%0 Journal Article %1 hoser2020introduction %A Höser, Thorsten %A Bachofer, Felix %A Künzer, Claudia %D 2020 %K article kuenzer lsfe %T An Introduction to Deep Learning with CNNs Applied on Earth Observation Data %U https://elib.dlr.de/136742/ %X Since 2012, Deep Learning with Convolutional Neural Networks (CNNs) is the state of the art approach for image recognition (IR), segmentation (IS) and object detection (OD). CNNs have mainly been developed in Computer Vision (CV). Here we introduce the evolution of CNNs in CV and its transition to Earth Observation (EO) applications, by reviewing CV and EO publications from 2012 to late 2019.

@article{hoser2020introduction, abstract = {Since 2012, Deep Learning with Convolutional Neural Networks (CNNs) is the state of the art approach for image recognition (IR), segmentation (IS) and object detection (OD). CNNs have mainly been developed in Computer Vision (CV). Here we introduce the evolution of CNNs in CV and its transition to Earth Observation (EO) applications, by reviewing CV and EO publications from 2012 to late 2019.}, added-at = {2020-11-27T10:17:20.000+0100}, author = {Höser, Thorsten and Bachofer, Felix and Künzer, Claudia}, biburl = {https://www.bibsonomy.org/bibtex/2c275cc843c24189b07fb9f9a6b2a712b/earthobs_uniwue}, description = {electronic library - An Introduction to Deep Learning with CNNs Applied on Earth Observation Data}, id = {https://elib.dlr.de/136742/1/2020-09-28_DLinEO_ESA_Phiweek_Hoeser.pdf, Höser, Thorsten und Bachofer, Felix und Künzer, Claudia (2020) An Introduction to Deep Learning with CNNs Applied on Earth Observation Data. ESA Phi-Week 2020, 28. Sep - 02. Okt 2020, Frascati, Italien.}, interhash = {840c007d25dc29af77c77a3b1c19f472}, intrahash = {c275cc843c24189b07fb9f9a6b2a712b}, keywords = {article kuenzer lsfe}, timestamp = {2020-11-27T10:17:38.000+0100}, title = {An Introduction to Deep Learning with CNNs Applied on Earth Observation Data}, type = {Konferenzbeitrag, NonPeerReviewed}, url = {https://elib.dlr.de/136742/}, year = 2020 }