

large-scale geospatial datasets: SpaceNet satellite images and USGS 3DEP LiDAR.
Sagemaker spacenet how to#
Training data typeAPLSlengthAPLStime RGB images0.596240.54298LiDAR intensity0.578110.52697RGB+LiDAR merged 0.636510.58518 We demonstrate how to extract buildings and roads from two large-scale geospatial datasets hosted on the Registry of Open Data on AWS using a SageMaker notebook instance.īy using the LiDAR dataset from the Registry of Open Data on AWS and reproducing winning algorithms from SpaceNet building and road challenges, we show that you can use LiDAR data to perform the same task with similar accuracy, and even outperform the RGB models when combined. Extracting buildings and roads from AWS Open Data using Amazon SageMaker. After sending the message, SageMaker Pipelines waits for a response from the customer.

The message contains a SageMaker Pipelinesgenerated token and a customer-supplied list of input parameters.

You can use Jupyter notebooks in your notebook instance to prepare and process data, write code to train models, deploy models to SageMaker hosting, and test or validate your models.įrom left to right, the columns are RGB image, LiDAR elevation image, model prediction trained with RGB and LiDAR data, and ground truth building footprint mask.įrom left to right, the columns are RGB image, LiDAR reflectivity intensity image, model prediction trained with RGB and LiDAR data, and ground truth road mask. SageMaker Pipelines sends a message to a customer-specified Amazon Simple Queue Service (Amazon SQS) queue. From left to right, the columns are RGB image, LiDAR elevation image, model prediction trained with RGB and LiDAR data, and ground truth building footprint You can use Jupyter notebooks in your notebook instance to prepare and process data, write code to train models, deploy models to SageMaker hosting, and test or validate your models.
