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Please use this identifier to cite or link to this item: https://doi.org/10.21256/zhaw-25912
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dc.contributor.authorMohanty, Sharada Prasanna-
dc.contributor.authorCzakon, Jakub-
dc.contributor.authorKaczmarek, Kamil A.-
dc.contributor.authorPyskir, Andrzej-
dc.contributor.authorTarasiewicz, Piotr-
dc.contributor.authorKunwar, Saket-
dc.contributor.authorRohrbach, Janick-
dc.contributor.authorLuo, Dave-
dc.contributor.authorPrasad, Manjunath-
dc.contributor.authorFleer, Sascha-
dc.contributor.authorGöpfert, Jan Philip-
dc.contributor.authorTandon, Akshat-
dc.contributor.authorMollard, Guillaume-
dc.contributor.authorRayaprolu, Nikhil-
dc.contributor.authorSalathe, Marcel-
dc.contributor.authorSchilling, Malte-
dc.date.accessioned2022-11-03T15:42:46Z-
dc.date.available2022-11-03T15:42:46Z-
dc.date.issued2020-
dc.identifier.issn2624-8212de_CH
dc.identifier.urihttps://digitalcollection.zhaw.ch/handle/11475/25912-
dc.description.abstractTranslating satellite imagery into maps requires intensive effort and time, especially leading to inaccurate maps of the affected regions during disaster and conflict. The combination of availability of recent datasets and advances in computer vision made through deep learning paved the way toward automated satellite image translation. To facilitate research in this direction, we introduce the Satellite Imagery Competition using a modified SpaceNet dataset. Participants had to come up with different segmentation models to detect positions of buildings on satellite images. In this work, we present five approaches based on improvements of U-Net and Mask R-Convolutional Neuronal Networks models, coupled with unique training adaptations using boosting algorithms, morphological filter, Conditional Random Fields and custom losses. The good results-as high as AP=0.937 and AR=0.959 -from these models demonstrate the feasibility of Deep Learning in automated satellite image annotation.de_CH
dc.language.isoende_CH
dc.publisherFrontiers Research Foundationde_CH
dc.relation.ispartofFrontiers in Artificial Intelligencede_CH
dc.rightshttp://creativecommons.org/licenses/by/4.0/de_CH
dc.subjectDeep learningde_CH
dc.subjectMachine learningde_CH
dc.subjectRemote sensingde_CH
dc.subjectSatellite imageryde_CH
dc.subjectSemantic segmentationde_CH
dc.subject.ddc006: Spezielle Computerverfahrende_CH
dc.titleDeep learning for understanding satellite imagery : an experimental surveyde_CH
dc.typeBeitrag in wissenschaftlicher Zeitschriftde_CH
dcterms.typeTextde_CH
zhaw.departementSchool of Engineeringde_CH
zhaw.organisationalunitInstitut für Datenanalyse und Prozessdesign (IDP)de_CH
dc.identifier.doi10.3389/frai.2020.534696de_CH
dc.identifier.doi10.21256/zhaw-25912-
dc.identifier.pmid33733198de_CH
zhaw.funding.euNode_CH
zhaw.issue534696de_CH
zhaw.originated.zhawYesde_CH
zhaw.publication.statuspublishedVersionde_CH
zhaw.volume3de_CH
zhaw.publication.reviewPeer review (Publikation)de_CH
zhaw.author.additionalNode_CH
zhaw.display.portraitYesde_CH
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Mohanty, S. P., Czakon, J., Kaczmarek, K. A., Pyskir, A., Tarasiewicz, P., Kunwar, S., Rohrbach, J., Luo, D., Prasad, M., Fleer, S., Göpfert, J. P., Tandon, A., Mollard, G., Rayaprolu, N., Salathe, M., & Schilling, M. (2020). Deep learning for understanding satellite imagery : an experimental survey. Frontiers in Artificial Intelligence, 3(534696). https://doi.org/10.3389/frai.2020.534696
Mohanty, S.P. et al. (2020) ‘Deep learning for understanding satellite imagery : an experimental survey’, Frontiers in Artificial Intelligence, 3(534696). Available at: https://doi.org/10.3389/frai.2020.534696.
S. P. Mohanty et al., “Deep learning for understanding satellite imagery : an experimental survey,” Frontiers in Artificial Intelligence, vol. 3, no. 534696, 2020, doi: 10.3389/frai.2020.534696.
MOHANTY, Sharada Prasanna, Jakub CZAKON, Kamil A. KACZMAREK, Andrzej PYSKIR, Piotr TARASIEWICZ, Saket KUNWAR, Janick ROHRBACH, Dave LUO, Manjunath PRASAD, Sascha FLEER, Jan Philip GÖPFERT, Akshat TANDON, Guillaume MOLLARD, Nikhil RAYAPROLU, Marcel SALATHE und Malte SCHILLING, 2020. Deep learning for understanding satellite imagery : an experimental survey. Frontiers in Artificial Intelligence. 2020. Bd. 3, Nr. 534696. DOI 10.3389/frai.2020.534696
Mohanty, Sharada Prasanna, Jakub Czakon, Kamil A. Kaczmarek, Andrzej Pyskir, Piotr Tarasiewicz, Saket Kunwar, Janick Rohrbach, et al. 2020. “Deep Learning for Understanding Satellite Imagery : An Experimental Survey.” Frontiers in Artificial Intelligence 3 (534696). https://doi.org/10.3389/frai.2020.534696.
Mohanty, Sharada Prasanna, et al. “Deep Learning for Understanding Satellite Imagery : An Experimental Survey.” Frontiers in Artificial Intelligence, vol. 3, no. 534696, 2020, https://doi.org/10.3389/frai.2020.534696.


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