ESA Φ-lab participates to ISPRS 2021 and IGARSS 2021
The International Society for Photogrammetry and Remote Sensing Congress, or ISPRS, and the International Geoscience and Remote Sensing Symposium, or IGARSS, are two of the main annual rendez-vous events for the entire Earth observation community. This year the ESA Φ-lab has a significant contribution to these events, taking part to sessions and scientific tracks and publishing several papers in both events.
The ISPRS Congress will be held virtually from 5 to 9 July.
The ESA Earth observation Programmes Directorate will be present with an online stand, a technology track titled “The ESA Earth Observation Platforms to leverage Open Science and Pioneer Innovative Applications” and two scientific tracks that will feature Φ-lab Research Fellows and Visiting Researchers:
- Monday 5 July, 10:00 – 10:50, Jamila Mifdal and Nicolas Longépé will be amongst the speakers in th session titled “Thematic Information Extraction” (MO.3.2:#620)
- Tuesday 6 July, 11:00 – 11:50, Dario Spiller will be amongst the speakers in the session titled “Hyperspectral Image Processing and Data Fusion” (TU.4.3: #539)
For more information: https://www.isprs2020-nice.com/programme/2021Digital_Program_06012021.pdf
The IGARSS event will be held from 12 to 16 July, also virtually.
The Directorate will be present with an online stand and some sessions, including one co-chaired by two ESA Φ-lab members, Nicolas Longépé and Bertrand Le Saux, titled “Physics-aware AI4EO and Simulation” on Friday 16 July, 14:40 – 16:10.
Ana Raquel Carmo and Nicolas Longépé will present their paper on “Deep Learning Approach For Tropical Cyclones Classification Based On C-Band Sentinel-1 Sar Images” on Friday 16 July, 8:30 – 8: 45, whereas other Φ-lab researchers, including James Wheeler, Dario Spiller and Alessandro Sebastianelli, also have successfully submitted accepted papers.
For more information: https://igarss2021.com/technical_program.php
Header image: Extreme weather events monitoring with AI4EO: tropical cyclone detection and categorization using deep neural networks. ©Raquel Carmo