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Last active September 6, 2022 14:45
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Python script for downloading data from Mapillary API v4 in a bounding box
import mercantile, mapbox_vector_tile, requests, json
from import vt_bytes_to_geojson
# define an empty geojson as output
output= { "type": "FeatureCollection", "features": [] }
# vector tile endpoints -- change this in the API request to reference the correct endpoint
tile_points = 'mly_map_feature_point'
tile_traffic_signs = 'mly_map_feature_traffic_sign'
tile_coverage = 'mly1_public'
# tile layer depends which vector tile endpoints:
# 1. if map features or traffic signs, it will be "point" or "traffic_sign" respectively
# 2. if looking for coverage, it will be "image" for points, "sequence" for lines, or "overview" for far zoom
tile_layer = "point"
# Mapillary access token -- user should provide their own
access_token = 'MLY|XXX'
# a bounding box in [west_lng,_south_lat,east_lng,north_lat] format
west, south, east, north = [-80.13423442840576,25.77376933762778,-80.1264238357544,25.788608487732198]
# list of values to filter for and keep -- update this if changing to traffic signs
filter_values = ['object--support--utility-pole','object--street-light']
# get the list of tiles with x and y coordinates which intersect our bounding box
# MUST be at zoom level 14 where the data is available, other zooms currently not supported
tiles = list(mercantile.tiles(west, south, east, north, 14))
# loop through list of tiles to get tile z/x/y to plug in to Mapillary endpoints and make request
for tile in tiles:
tile_url = '{}/2/{}/{}/{}?access_token={}'.format(tile_points,tile.z,tile.x,tile.y,access_token)
response = requests.get(tile_url)
data = vt_bytes_to_geojson(response.content, tile.x, tile.y, tile.z,layer=tile_layer)
# filter for only features matching the values in filter list above
filtered_data = [feature for feature in data['features'] if feature['properties']['value'] in filter_values]
# check if features are inside bbox, and push to output geojson object if yes
for feature in filtered_data:
if (feature['geometry']['coordinates'][0] > east and feature['geometry']['coordinates'][0] < west)\
and (feature['geometry']['coordinates'][1] > south and feature['geometry']['coordinates'][1] < north):
# save a local geojson with the filtered data
with open('mydata.geojson', 'w') as f:
json.dump(output, f)
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