Agricultural Understanding Platform

  • The Agricultural Understanding API provides agricultural data and insights for stakeholders in the sector.

  • Users can monitor landscape and crop types, along with sowing and harvest dates at the field level.

  • Geographic location can be used to retrieve crop or landscape data through the API.

The Agricultural Understanding Platform provides agricultural data and insights for stakeholders across the agricultural sector. This platform lets you identify landscape features and monitor crop type, and sowing and harvest dates at the individual field level for targeted decision-making and interventions.

Key capabilities

The platform provides the following key capabilities:

  • Agricultural Landscape Understanding (ALU): ALU segments the landscape into different elements to identify multiple classes, including individual field boundaries, trees, woodlands, and minor irrigation structures like farm wells and dug ponds.
  • Agricultural Monitoring and Event Detection (AMED): For landscape features corresponding to fields, AMED provides crop monitoring history at the feature level, indicating sowing and harvest times as well as the likely cultivated crop.

Data organization

To work with the data effectively, keep the following concepts in mind:

  • S2 cells: The platform organizes spatial data into level 13 S2 cells (approximately 1x1 km grids). The system assigns features to a cell based on the feature's centroid location.

  • Data coverage: Coverage varies by surface, with an initial focus on agricultural regions of the Global South. By default, the system filters out non-agricultural areas. For data access using the REST API, these areas can be included in queries.

  • Confidence levels: Confidence scores accompany feature geometries and crop predictions. Learn more in the data understanding guides for ALU and AMED.

  • Temporal snapshots: The system derives field boundaries and crop predictions from satellite imagery captured at specific times. You can query historical captures to observe year-over-year changes. See Retrieve historical captures for more details.

Choose your access mode

You can interact with Agricultural Understanding data through more than one platform. Select the modality that best fits your workflow:

  • REST API: Best for low-latency, rapid point-lookups for live mobile or web applications.
  • Google Earth: Best for immediate visual context, inspection, and spatial overlays without writing code.

Try it now

For an interactive example of querying and visualizing data using the REST API, see the Example Colab.

Research and methodology

To learn more about the models and methodology powering the platform:

  • For details on agricultural landscape understanding and panoptic segmentation, read the ALU research paper.
  • For details on crop monitoring and prediction approaches, read the AMED research paper.