BEGIN:VCALENDAR VERSION:2.0 PRODID:-//pretalx//talks.staging.osgeo.org//foss4g-it-2023//speaker//DT79DH 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-ADRQMK@talks.staging.osgeo.org DTSTART;TZID=GMT:20230612T153000 DTEND;TZID=GMT:20230612T154500 DESCRIPTION:As humanity is entering the 4th Industrial Revolution\, marked by the digital transition\, the global demand for strategic minerals is qu ickly rising. Critical Raw Materials (CRM) are among those commodities whi ch are facing an increasing supply risk due to availability and political reasons. In order to increase EU's self-sufficiency in CRM\, there is a gr owing interest for the identification of mineral resources in Europe and f or the stipulation of acceptable trade agreements with diverse external su ppliers. With the Raw Materials Act\, the European Union commits to a sust ainable management of raw materials. This includes promoting sustainable m ining\, which undertakes to the minimization of social\, economic and envi ronmental impacts caused by resource extraction. It means also reducing mi ning rates\, in order to guarantee reserves for future generations. Despit e these stringent rules applied to the extractive industry\, the conversio n to more sustainable practices on a global scale is still slow\, and not all countries have translated the principles of sustainable mining to laws or are able to successfully enforce them. In this context\, thanks to the increasing availability of aerial and satellite data\, mineral and mine f acility mapping with optical images is quickly gaining ground. This techni que is a cost-effective\, non-invasive solution for supporting early-stage exploration and monitoring of extractive facilities. Here we show some ex amples of how Earth Observations can support the mining industry at differ ent phases of the supply chain. These applications use freely available mu lti-spectral satellite data\, such as Landsat and Sentinel-2 images\, as w ell as commercial high-resolution data\, such as Planet. The high temporal resolution\, as is the case of Planet and Sentinel-2 products\, and the l ong lifespan of Landsat data\, allow to effectively analyze the evolution of mine sites and their surroundings. The outcomes represent preliminary r esults focused on mineral characterization through band indexes and spectr al signature analyses\, and impact assessments on the nearby land associat ed with the extraction sites. The study aims at being a contribution to un derstanding the current relative standing of the mining sector in the achi evement of the sustainable mining targets. It shows\, on the one hand\, th at remote sensing is an innovative tool for identifying and characterizing new\, inaccessible resource deposits\; on the other\, that it is a suffic iently mature technology for measuring the social and environmental footpr int of the CRM market on a global scale. As illustrated in the Raw Materia ls Act\, Earth Observations are key to supporting different phases of mine rals’ value chain. These results and the related literature may be consi dered as a benchmark for future research in this domain.\nThis research is funded by the National Plan for Recovery and Resilience (PNRR) project G eosciencesIR. DTSTAMP:20260526T130955Z LOCATION:Sala Videoconferenza @ PoliBa SUMMARY:Earth Observations applied to Critical Raw Materials supply chain - Susanna Grita\, Piero Boccardo\, Vittoria Olgiati\, Alberta Pavone URL:https://talks.staging.osgeo.org/foss4g-it-2023/talk/ADRQMK/ END:VEVENT BEGIN:VEVENT UID:pretalx-foss4g-it-2023-SGWTNN@talks.staging.osgeo.org DTSTART;TZID=GMT:20230612T163000 DTEND;TZID=GMT:20230612T164500 DESCRIPTION:Glaciers are critical elements in the Earth’s climate system\ , and can be considered as sensitive indicators of climate change. Glacier s store significant amounts of freshwater\, which is essential for animal and human consumption and activities like industry and agriculture. Furthe rmore\, glaciers have a significant impact on the hydrological cycle\, and their melting also contributes to rising sea levels. Understanding and mo nitoring glacier extent changes is critical to informing climate policies\ , assessing natural hazards and safeguarding global water resources. Nowad ays\, remote sensing technology is a proved and widely adopted source of i nformation in this sense.\nIn this context\, the proposed study aims to de velop a regression model able to predict future changes in glacier extent\ , using supervised machine learning algorithms applied to open access medi um and HR spatial resolution satellite data of the EU Copernicus programme . To achieve this objective\, two machine learning models are developed. T he first model is a segmentation model that employs a U-Net architecture\, along with a final Conditional Random Field (CRF) module\, to digitalize glaciers features from satellite images. The purpose of the segmentation m odel is to vastly expand the dataset required by the regression model\, in terms of glacier surface values. In fact\, this work presents an addition al contribution in the form of a novel dataset consisting of time series o f glaciers and snow extent. This dataset is generated using the best-perfo rming segmentation model previously trained\, applied to multiple glaciers \, spanning a 30-year period and a consistent seasonal interval. To train the segmentation model\, and to create the required ground truth images\, the GLIMS initiative database is used again\, while optical satellite imag es are obtained in part from Sentinel-2 data and in part from other public ly available datasets such as the "Hindu Kush Himalayas (HKH) glacier mapp ing dataset". The latter couples annotated glacier locations\, which were produced by experts\, with multispectral imagery from Landsat 7.\nThe seco nd model is a multivariate regression model that seeks to identify the rel ationships between Land Surface Temperature (LST) and glacier/snow extent. \nIn order to train the models\, two datasets are required. For the regre ssion model and specifically LST\, data from the Sentinel-3 SLSTR instrume nt\, as well as data from the ESA Climate Change Initiative\, which consol idates data from various satellites over the past 25 years\, are utilized. Historical data on glacier extent and elevation is obtained from the "Gla ciers elevation and mass change data from 1850 to present from the Fluctua tions of Glaciers" database by the Copernicus Climate Change Service and d atasets provided by the Global Land Ice Measurements from Space (GLIMS) in itiative. Finally\, both models are validated on testing data to assess t heir generalization capabilities and their performance on real-world cases . A subset of the segmentation dataset is kept aside to extrapolate metric s such as the Intersection-Over-Union (IoU)\, which allows to assess the a ccuracy of the results obtained and to make comparison with other architec tures. For the regression model\, error metrics such as the Root-Mean Squa red Error (RMSE) are considered to assess the model performance. The resul ts of the study are expected to provide insights that will enhance the mon itoring efforts of glacial features and provide useful information about t he impact of climate change on glaciers worldwide. DTSTAMP:20260526T130955Z LOCATION:Sala Biblioteca @ PoliBa SUMMARY:Assessing Glacier Extent Changes through Machine Learning Algorithm s and Remote Sensing Data - Vanina Fissore\, Lorenza Ranaldi\, Davide Lisi \, Piero Boccardo\, Alessandro La Rocca\, Mirko Frigerio\, Daniele Sanmart ino URL:https://talks.staging.osgeo.org/foss4g-it-2023/talk/SGWTNN/ END:VEVENT END:VCALENDAR