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.
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.
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:
More than 475 million out of 570 million farms in the world are smaller than 2 hectares. A substantial amount of them is situated in Africa where small-scale farming remains particularly common. Many of these farmers lack access to the information necessary to make sustainable farming decisions. This is true in part because most agricultural research publications don’t offer any applicable solutions or useful soil information for smallholders.
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.