Detecting roads with RasterFlow

import wkls
import geopandas as gpd
import os

# Generate a geometry for Montgomery County, Maryland using Well-Known Locations (https://github.com/wherobots/wkls)
gdf = gpd.read_file(wkls.us.md.montgomerycounty.geojson())

gdf

from datetime import datetime

from rasterflow_remote import RasterflowClient

from rasterflow_remote.data_models import (
    ModelRecipes, 
    VectorizeMethodEnum
)

rf_client = RasterflowClient()

model_output_index = rf_client.predict_mosaic_recipe(
    # Area of Interest (AOI)
    aoi = gdf,

    # Date range for imagery to be used by the model
    start = datetime(2017, 1, 1),
    end = datetime(2018, 1, 1),

    # Coordinate Reference System EPSG code for the output mosaic   
    target_crs = 3857,

    # The model recipe to be used for inference (ChesapeakeRSC in this case)
    model_recipe = ModelRecipes.CHESAPEAKE_RSC
)

model_output_index.mosaic_index_gdf

# This example AOI has one geometry, so we select the first (only) mosaic location.
model_output_store = model_output_index.first_row_mosaic

model_output_store

# optimized_store = rf_client.build_zarr_multiscales(source_store=model_output_store)
# optimized_store

import xarray as xr
import s3fs
import zarr
# Determine the classes that are predicted by the model
fs = s3fs.S3FileSystem(profile="default", asynchronous=True)
zstore = zarr.storage.FsspecStore(fs, path=model_output_store[5:])
ds = xr.open_zarr(zstore)
model_features = ds['band'].data.tolist()

# Remove the 'background' class for vectorization
vector_features = [f for f in model_features if f != 'background']  

# Note: this should take about 5 minutes to complete
vectorized_results = rf_client.vectorize_mosaic(
        mosaic = model_output_store,
        features = vector_features,
        threshold = 0.5,
        vectorize_method = VectorizeMethodEnum.SEMANTIC_SEGMENTATION_RASTERIO,
        vectorize_config={"stats": True, "medial_skeletonize": False}
    )

print(vectorized_results)

from sedona.spark import *

config = SedonaContext.builder().getOrCreate()
sedona = SedonaContext.create(config)

sedona.sql("CREATE DATABASE IF NOT EXISTS examples_temp.chesapeake_db")

df = sedona.read.format("geoparquet").load(vectorized_results.uri)
df = df.withColumnRenamed("label", "layer")
df.writeTo("examples_temp.chesapeake_db.chesapeake_vectorized").createOrReplace()

from sedona.spark.sql.st_constructors import ST_MakeEnvelope
from sedona.spark.sql.st_predicates import ST_Intersects
import pyspark.sql.functions as f

# Compute a small bounding box around the center of the AOI
minx, miny, maxx, maxy = gdf.total_bounds
center_lat = (miny + maxy) / 2
center_lon = (minx + maxx) / 2
delta = 0.01

min_lon = center_lon - delta
max_lon = center_lon + delta
min_lat = center_lat - delta
max_lat = center_lat + delta

df = df.filter(
    ST_Intersects(f.col("geometry"), ST_MakeEnvelope(min_lon, min_lat, max_lon, max_lat))
).filter("score_mean > 0.7")

from sedona.spark.maps.SedonaKepler import SedonaKepler
map = SedonaKepler.create_map(df=df, name="Vectorized results")
map

from wherobots import vtiles

full_tiles_path = os.getenv("USER_S3_PATH") + "tiles.pmtiles"
vtiles.generate_pmtiles(df, full_tiles_path)

vtiles.show_pmtiles(full_tiles_path)

full_tiles_path