从OSM中提取矢量和光栅数据。
osmxtract的Python项目详细描述
OSMXtract
说明
osmxtract是一个简单的python包，它使用Overpass API来获取OpenStreetMap特性并将它们导出到geojson文件中。
安装
使用pip：
pip install osmxtract
命令行界面
使用量
osmxtract可以根据三个不同的选项猜测查询的范围：
--fromfile：使用输入矢量或光栅文件的边界；--latlon和--buffer：使用给定点周围缓冲区的边界；--address和--buffer：使用地理编码地址周围的缓冲区边界。
Usage: osmxtract [OPTIONS] OUTPUT
Extract GeoJSON features from OSM with the Overpass API.
Options:
--fromfile PATH Bounding box from input file.
--latlon FLOAT... Space-separated lat/lon coordinates.
--address TEXT Address to geocode.
--buffer INTEGER Buffer size in meters around lat/lon or
address.
--tag TEXT OSM tag of interest (ex: "highway").
--values TEXT Comma-separated list of possible values (ex:
"tertiary,primary").
--case-insensitive Make the first character of each value case
insensitive.
--geom [point|linestring|polygon|multipolygon]
Output geometry type.
--help Show this message and exit.
示例
# buildings around the "Université Libre de Bruxelles" as polygons# save features in the file `buildings.geojson`. since no values# are provided, all non-null values are accepted for the tag# "highway" are accepted. osmxtract --address "Université Libre de Bruxelles" --buffer 5000\ --tag building --geom polygon buildings.geojson # primary, secondary and tertiary roads based on the extent# of an existing raster. save the result as linestrings in the# `major_roads.geojson` file. we use the `--case-insensitive`# flag to get roads tagged as "primary" as well as "Primary". osmxtract --fromfile map.tif --tag highway \ --values "primary,secondary,tertiary"\ --case-insensitive --geom linestring \ major_roads.geojson # cafes and bars near "Atomium, Brussels" osmxtract --address "atomium, brussels" --buffer 1000\ --tag amenity --values "cafe,bar" --geom point \ cafes_and_bars.geojson
API
importjsonfromosmxtractimportoverpass,locationimportgeopandasasgpd# Get bounding box coordinates from a 2km buffer# around the Atomium in Brusselslat,lon=location.geocode('Atomium, Brussels')bounds=location.from_buffer(lat,lon,buffer_size=2000)# Build an overpass QL query and get the JSON responsequery=overpass.ql_query(bounds,tag='amenity',values=['cafe','bar'])response=overpass.request(query)# Process response manually...foreleminresponse['elements']:print(elem['tags'].get('name'))# Output:# Au Bon Coin# Aux 4 Coins du Monde# Excelsior# Welcome II# Heymbos# Games Café# Stadium# Le Beau Rivage# The Corner# None# Expo# Koning# Centrum# St. Amands# Bij Manu# ...or parse them as GeoJSONfeature_collection=overpass.as_geojson(response,'point')# Write as GeoJSONwithopen('cafes_and_bars.geojson','w')asf:json.dump(feature_collection,f)# To GeoPandas GeoDataFrame:geodataframe=gpd.GeoDataFrame.from_features(feature_collection)
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