Environmental Sustainability Research
Microsoft Corporate Environmental Sustainability
Driving a more sustainable world through mission-driven scientific research and applied technology
项目
The Renewable Energy Mapping project uses geospatial machine learning to map and monitor renewable energy development at scale.
Our land cover mapping work uses computer vision to accelerate the process of turning remote sensing data into land use and land cover information, so that environmental scientists and geospatial analysts can spend less time drawing polygons, and more time…
Glacier mapping and glacial lakes monitoring uses a machine learning-based approach to support ecological monitoring.
In accordance with Microsoft’s pledge of reaching carbon negativity by 2030 and along with several promising avenues such as Energy 2.0 and agricultural carbon sequestration, we are actively contributing research towards enabling CCS as a viable and cost-efficient technique for…
Vasudha is a collection of projects that offer AI and IoT-enabled technology solutions to drive sustainability goals. This includes developing Vision AI algorithms for solar panel quality assurance or optimization and multi-agent simulations for renewable control decisions under uncertainties.
The Windflow project explores the research questions: Could airplanes in flight be employed as a vast sensor network to determine atmospheric conditions? Could data available today be used to infer winds on a large scale without special plane-based wind sensors…
成立:
Our goal is to enable data-driven farming. We believe that data, coupled with the farmer’s knowledge and intuition about his or her farm, can help increase farm productivity, and also help reduce costs. However, getting data from the farm is…
With Project Eclipse, the Urban Innovation Initiative presents a full stack -from sensors to analytics- air quality sensing platform for cities. The goal is a radical increase (10x – 100x) in the geographic granularity of environmental sensing in cities in…
Through our drive-by air pollution sensing research project, we are enabling cost-efficient collection of granular spatiotemporal pollution data through drive-by sensing.