Oriol Monserrat


Geomatics (GM)

Phd , Researcher (R3)

Phone: +34 93 645 29 00

Oriol Monserrat holds a PhD in aerospace science and technology from the Polytechnic University of Catalonia (2012) and a degree in mathematics from the University of Barcelona (2004). In 2003 he started working as a researcher in the Active Remote Sensing Unit of the Geomatics Institute. Since January 2014 works as a Head of the Remote Sensing Department of the Division of Geomatics at the Technological Centre of Telecommunications of Catalunya (CTTC-old.cttc.es) and as a Division Manager of the Geomatics division. Among others, his main tasks are assuring the fulfillment of the department objectives, giving technical and administrative support to the members of the group, giving support to the head of division in the elaboration of the strategic plan of the CTTC and on the day to day division functionality.

As a researcher, he is contributing actively to the lines of PSI/DInSAR by leading research activities and projects. He leads the Ground-Based SAR line of research. Finally, he also contributes to the RAR, Laser Scanner and Image classification lines. All these lines are part of the strategic plan of the CTTC. His main research activity is related to the analysis of satellite, airborne and terrestrial remote sensing data and the development of scientific and technical applications using mainly active sensors, such as the Synthetic Aperture Radar (SAR) radar, Real aperture Radar (RAR) and laser scanners. He has also experience with passive sensors. From the point of view of the application, Dr. Monserrat is specialized in the measurement and monitoring of deformations using SAR interferometry techniques (InSAR). In particular, he has a wide experience on the use of remote sensing data for geohazard management.  His most relevant research includes: (i) the development of algorithms and analysis methods for SAR interferometry, differential SAR interferometry (DInSAR) and terrestrial SAR; (ii) processing and analysis of remote sensing data; (iii) terrestrial remote sensing techniques: terrestrial laser scanner, SAR and RAR; iv) the measurement of deformations with Remote Sensing Data. This last aspect is directly related to applications related with natural hazards risk management, and in particular, with landslide and Subsidence hazards. (v) Generation of tools for post-processing data analysis and management.

In his research career he has participated in different projects (most of them related to Geohazards) of the Sixth and Seventh Framework Programs of the EU (Galahad, SubCoast, PanGeo and Aphorism) as well as H2020 (HEIMDALL, GYMS, U-Geohaz). He also participated in outstanding projects funded by the European Space Agency (PSIC4, Terrafirma and Respond) and in national projects of different ministries (Xlide, Lira, Mides, DEMOS or Criordesa). Since 2014, he has promoted over 40 projects and proposals. He has also experience on leading international projects as a CTTC-PI or as a Coordinator of the whole consortium (SAFETY, U-Geohaz, RISKCOAST). He is the co-owner of a patent (European patent, application nº 11382216.7) and has participated in the creation of the spin-off GEO-KINESIA as one of the PIs. He has also expended one year as invited researcher at the Geological Survey of Chile. During his stage the main goal was to analyze and test how to integrate remote sensing data on standard geohazard risk management procedures. During his stage he was granted with the Jose Castillejo grant for young doctors. He is listed as reviewer of the Agencia Estatal de Investigación and of the H2020 program and has participated as a proposal reviewer for the Italian Ministry of Education, Universities and Research (MIUR) and for the Helmholtz Association (Germany).

He has more than 120 scientific publications, of which 50 are in international indexed journals (ISI). Most of them Q1 journals. According to google scholar he has more than 2600 citations and a h-index of 25. In particular, one of his co-authored publications was within 1% of the most cited papers worldwide in its year of publication (WoS). He is board editor in the Remote Sensing in Geology, Geomorphology and Hydrology section of the Remote Sensing Journal. He has also more than 70 participations in international and national workshops and conferences. In the last years he has acted as keynote or invited speaker in different workshop sessions or events related to his field (XV-SBRS or SMPR 2015, GFZ weekly seminars). Moreover, he chaired and co-chaired different sessions in relevant conferences for his field like EGU (around 40 abstracts for each organized session) or Living planet Symposium (LPS19). In this last congress he co-organized a session with more than 100 abstract submission and with 5 oral session. He is part of the authors team of the second Report in the “Science for Disaster Risk Management” promoted by the EU thorough The Disaster Risk Management Knowledge Centre (DRMKC). Finally, he directed a doctoral thesis, has four more in progress and co-directed more than 10 master and degree thesis.

Scholar Google:https://scholar.google.es/citations?user=ZvQMNncAAAAJ&hl=es&oi=ao

ResearchGate: https://www.researchgate.net/profile/O_Monserrat

ORCID ID: https://orcid.org/0000-0003-2505-6855

Rapid Mapping of Landslides on SAR Data by Attention U-Net
Remote Sensing. Vol 14. No. 6. January 2022.
Nava, L, Bhuyan, K, Meena, SR, Monserrat, O, Catani, F
10.3390/rs14061449 Google Scholar
Spatio-Temporal Quality Indicators for Differential Interferometric Synthetic Aperture Radar Data
Remote Sensing. Vol 14. No. 3. January 2022.
Wassie Y., Mirmazloumi S.M., Crosetto M., Palamà R., Monserrat O., Crippa B.
10.3390/rs14030798 Google Scholar
Classification of ground deformation using sentinel-1 persistent scatterer interferometry time series
GISCI REMOTE SENS. Vol 59. No. 1. pp. 374-392 January 2022.
Mirmazloumi S.M., Wassie Y., Navarro J.A., Palamà R., Krishnakumar V., Barra A., Cuevas-González M., Crosetto M., Monserrat O.
10.1080/15481603.2022.2030535 Google Scholar
Improving landslide detection on SAR data through Deep Learning
Nava, Lorenzo, Monserrat, O., Catani, Filippo
10.1109/LGRS.2021.3127073 Google Scholar
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Researcher R2
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Researcher R3