BEGIN:VCALENDAR VERSION:2.0 PRODID:-//pretalx//talks.osgeo.org//foss4g-europe-2026//speaker//UBHA3R BEGIN:VTIMEZONE TZID:EET BEGIN:STANDARD DTSTART:20001029T050000 RRULE:FREQ=YEARLY;BYDAY=-1SU;BYMONTH=10 TZNAME:EET TZOFFSETFROM:+0300 TZOFFSETTO:+0200 END:STANDARD BEGIN:DAYLIGHT DTSTART:20000326T040000 RRULE:FREQ=YEARLY;BYDAY=-1SU;BYMONTH=3 TZNAME:EEST TZOFFSETFROM:+0200 TZOFFSETTO:+0300 END:DAYLIGHT END:VTIMEZONE BEGIN:VEVENT UID:pretalx-foss4g-europe-2026-MX3ZAN@talks.osgeo.org DTSTART;TZID=EET:20260630T123500 DTEND;TZID=EET:20260630T124000 DESCRIPTION:The increasing availability of geospatial data and the growing maturity of open-source technologies have created new opportunities for ad dressing complex challenges across domains such as maritime surveillance a nd urban mobility. However\, despite significant progress\, the geospatial community continues to face limitations in accessing high-quality\, inter operable\, multimodal\, and semantically enriched open datasets. This work addresses this gap by presenting four open-access geospatial datasets dev eloped within a unified vision of openness\, interoperability\, and reprod ucibility: two datasets targeting vessel monitoring and two focusing on ur ban mobility. These datasets are part of the MUltiSensor Inferred Trajecto ries (MUSIT) project\, an international\, interdisciplinary initiative fun ded by the European Union's Horizon Europe program. MUSIT aims to transfor m heterogeneous tracking sensor data into complete\, semantically enriched trajectories\, opening new perspectives in mobility monitoring and foster ing collaboration among academia\, industry\, and innovators.\nThe first d ataset\, namely Multimodal Maritime Dataset on the English Channel [1] (MM DEC)\, provides a comprehensive multi-source view of maritime activity wit hin a defined Area of Interest covering the western Celtic Sea\, the Engli sh Channel\, and part of the North Sea. Spanning a three-month period from July to October 2023\, MMDEC integrates heterogeneous data streams includ ing Automatic Identification System (AIS) signals\, satellite imagery\, me teorological and oceanographic data\, port locations\, and marine protecte d areas. By combining these diverse sources into a single\, harmonized dat aset\, MMDEC enables advanced analysis of maritime behavior\, anomaly dete ction\, and environmental monitoring. Its multi-layered structure reflects real-world operational complexity and supports a wide range of use cases\ , from maritime safety to ecological impact assessment. Within the MUSIT f ramework\, MMDEC represents a concrete realization of the project's data c ollection and integration pillar\, contributing a rich\, multi-sensor foun dation for subsequent trajectory reconstruction and analysis.\nComplementi ng this dataset\, AegeaNET [2] introduces a real-time dimension to maritim e monitoring through an open sensor network deployed across the Aegean Sea . AegeaNET comprises strategically positioned AIS and ADS-B receivers that capture maritime traffic\, providing continuous streams of positioning da ta to facilitate real-time tracking and situational awareness. As an acade mic and open initiative\, AegeaNET exemplifies how distributed\, community -driven sensor networks can enhance transparency and data availability in critical domains such as navigation safety and border monitoring. In align ment with MUSIT's core vision\, AegeaNET directly addresses the challenge of incomplete or fragmented tracking data by offering persistent\, sensor- based observations that feed trajectory inference and fusion pipelines. To gether\, MMDEC and AegeaNET demonstrate complementary approaches to mariti me data collection: one focused on multi-source historical integration\, a nd the other on real-time\, sensor-based observation.\nIn the domain of ur ban mobility\, we present two semantically enriched trajectory datasets ge nerated for the metropolitan areas of Paris and New York City [3]. The raw trajectory data underpinning both datasets consists of publicly available GPS traces voluntarily shared by users through OpenStreetMap\, retrieved via the OSM API over geographic bounding boxes covering each city. This ch oice of source ensures full openness and compliance with the Open Database License\, while avoiding the privacy issues that typically hinder the rel ease of mobility data. These trajectories are then semantically enriched w ith multiple contextual layers drawn from heterogeneous open sources. Spat ial context is provided through Points of Interest\, also extracted from O SM\, while weather conditions are integrated from meteorological data serv ices. Additional inferred attributes - including detected stops\, movement segments\, and transportation modes - are derived through spatio-temporal analysis of the raw GPS signal. A particularly novel contribution is the inclusion of synthetic yet realistic social media posts\, generated by a L arge Language Model carefully instructed to simulate user-generated conten t associated with observed movements. This multimodal enrichment opens new possibilities for research at the intersection of mobility analysis and n atural language processing. Consistent with MUSIT's emphasis on cross-doma in representation and information fusion\, the datasets are released in bo th tabular and Resource Description Framework formats\, supporting semanti c reasoning\, knowledge graph construction\, and compliance with the FAIR (Findable\, Accessible\, Interoperable\, Reusable) data principles. Togeth er\, these design choices make the datasets valuable resources for a wide range of tasks\, including behavior modeling\, mobility prediction\, and L LM-based applications.\nA key contribution of this work lies not only in t he datasets themselves but also in the reproducible and extensible process es used to generate them. By openly sharing both the data and the underlyi ng pipelines\, we aim to empower the community to replicate\, adapt\, and extend our approach to other geographic regions and application domains. T his is particularly important in the context of semantically enriched mobi lity data\, where the combination of heterogeneous contextual information remains a significant barrier to entry for many researchers and practition ers. The MUSIT project\, through its training and mobility programs and it s commitment to open knowledge exchange\, actively encourages reproducibil ity and community-driven engagement.\nFrom a broader perspective\, these f our datasets illustrate the potential of open geospatial data to bridge do main gaps and foster cross-disciplinary innovation. The maritime datasets highlight the importance of integrating heterogeneous environmental and op erational data sources\, while the urban mobility datasets demonstrate how semantic enrichment can unlock insights into human movement patterns. Bot h cases emphasize the role of open standards\, open-source tools\, and col laborative infrastructures in advancing the state of the art - values that are central to MUSIT's mission of building a dynamic community capable of turning research into tangible societal value.\nFinally\, this work align s closely with the principles of the open geospatial ecosystem by promotin g transparency\, accessibility\, and reuse. By contributing these datasets to the community under the MUSIT project\, we seek to support ongoing res earch\, policy-making\, and industry applications\, while also encouraging further contributions and collaborations within and beyond the consortium . DTSTAMP:20260604T221556Z LOCATION:A01 SUMMARY:Advancing Open Geospatial Data: Multi-Source Maritime Monitoring an d Semantically Enriched Urban Mobility Datasets - Ioannis Kontopoulos\, Je lena Panagiotakou URL:https://talks.osgeo.org/foss4g-europe-2026/talk/MX3ZAN/ END:VEVENT END:VCALENDAR