
XXIV ISPRS Congress
2021 Digital Edition
Nice, France, 05.07.2021 - 09.07.2021
Organizer: Société Française de Photogrammétrie et de Télédétection / Nicolas Paparoditis
Type of Event: Congress
Multi Media Type: Remote
Session: TH.2
Session : Unconventional applications for geospatial deep learning
Chair(s): MOLINIER Matthieu, TUIA Devis
Video of the whole session
Single Presentations

Deep learning for vessel detection and identification from spaceborne optical imagery
Corresponding paper: DEEP LEARNING FOR VESSEL DETECTION AND IDENTIFICATION FROM SPACEBORNE OPTICAL IMAGERY
Auhor(s): G. Matasci, J. Plante, K. Kasa, P. Mousavi, A. Stewart, A. Macdonald, A. Webster, and J. Busler
Volume: V-3-2021 / 2021
https://doi.org/10.5194/isprs-annals-V-3-2021-303-2021

End-to-end physics-informed representation learning from and for satellite ocean remote sensing data
Corresponding paper: END-TO-END PHYSICS-INFORMED REPRESENTATION LEARNING FOR SATELLITE OCEAN REMOTE SENSING DATA: APPLICATIONS TO SATELLITE ALTIMETRY AND SEA SURFACE CURRENTS
Auhor(s): R. Fablet, M. M. Amar, Q. Febvre, M. Beauchamp, and B. Chapron
Volume: V-3-2021 / 2021
https://doi.org/10.5194/isprs-annals-V-3-2021-295-2021

Deep no learning approach for unsupervised change detection in hyperspectral images
Corresponding paper: DEEP NO LEARNING APPROACH FOR UNSUPERVISED CHANGE DETECTION IN HYPERSPECTRAL IMAGES
Auhor(s): S. Saha, L. Kondmann, and X. X. Zhu
Volume: V-3-2021 / 2021
https://doi.org/10.5194/isprs-annals-V-3-2021-311-2021

JUngle-Net: using explainable machine learning to gain new insights into the appearance of wilderness in satellite imagery
Corresponding paper: JUNGLE-NET: USING EXPLAINABLE MACHINE LEARNING TO GAIN NEW INSIGHTS INTO THE APPEARANCE OF WILDERNESS IN SATELLITE IMAGERY
Auhor(s): T. Stomberg, I. Weber, M. Schmitt, and R. Roscher
Volume: V-3-2021 / 2021
https://doi.org/10.5194/isprs-annals-V-3-2021-317-2021