Library for running label-maker as a dask job
This work was undertaken with support from Microsoft to be run on the Planetary Computer. With access to the Planetary Computer Hub, you can find an interactive notebook tutorial for running this library.
Instantiate a distributed dask cluster
from dask.distributed import Client
cluster = ...
client = Client(cluster)
Create a label maker job
from label_maker_dask import LabelMakerJob
lmj = LabelMakerJob(
zoom=13,
bounds=[-44.4836425781, -23.02665962797, -43.412719726, -22.5856399016],
classes=[
{ "name": "Roads", "filter": ["has", "highway"] },
{ "name": "Buildings", "filter": ["has", "building"] }
],
imagery="http://a.tiles.mapbox.com/v4/mapbox.satellite/{z}/{x}/{y}.jpg?access_token=ACCESS_TOKEN",
ml_type="segmentation",
label_source="https://qa-tiles-server-dev.ds.io/services/z17/tiles/{z}/{x}/{y}.pbf"
)
Build & execute the job
lmj.build_job()
lmj.execute_job()
View or otherwise use the results (by passing to a machine learning framework)
for result in lmj.results:
...