BEGIN:VCALENDAR VERSION:2.0 PRODID:-//pretalx//talks.osgeo.org//foss4g-europe-2026//speaker//7EUFEJ 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-UY9NQK@talks.osgeo.org DTSTART;TZID=EET:20260630T150000 DTEND;TZID=EET:20260630T153000 DESCRIPTION:Water-quality monitoring increasingly relies on heterogeneous s ensing systems that combine in situ probes\, automated acquisition pipelin es\, interoperable web services\, and data-driven analysis. Open geospatia l standards such as the OGC SensorThings API [1] were developed to enable interoperable management of observations and metadata from heterogeneous s ensor systems\, while platforms such as istSOS4 [2] show how these princip les can be implemented in open-source environmental monitoring infrastruct ures. At the same time\, recent literature highlights the growing relevanc e of citizen science and IoT-based participatory sensing for water-quality monitoring [3]\, both to expand observation capacity and to strengthen co mmunication and public engagement around environmental data. In parallel\, machine-learning approaches for algal bloom detection and prediction [4] increasingly combine physicochemical measurements with image-based or remo tely sensed observations\, indicating the potential of AI-enabled optical monitoring for aquatic environments. However\, the integration of open sen sor standards\, participatory monitoring\, and future AI-derived optical o bservations within a single geospatial framework remains limited. This con tribution addresses that gap through the following research question: how can an open geospatial infrastructure based on istSOS4 support multimodal and participatory water monitoring today\, while also providing a coherent integration path for future edge AI-derived optical observations? \n\nThe work is developed within the Interreg WINCA4TI project\, Water Interactio ns with Nature\, Climate and Agriculture for Ticino\, which aims to analys e and describe the interactions between water\, economy\, environment\, an d agriculture in the Ticino basin. Within this broader framework\, SUPSI p romotes participatory environmental monitoring initiatives on Lake Lugano\ , combining scientific observation\, local collaboration\, and territorial awareness. The monitoring activity described in this paper is part of thi s effort. Through a collaboration based on citizen science principles\, a local nautical club hosts and helps maintain our sensor infrastructure\, w hile receiving in return water-quality information and analytics through d edicated dashboards \n\nThe current deployment on Lake Lugano consists of a multisensor platform combining conventional aquatic measurements with an optical experimental subsystem. At present\, the system acquires fluorime tric measurements and dissolved oxygen observations\, together with image data collected by an in-house developed three-camera optical device. These sensing components coexist within the same monitoring initiative\, but th ey do not yet operate within a fully unified observation model. The geospa tial backbone of the proposed framework is istSOS4\, which implements the OGC SensorThings API and provides a machine-readable\, discoverable\, and reusable way to organize and expose environmental observations\, metadata\ , and temporal series. Additionally\, within this project\, the current AP I is planned to be extended to support the STAplus standard\, in order to better address citizen science requirements related to data attribution\, storage\, and handling. Within this architecture\, conventional sensors su ch as fluorimeters and dissolved oxygen probes naturally fit the SensorThi ngs observation model. The more challenging issue concerns the optical sub system\, whose outputs differ substantially from scalar probe measurements . \n\nThe methodological choice proposed in this paper is therefore to dis tinguish between raw optical acquisition and published environmental obser vations. Raw imagery is not ingested directly into istSOS4\; image acquisi tion\, storage\, and processing instead remain outside the observation ser vice. Building on this distinction\, the paper proposes that the optical s ubsystem should evolve into an edge AI sensor. In this envisioned configur ation\, images would be processed locally through dedicated computer-visio n pipelines running close to the sensor. These models would transform raw visual input into higher-level variables that can be represented as time-s tamped observations\, such as algal classification\, estimated algal conce ntration\, bloom-related indicators\, anomaly flags\, and associated confi dence scores. Once formalized as observations with explicit timestamps\, o bserved properties\, and provenance\, these outputs could be published thr ough istSOS4 alongside the measurements acquired by conventional probes. \ n\nThe current results of the work are both practical and methodological. First\, the project has produced an operational multisensor deployment on Lake Lugano that already collects conventional water-quality measurements together with optical data from the three-camera system. Second\, the proj ect has led to the definition of an integration framework in which istSOS4 supports current probe-based observations and is designed to accommodate AI-derived optical indicators. \n\nThis contribution is relevant to the FO SS4G Europe Scientific Track because it addresses a concrete environmental -monitoring problem through a geospatial and standards-based approach\; it highlights the role of free and open source geospatial software as an ena bling infrastructure connecting sensors\, metadata\, interoperability\, an d downstream analytics\; and it brings together themes like GeoAI\, remote sensing for water resources management\, participatory monitoring\, and o pen geospatial infrastructures for environmental observation. The original ity of the work lies in defining how AI-derived optical indicators\, rathe r than raw imagery\, can be integrated into an istSOS4-based observation f ramework alongside conventional water-quality measurements within a partic ipatory monitoring setting. The framework shows how a standards-based open -source infrastructure can support current sensor observations while remai ning extensible toward future AI-enabled optical sensing. \n\nReproducibil ity is a key aspect of the framework\, which uses istSOS4 and the SensorTh ings API to support explicit sensor descriptions\, consistent observation structures\, timestamps\, and traceable data access. By separating acquisi tion\, storage\, inference\, feature extraction\, and publication\, the ar chitecture clarifies provenance and supports reusable environmental observ ations. Grounded in the Lake Lugano deployment within WINCA4TI / Interreg and supported by local stakeholders\, the work proposes a generalizable fr amework in which istSOS4 acts as the interoperable layer for conventional and future AI-derived environmental observations. \n\n \n\n(1) Open Geospa tial Consortium\, OGC SensorThings API Standard\, 2025. \n\n(2) M. Cannata \, M. Antonovic\, M. E. Molinari\, and M. Pozzoni\, “istSOS\, a new sens or observation management system: software architecture and a real-case ap plication for flood protection\,” ISPRS Archives\, 2013. \n\n(3) S. Blan co Ramírez\, I. van Meerveld\, and J. Seibert\, “Citizen science approa ches for water quality measurements\,” Science of the Total Environment\ , 2023. \n\n(4) J. Park\, K. Patel\, and W. H. Lee\, “Recent advances in algal bloom detection and prediction technology using machine learning\, ” Science of the Total Environment\, 2024. DTSTAMP:20260605T141410Z LOCATION:A01 SUMMARY:Integrating Participatory Water Monitoring and Edge AI Sensing thro ugh istSOS4: A Lake Lugano Case Study - alessandro centazzo URL:https://talks.osgeo.org/foss4g-europe-2026/talk/UY9NQK/ END:VEVENT END:VCALENDAR