Zanaga, D., Van De Kerchove, R., Daems, D., De Keersmaecker, W., Brockmann, C., Kirches, G., Wevers, J., Cartus, O., Santoro, M., Fritz, S., Lesiv, M., Herold, M., Tsendbazar, N.E., Xu, P., Ramoino, F., Arino, O., 2022. ESA WorldCover 10 m 2021 v200. https://doi.org/10.5281/zenodo.7254221

WorldCover provides the first global land cover products for 2020 and 2021 at 10 m resolution, developed and validated in near-real time based on Sentinel-1 and Sentinel-2 data.  A tremendous step forward towards the joint use of Sentinel satellite data for worldwide land cover mapping.

WorldCover

https://map.geo-wiki.org/geoserver/eshape/wms?service=WMS&version=1.1.0&request=GetMap&layers=eshape%3APhs_EShapeResults&bbox=-180.0%2C-90.0%2C180.0%2C90.0&width=768&height=384&srs=EPSG%3A4326&format=geojson

A mobile application in support of the Food Security & Sustainable Agriculture Showcase in e-Shape. This app is designed to gather crop species and management information in the field. It can be used to collect information on any crop species. Data collected via the app is immediately visible on the map and available for download.

CropObserve data

https://geoserver.iiasa.ac.at/geoserver/GeoWiki/wms?tiled=true

See, L., Georgieva, I., Duerauer, M., Kemper, T., Corbane, C., Maffenini, L., Gallego, J., Pesaresi, M., Sirbu, F., et al. (2022): A crowdsourced global data set for validating built-up surface layers. Scientific Data. https://doi.org/10.1038/s41597-021-01105-4

Data on built-up surfaces around the world were collected via a Geo-Wiki campaign, where this data set is intended for use as an independent validation of global maps of built-up surfaces.

https://pure.iiasa.ac.at/id/eprint/17534/



Built-up Surface

https://camtapi.picturepile.geo-wiki.org/log/map/geojson

https://www.camaliot.org/en/publications/

The Global Navigation Satellite System (GNSS) is a set of satellites that orbit the Earth. These GNSS satellites provide valuable information to many different location-based applications from the satnav in your car to flying a plane. But we can also use the data for scientific research. As the GNSS satellite signals are received in Earth, they are modified by the amount of water vapour in the atmosphere. We can use these modified signals in combination with machine learning algorithms to improve weather forecasting, in particular, of rainfall. By using crowd-sourced GNSS data from many mobile phones around the world, we hope to create better inputs to weather prediction models so that local rainfall events are forecast more accurately.


