BlondelEvoland 2 estΓ‘ a sΓ³lo 480 CLP en la Playstore.
EvoLandAs #EvoLand concludes, we thank everyone who engaged with us across this journey!
EvoLand leaves behind open assets for the #EarthΞbservation community:
π°οΈ Results Portal β interactive access to 12 prototypes: https://browser.dataspace.copernicus.eu/?zoom=5&lat=50.16282&lng=20.78613&themeId=EVOLAND-THEME&demSource3D=%22MAPZEN%22&cloudCoverage=30&dateMode=SINGLE&useEvoland=true
π₯ Final Webinar β project insights: https://www.evo-land.eu/discover-evolands-results-webinar-on-4-december-2025/
π» EvoLand GitHub Repository
Open-source software developed by the project: https://github.com/Evoland-Land-Monitoring-Evolution
π Publications & digital materials: https://www.evo-land.eu/newsroom/
π New open access publications from #EvoLand!
Our newsroom now features 4 newly added, peer-reviewed papers advancing research in #EarthΞbservation and land monitoring.
π Topics include:
β’ Hyperspectral foundation models at scale
β’ Multispectral-to-hyperspectral learning
β’ Multi-resolution satellite time-series fusion
β’ Cross-sensor super-resolution and distortions
π Explore all publications: evo-land.eu/newsroom
πΏ #EvoLand C3 candidate prototype provides consistent estimates of above-ground woody #biomass (AGB) and #forest canopy height (FCH) to complement the CLMS portfolio. It exploits new EO missions (e.g., GEDI) and applies parametric and non-parametric models for pixel-level AGB estimation, adaptable to stakeholder needs.
π To use the Results Portal and explore all that C3 has to offer head to our website https://www.evo-land.eu/results-portal/
ποΈ EvoLand C11 candidate prototype develops an on-demand land cover mapping pipeline via openEO, generating customised products based on user-defined parameters. This was demonstrated through a cloud-based inference pipeline of a 10m resolution forest management layer.
π Check out our website to see where the Results Portal can be accessed and dive into C11 in more detail https://www.evo-land.eu/results-portal/
π #EvoLand C10 candidate prototype develops continuous 10β―m land surface categories for trees, shrubs, herbaceous vegetation, bare, water, snow/ice, built-up, shadows and clouds complementing CLMS land cover products. Using Sentinel-2 data and AI-trained models, it aims to improve timeliness and the thematic flexibility of land cover maps.
π Discover access routes to the Results Portal on our website https://www.evo-land.eu/results-portal/
β³ One day to go!
Dive into #EvoLandResults tomorrow in our webinar: findings, methods, case studies + the Results Portal tour.
Sign up π https://us02web.zoom.us/webinar/register/WN_OoNkm412T6WhDCtIVPxkOA
π³ #EvoLand C2 candidate prototype maps forest disturbances continuously by leveraging synergies with the Continuous #Forest Monitoring service. Using AI/ML methods trained with in-situ disturbance data and time series from prototype C1, it identifies main disturbance agents (e.g., bark beetle infestations, wildfires, storms, logging).
π Explore how to access the Results Portal for detailed insights into C2 services here: https://www.evo-land.eu/results-portal/
π Join us to Discover EvoLandβs Results!
A deep dive into CLMS, #EvoLand prototypes, methods + live tutorials.
Featuring case studies on Forest, Land Cover, Urban & Water π
πDonβt miss it! https://www.evo-land.eu/discover-evolands-results-webinar-on-4-december-2025/
πΎ #EvoLand C4 candidate prototype maps cover crop types at 10β―m spatial resolution, complementing the VLCC service that currently detects cover crop presence only. Using new in-situ data and hierarchical deep learning models with Sentinel-1 and 2, C4 aims to provide detailed information on when & where different cover crops are grown.
π Ξore details on where to access the Results Portal for C4: https://www.evo-land.eu/results-portal/
