Home Competitions/Awards Leonardo DiCaprio Foundation & Microsoft AI for Earth Innovation Grant 2019/2020

Leonardo DiCaprio Foundation & Microsoft AI for Earth Innovation Grant 2019/2020

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Deadline: 31 Jul,2019    Next term Starts:: 01 Jan,2020

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About

The Leonardo DiCaprio Foundation (LDF) aims to create a world where both nature and humanity coexist in harmony. Over the past twenty years, LDF has awarded over $100 million in grants, funding 200+ high-impact projects in 50 countries across Asia, the Americas, Africa, the Arctic, Antarctica, and all five oceans. LDF has achieved this success through active collaboration with a broad network of effective organizations to find and support the best, results-driven projects in the world’s most wild and threatened ecosystems.

Microsoft’s AI for Earth program supports organizations who are working to solve global environmental challenges using artificial intelligence. To further their missions, LDF and Microsoft are collaborating on the AI for Earth innovation grant to support applicants in creating and deploying open source machine learning models, algorithms, and data sets that directly tackle environmental problems faced by the world today.

With the latest IPCC Special Report outlining the level and urgency of broad action required to prevent global temperatures from exceeding 1.5 degrees, we would like to request a call for proposals that can deliver impactful solutions to immediate matters in the following four categories:

· Climate change
· Biodiversity conservation
· Agriculture
· Water

 

Eligibility and Criteria

  • All models supported through this grant must be open source, and grant recipients must be willing to share their models for use by other environmental researchers and innovators.
  • Grant recipients must make their solutions available for publication on the AI for Earth website through a designated open source license.
  • Grant recipients must make the training data on which their solutions are developed publicly available in standard digital format.
  • Grant recipients must include with their solutions a standard description template, including information on the machine learning data on which the solution was trained, summary statistics about solution performance, example use cases, and a disclaimer about the prototype nature of the solution.
  • Grant recipients must implement and deploy their solutions on Microsoft Azure.
  • Applicants must be nonprofit organizations or academic institutions. We recommend that the main applicant has a demonstrated background in environmental science and/or technology, and we require that at least one member of the team has strong enough technical skills (such as machine learning, statistical data analysis, scientific modelling, software development, and/or remote sensing) to complete the proposed project successfully.
  • We believe great ideas spring from a diversity of experiences and thus encourage applications from all over the world.

 

Awards and Benefits

  • The traction that the work will lead to the implementation of the proposed solution (whether directly by the applicant or in collaboration with others).
  • Solutions that are developed in and/or will be implemented in developing countries or underserved geographies.
  • Solutions that demonstrate the ability to rapidly scale and create lasting impact.
  • Proposal requests can be up to $100,000, for the support over one year only. In addition to financial support, successful proposals will receive free access to AI for Earth API’s, applications, tools, and tutorials, and support for their computational work on Microsoft Azure.
  • Applicants must demonstrate a plan for the continued support of the work beyond the grant period.
  • Applicants must consider how to utilize AI for global environmental impact in at least one of the following core categories:
  • Biodiversity conservation: Species are going extinct at alarming rates, and our planet’s oceans and last wild places need protection. We would like to see how AI can help, particularly in the following areas:
  • Protected area identification, management and restoration.
  • Sustainable supply chain management.
  • Illegal trade and poaching control Wildlife tracking and ecosystem health monitoring.
  • Realizing natural capital (including valuing natural capital, species identification).

Climate change: Extreme weather events, rising sea levels, higher global temperatures, and increased ocean acidity threaten human health, infrastructure, and the natural systems we rely on for life itself. We would like to see how AI can help, particularly in the following areas

· Climate resilience
· Extreme weather and climate modelling
· Measuring carbon sequestration through natural climate solutions
· Pollution monitoring and reporting on air quality
· More efficient energy and transportation systems

Agriculture: To feed the world’s rapidly growing population, farmers must produce more food on less arable land, and with lower environmental impact. We would like to see how AI can help, particularly in the following areas

· Sustainable land use planning and management
· Natural resource conservation
· Sustainable, closed-loop food systems
· Climate-resilient, regenerative agriculture
· Food recovery

Water: In the next two decades, demand for fresh water—for human consumption, agriculture, and hygiene, as well as for the well-being of natural systems and species—is predicted to dramatically outpace supply. We would like to see how AI can help, particularly in the following areas

· Beneficial water supply strategies for human and natural systems
· Water quality and sanitation
· Water source monitoring
· Water use efficiency
· Extreme-event (droughts, floods, disasters, etc.) water management

For more examples of projects, refer to Microsoft AI for Earth APIs and applications.

 

How To Apply

To Apply please click here

Applicants will be reviewed and awarded within 2-3 months of application close. It is anticipated that the selected projects will commence on January 1, 2020, and will be completed by December 31, 2020.