Geospatial data presents tremendous opportunities for machine learning, from helping nonprofits and governments with urban planning and disaster relief to helping enterprises determine the economic value of a physical location. But organizations routinely fail to realize the full value of geospatial – in fact, only 26% of data strategy leaders say they are leveraging location intelligence to its full potential, according to Forrester. What’s holding them back? Data availability, interoperability and scalability play a role, as well as skill shortages that are only becoming more prominent in today’s economy. Machine learning can address business pain points and make workflows more efficient, when fueled by the right data. While data sourcing and preparation take time and energy, it can lead to a major payoff.
Agenda:
- Key use cases for ML on geospatial data
- Why organizations frequently fail to realize value
- Examples of how ML is being applied on geospatial data in practice