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OpenLandMap: using Machine Learning for global good

At OpenGeoHub and EnvirometriX, we recognize that machine learning, combined with AI, is a game-changer for both science and business. It's having a major impact on our daily lives in areas such as healthcare, security, web technology, self-driving vehicles, and many other fields. But it's still relatively under-used in areas such as landscape planning, food production and land restoration. Even the gaming industry has made more progress in machine learning than natural resource conservation.

MOOD - MOnitoring Outbreak events for Disease surveillance in a data science context

OpenGeoHub is proud to be part of the international consortium on the H2020 project MOnitoring Outbreak events for Disease surveillance in a data science context. H2020 Grant agreement ID: 874850. In these difficult times of the Coronavirus outbreak, we will be looking at user perspective and tracking and understanding outbreaks, predicting potential outbreaks that might happen as a result of climate change and loss of habitat for native species.

Geo-harmonizer project: making new seamless spatial layers for the continental EU

OpenGeoHub has started in 2019 an Innovation and Networks Executive Agency (INEA) co-funded project called "Geo-harmonizer: EU-wide automated mapping system for harmonization of Open Data based on FOSS4G and Machine Learning". The project homepage is https://opendatascience.eu. We have published in March 2020 the Implementation plan and are now working on new data-sets including:

Pre-release LandGIS

Making an OpenStreetMap-type data portal for land-related environmental data

The OpenGeoHub Foundation is pleased to announce the first release of LandGIS, a new webmapping system that aspires to be recognized as the "OpenStreetMap for land-related environmental data". LandGIS includes globally complete, fine spatial resolution (250 m to 1 km) datasets on relief, geology, land cover, land use, vegetation and land degradation indices, soil properties, soil classes and potential natural vegetation (see e.g.

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