as cloud cover, seasonality, etc.); (2) individual (i.e., per-sample) properties, including a. lineage and provenance, b. geometric properties (size, orientation, shape), c. spectral features (standardized colour code); (3) context-related properties (arrangement Several hundred samples collected from different camp settings were hand-selected and annotated with computed features in an initial stage. The supervised annotation routine is automated so that thousands of existing samples can be labelled with this extended feature set. This should better condition the subsequent DL tasks in a hybrid AI approach.

Keywords:  humanitarian action, earth observation, deep learning, data assimilation, hybrid AI, sample quality, automation
  2021/06/29 11:16:58
Object Identifier:  0xc1aa5576 0x003c9b7a
https://creativecommons.org/licenses/by-nd/4.0/

GI_Forum publishes high quality original research across the transdisciplinary field of Geographic Information Science (GIScience). The journal provides a platform for dialogue among GI-Scientists and educators, technologists and critical thinkers in an ongoing effort to advance the field and ultimately contribute to the creation of an informed GISociety. Submissions concentrate on innovation in education, science, methodology and technologies in the spatial domain and their role towards a more just, ethical and sustainable science and society. GI_Forum implements the policy of open access publication after a double-blind peer review process through a highly international team of seasoned scientists for quality assurance. Special emphasis is put on actively supporting young scientists through formative reviews of their submissions. Only English language contributions are published.


Starting 2016, GI_Forum publishes two issues a Year.
Joumal Information is available at: GI-Forum

GI_Forum is listed on the Directory of Open Access Journals (DOAJ)



Vergessen Sie nicht das Login am Server, wenn Sie auf Kapitel zugreifen wollen, die nicht allgemein zugänglich sind.
Links zu diesen Dokumenten werden erst nach dem Login sichtbar.
Do not forget to Login on the server if you want to access chapters that are not freely accessible.
Links to these documents will only be visible after logon.

Verlag der Österreichischen Akademie der Wissenschaften
Austrian Academy of Sciences Press
A-1011 Wien, Dr. Ignaz Seipel-Platz 2
Tel. +43-1-515 81/DW 3420, Fax +43-1-515 81/DW 3400
https://verlag.oeaw.ac.at, e-mail: verlag@oeaw.ac.at