Move to production faster with geospatial workflows

Join a growing community of leading drone and satellite imaging companies that use Labelbox to simplify and scale their AI toolchain, boost engineering productivity and deliver results to customers faster.

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Annotate tiled imagery

Drone maps and satellite imagery are often large and need to be processed into smaller chips for training models. With Labelbox, you can simplify this process by creating and storing annotations on your source data directly. Labelbox supports slippy maps (x,y,z) and WMS tile layers.

Distribute work to your internal team and outsourced services

Label data with internal and external teams simultaneously. Review annotations collaboratively. Keep track of activity and progress.

Ensure accurate and consistent training data

Catch bad labels in real time using the consensus system to automaticaly identify and correct inconsistent annotations. Set up review workflows to correct annotations and ensure accuracy of the training data.

A system of record for all of your training data

Search, browse and curate all of your training data in one place. Investigate bad or inconsistent labels. Improve your training data collaboratively.

Extensible and Developer Friendly

Set up ingest, ETL, and egress integrations between your ML pipeline and Labelbox. Connect your ML models to supercharge labeling productivity and orchestrate active learning. Labelbox is API-first so you can do just about anything!


from graphqlclient import GraphQLClient
client = GraphQLClient('https://api.labelbox.com/graphql')
client.inject_token('Bearer <API_KEY_HERE>')

data = client.execute('
  mutation {
    createDataRow(
      data: {
        rowData: "<DATA_THAT_NEEDS_TO_BE_LABELED>",
        dataset: {
          connect: {
            id: "<DATASET_ID_HERE>"
          }
        },
        organization: {
          connect: {
            id: "<INSERT_YOUR_ORGANIZATION_ID_FROM_ABOVE_HERE>"
          }
        },
      }
    )
  }
')
 

from graphqlclient import GraphQLClient
client = GraphQLClient('https://api.labelbox.com/graphql')
client.inject_token('Bearer <API_KEY_HERE>')

data = client.execute('
  mutation {
    createLabel(
      data: {
        label:"<INSERT_LABEL_DATA_STRING_HERE>",
        secondsToLabel: 42,
        dataRow: {
          connect: {
            id: "<INSERT_DATA_ROW_ID_HERE>"
          }
        },
        project: {
          connect: {
            id: "<INSERT_PROJECT_ID_HERE>"
          }
        },
        type: {
          connect: {
            name: "Any"
          }
        }
      })  {
        id
        label
    }}
')

from graphqlclient import GraphQLClient
client = GraphQLClient('https://api.labelbox.com/graphql')
client.inject_token('Bearer <API_KEY_HERE>')

data = client.execute('
  mutation{
    createPrediction(data:{
      label:"<should-be-the-exact-same-as-label.label>",
      predictionModelId:"<from-the-prediction-model-you-created-earlier>",
      projectId:"<any-project-id>",
      dataRowId:"<any-datarow-id>",
    }){
      id
    }
  }
')

from graphqlclient import GraphQLClient
client = GraphQLClient('https://api.labelbox.com/graphql')
client.inject_token('Bearer <API_KEY_HERE>')

data = client.execute('
  query {
    project(where:{id: "<INSERT_PROJECT_ID_HERE>"}) {
      labels(first: 5){
        id
        label
        createdBy{
          id
          email
        }
        type {
          id
          name
        }
        secondsToLabel
        agreement
        dataRow {
          id
          rowData
        }
      }
    }
  }
')

Customers ♥ Labelbox

Labelbox has become essential to our process. It is the beginning of every single deep learning exercise we do. The only thing that comes before Labelbox is recording the data.

Gal Ozery

AI Product Owner, Genius Sports

Labelbox is very well integrated as a part of our overall system design. Labelbox has helped us efficiently collect and analyze data through their strong infrastructure support. We’re able to get the labels we need for all different types of video and image analysis.

Stephen Krotosky

Manager, Applied Machine Learning

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