BEGIN:VCALENDAR VERSION:2.0 PRODID:-//pretalx//talks.staging.osgeo.org//foss4g-it-2023//speaker//3MMS89 BEGIN:VTIMEZONE TZID:GMT BEGIN:STANDARD DTSTART:20001029T030000 RRULE:FREQ=YEARLY;BYDAY=-1SU;BYMONTH=10 TZNAME:GMT TZOFFSETFROM:+0100 TZOFFSETTO:+0000 END:STANDARD BEGIN:DAYLIGHT DTSTART:20000326T020000 RRULE:FREQ=YEARLY;BYDAY=-1SU;BYMONTH=3 TZNAME:BST TZOFFSETFROM:+0000 TZOFFSETTO:+0100 END:DAYLIGHT END:VTIMEZONE BEGIN:VEVENT UID:pretalx-foss4g-it-2023-URR3NB@talks.staging.osgeo.org DTSTART;TZID=GMT:20230612T143000 DTEND;TZID=GMT:20230612T144500 DESCRIPTION:Since the deployment of the first satellite equipped with a Syn thetic Aperture Radar (SAR) into orbit in 1978\, the use of SAR imagery ha s been a vital part of several scientific domains\, including environmenta l monitoring\, early warning systems\, and public safety.\nSAR could be de scribed as "non-literal imaging" since the raw data does not resemble an o ptical image and is incomprehensible to humans.\nFor this reason\, raw dat a is typically processed to create a Single Look Complex (SLC) image\, whi ch is a high-resolution image of the scene being observed. The processing of raw data to create a SLC image involves several steps\, including range compression\, Doppler centroid estimation and azimuth compression.\nProce ssing raw data requires a significant amount of computer power\; as a resu lt\, it is almost never practical to do it on board. As a direct consequen ce\, the data is transmitted back to Earth to be processed.\nThe objective of next-generation studies [1] is to optimize Earth Observation (EO) data processing and image creation in order to deliver EO products to the end user with very low latency using a combination of advancements in the on-b oard parts of the data chain.\nIn this work\, we focus on a sea scenario a nd propose to eliminate any pre-processing by training a Deep Convolutiona l Neural Network (DCNN) to directly recognize bright targets on raw data. \nThis indeed might substantially shorten the delivery time thus improving the efficiency of satellite-based maritime monitoring services.\nIn this regard\, the availability of training data represents one of the critical issues for the development of machine learning algorithms. In fact\, the e fficacy of the final machine learning-powered solution for a specific appl ication is ultimately determined by the quality and amount of the training data.\nHowever\, to date\, there are no training SAR raw data available i n scientific literature with regard to the specific topic of sea scenario monitoring. Furthermore\, their generation from real data is a time-consum ing task.\nIn this work we propose and investigate physically and statisti cally based approaches to simulate a marine scenario and generate realisti c synthetic training SAR raw datasets.\nWe then trained and evaluated a st ate-of-the-art DCNN on the generated synthetic dataset and successively on real raw data extracted from ERS imagery archive. It is one of the first\ nexperiments proposed in the SAR literature and results are quite encourag ing\, as they reveal that a well-trained DCNN can correctly recognize stro ng scattering objects on SAR raw data. \n\n[1] M. Kerr\, et al. “EO-ALER T: a novel architecture for the next generation of earth observation satel lites supporting rapid civil alerts”\, in 71st International Astronautic al Congress (IAC)\, 2020.\n\nAcknowledgments \nThis work was carried out i n the framework of the APP4AD project (“Advanced Payload data Processing for Autonomy & Decision”\, Bando ASI “Tecnologie Abilitanti Trasversa li”\, Codice Unico di Progetto F95F21000020005)\, funded by the Italian Space Agency (ASI). ERS data are provided by the European Space Agency (ES A). DTSTAMP:20260516T081444Z LOCATION:Sala Biblioteca @ PoliBa SUMMARY:Bright Target Detection on SAR Raw Data Based on Deep Convolutional Neural Networks - Giorgio Cascelli\, Alberto Morea\, Khalid Tijani\, Nico lò Ricciardi\, CATALDO GUARAGNELLA\, Raffaele Nutricato URL:https://talks.staging.osgeo.org/foss4g-it-2023/talk/URR3NB/ END:VEVENT BEGIN:VEVENT UID:pretalx-foss4g-it-2023-EHR9BQ@talks.staging.osgeo.org DTSTART;TZID=GMT:20230614T143000 DTEND;TZID=GMT:20230614T144500 DESCRIPTION:Multi-temporal SAR Interferometry (MTInSAR) techniques allow de tecting and monitoring millimetric displacements occurring on selected poi nt targets that exhibit coherent radar backscattering properties over time . Successful applications to different geophysical phenomena have been alr eady demonstrated in literature. New application opportunities have emerge d in the last years thanks to the greater data availability offered by rec ent launches of radar satellites\, and the improved capabilities of the ne w space radar sensors in terms of both resolution and revisit time. Curren tly\, different space-borne Synthetic Aperture Radar (SAR) missions are op erational\, e.g. the Italian COSMO-SkyMed (CSK) constellation and the Cope rnicus Sentinel-1 (S1) mission. \n\nEach CSK satellite is equipped with an X-band SAR sensor that acquires data with high spatial resolution (3x3 m2 )\, thus leading to a very high spatial density of the measurable targets and allowing the monitoring of very local scale events. Thanks to the nati onwide acquisition plan “MapItaly”\, CSK constellation covers the Ital ian territory with a best effort revisit time of 16 days since 2010. \n\n\ nS1 mission is instead operational since 2014 and acquires in C-band at me dium resolution (5x20 m2) with a minimum revisit time of 12 days (only 6 d ays between 2016 and 2021\, when the full S1 constellation was operational )\, thus allowing to monitor ground instabilities back in time almost all over the Earth. Moreover\, all data acquired by the S1 mission are provide d on an open and free basis by the European Space Agency (ESA) and the Eur opean Commission (EC)\, for promoting full utilization of S1 data\, with t he aim of increasing the scientific research\, growing the EO markets and fostering the development of continuous monitoring services\, such as the European Ground Motion Service (EGMS) and the Rheticus® Displacement Geo- information Service. \n\nThe EGMS is based on the MTInSAR analysis of S1 r adar images at full resolution\, updated annually\, and provides consisten t and reliable information regarding natural and anthropogenic ground moti on over the Copernicus Participating States and across national borders.\n \nRheticus® offers monthly updates of the millimetric displacements of th e ground surface\, through the MTInSAR processing chain based on the SPINU A© algorithm (“Stable Point Interferometry even in Un-urbanized Areas ”). Rheticus® is capable to process SAR images acquired by different SA R missions\, including CSK and S1. Thanks to the technological maturity as well as to the wide availability of SAR data\, these ground motion servic es can be used to support systems devoted to environmental monitoring and risk management. This work shows the results obtained in the framework of the SeVaRA project (“Environmental Risk Assessment Service”)\, coordin ated by Omnitech srl. The goal of SeVaRA is to implement an innovative sys tem for calculating an aggregate environmental risk index\, derived from s everal parameters related to hydrogeological instability phenomena and/or Weather-related extreme events. In particular\, the present work is focuse d on the analysis of the “Deformation Sub-System”\, that has been desi gned for the computation of risk indices related to structural and ground instabilities (landslides). The first step consists in the Hazard Map comp utation\, which requires the following input data:\n\n- Susceptibility Map (i.e.\, the European Landslide Susceptibility Map\, provided by the Joint Research Centre European Soil Data Centre)\n- National mosaic of landslid e hazard zones\, provided by ISPRA (River Basin Plans PAI)\n- Cumulated pr ecipitations (derived by cumulating ground measurement data collected by w eather stations\, if available\, or by interpolating hourly rainfall data provided by the Global Satellite Mapping of Precipitation service\, GSMaP\ , offered by the JAXA Global Rainfall Watch)\n- Land Cover Change (i.e.\, the CORINE Land Cover inventory)\n- Seismic events inventory\, provided by INGV\, to account for earthquake-induced landslides\n- MTInSAR ground dis placement time series.\n\nThe last input is essential for detecting instab le areas\, whose MTInSAR displacement trend exhibits a significant velocit y in the whole observation period and/or an acceleration in the acquisitio n dates of the last year. The SeVaRA “Deformation Sub-System” has been primarily designed to be interfaced with the Rheticus® Displacement Serv ice\, but it supports also products offered by the EGMS service as well as by other MTInSAR services available on the EO market. The final step cons ists in the computation of the landslide risk index\, obtained by combinin g the previous hazard index with the vulnerability and the exposure of the area of interest. The results of this study over specific areas of intere st will be presented and commented.\n\nAcknowledgments\n\nStudy carried ou t in the framework of the SeVaRA project\, funded by Apulia Region (PO FES R 2014/2020). DTSTAMP:20260516T081444Z LOCATION:Sala Biblioteca @ PoliBa SUMMARY:Exploitation of Multi-Temporal InSAR data for Environmental Risk As sessment Services - Davide Oscar Nitti\, Alberto Morea\, Khalid Tijani\, N icolò Ricciardi\, Fabio Bovenga\, Raffaele Nutricato URL:https://talks.staging.osgeo.org/foss4g-it-2023/talk/EHR9BQ/ END:VEVENT BEGIN:VEVENT UID:pretalx-foss4g-it-2023-W3NYLQ@talks.staging.osgeo.org DTSTART;TZID=GMT:20230614T151500 DTEND;TZID=GMT:20230614T153000 DESCRIPTION:This study presents a novel approach to monitor oil spills and ships using Synthetic Aperture Radar (SAR) raw data and deep learning tech niques. The proposed methodology involves several steps including pre-proc essing (focusing\, filtering and land sea mask)\, semantic segmentation\, and classification using a deep convolutional neural network (DCNN) model\ , as well as real-time (FFT-based) processing to ensure a fast response. \ n\nTo train the DCNN model\, the study combined three datasets: CleanSeaNe t\, TenGeoP-SARwv\, and GAP_OilSpill_DB. The first two datasets are public ly available\, while the third dataset was specifically built by the autho rs by integrating known and documented case studies from news articles and cases identified in the sea area in front of the port of Brindisi (Southe rn Italy)\, internally validated by expert GAP operators. \n\nData augmen tation techniques were also utilized to improve the model's performance by generating additional training data. The DCNN model uses DeepLab v3+ base d on ResNet-18 and is trained on a large dataset of SAR images that includ es various types of oil spills\, look-alikes\, novelty objects\, and ships . \n\nThe proposed system is optimized to process data on board the satell ite to ensure a real-time response. The system transmits images to the gro und segment only if there is an event of interest (e.g. a novelty object o r an oil spill detected eventually involving the nearest ships). \n\nThe study demonstrates that the proposed approach provides a promising solutio n for real-time monitoring of oil spills\, ships and novelty objects using satellite SAR raw data. The use of deep learning and data augmentation te chniques can significantly improve the accuracy and speed of detection\, w hich can ultimately lead to better environmental management and oil spill response. .Additionally\, the proposed approach can be applied to a variet y of SAR datasets and has the potential to be integrated with existing oil spill response systems. \n\nAcknowledgments  \n\nThis work was carried out in the framework of the APP4AD project (“Advanced Payload data Proc essing for Autonomy & Decision”\, Bando ASI “Tecnologie Abilitanti Tra sversali”\, Codice Unico di Progetto F95F21000020005)\, funded by the It alian Space Agency (ASI). ERS\, ENVISAT and Sentinel-1 data are provided b y the European Space Agency (ESA). DTSTAMP:20260516T081444Z LOCATION:Sala Biblioteca @ PoliBa SUMMARY:Real-Time Oil Spill Detection by Using SAR-Based Machine Learning T echniques - Davide Oscar Nitti\, Alberto Morea\, Khalid Tijani\, Nicolò R icciardi\, Raffaele Nutricato URL:https://talks.staging.osgeo.org/foss4g-it-2023/talk/W3NYLQ/ END:VEVENT END:VCALENDAR