We extract POIs, road networks, building footprints, and administrative boundaries from OpenStreetMap. Delivered as clean GeoJSON, CSV, or Parquet to PostGIS, S3, or BigQuery on your cadence.
Structured, schema-consistent data across all major object types — delivered clean, typed, and ready to query.
Complete list of extractable fields for Points of Interest objects from openstreetmap.org. All fields typed and schema-versioned.
"element_id": 412398471, "element_type": "node", "amenity": "cafe", "name": "Blue Tokai Coffee Roasters", "lat": 12.934533, "lon": 77.626579, "opening_hours": "Mo-Su 08:00-23:00", "website": "https://bluetokaicoffee.com"
| # | element_id | element_type | amenity | name | lat | lon |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Road Networks objects from openstreetmap.org. All fields typed and schema-versioned.
"element_id": 23049182, "element_type": "way", "highway": "primary", "maxspeed": "60", "lanes": "4", "oneway": "yes", "surface": "asphalt", "length_m": 1450.5
| # | element_id | element_type | highway | ref | maxspeed | lanes |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Building Footprints objects from openstreetmap.org. All fields typed and schema-versioned.
"element_id": 84920193, "element_type": "way", "building": "commercial", "building_levels": "12", "addr_street": "100 Feet Road", "addr_city": "Bengaluru", "geometry_type": "Polygon"
| # | element_id | element_type | building | building_levels | height | addr_housenumber |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Admin Boundaries objects from openstreetmap.org. All fields typed and schema-versioned.
"element_id": 1948201, "element_type": "relation", "boundary": "administrative", "admin_level": "4", "name": "Karnataka", "population": "61095297", "geometry_type": "MultiPolygon"
| # | element_id | element_type | boundary | admin_level | name | population |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Changesets objects from openstreetmap.org. All fields typed and schema-versioned.
"changeset_id": 134920184, "user": "mapper_kiran", "uid": 492018, "created_at": "2026-05-12T09:14:00Z", "num_changes": 42, "comments": "Added new cafes in Indiranagar", "bbox_min_lat": 12.9301
| # | changeset_id | user | uid | created_at | closed_at | num_changes |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Our OpenStreetMap pipeline handles complex geometry resolution, tag normalisation, and spatial partitioning. We bypass Overpass API limits to deliver clean, queryable spatial formats directly to your database.
Extract amenities, shops, tourism sites, and historic markers with all associated tags and coordinates.
Define specific coordinate boxes or custom polygons for precise regional extraction.
Extract highway tags, speed limits, lane counts, and turn restrictions for routing engines.
Capture building polygons, height tags, and address data for urban planning models.
We handle distributed extraction without hitting public Overpass API rate limits or timeouts.
Track map edits, new nodes, and tag modifications in real time for specific regions.
Clean and standardise inconsistent user-generated tags into structured database columns.
Deliver geometries in native spatial formats ready for PostGIS or QGIS ingestion.
Run continuous pipelines to capture only new or modified elements since the last run.
Brief in. Clean data out.
Provide bounding boxes, custom polygons, or tag filters. We design the extraction schema together.
We configure distributed crawlers, spatial partitioning, and tag normalisation logic for OpenStreetMap.
Geometry validation, coordinate projection verification, and null-rate checks before launch.
GeoJSON, CSV, or Parquet pushed to your S3 bucket, PostGIS database, or BigQuery on agreed cadence.
Processing raw OSM data requires significant computational overhead. Here is how we manage complex spatial relationships and deliver queryable geometries.
Standard endpoints fail on country-scale extractions due to memory limits. We partition target areas into grid squares, process them in parallel, and merge the geometries.
OSM tags are free-text and highly inconsistent. Our pipeline maps variant keys to normalised columns, converting local conventions into a unified schema for downstream analysis.
OSM stores data as raw nodes. We automatically resolve node references into complete LineString and Polygon geometries, saving you the compute overhead of reconstructing shapes.
Public OSM APIs enforce strict rate limits and timeout constraints. We utilise a distributed crawler network and dedicated replica mirrors to extract heavy datasets without throttling.
For live routing applications, full re-downloads are inefficient. We consume and parse minutely replication diffs, pushing only modified nodes and ways to your database.
Logistics companies extract road networks, turn restrictions, and speed limits to build custom routing engines.
Retailers analyse foot traffic proxies by extracting competitor POIs, public transit stops, and residential density.
City planners use building footprints, land-use polygons, and green space data for infrastructure modelling.
Proptech firms correlate property values with proximity to amenities, transport hubs, and environmental features.
Data science teams train satellite imagery segmentation models using OSM building and road geometries as ground truth.
Analysts track the growth of specific retail chains or amenity types across different administrative regions.
"OpenStreetMap is the most comprehensive spatial database available, but transforming its raw node-way-relation structure into queryable geometries requires significant engineering overhead."
Most teams underestimate the complexity of OSM data. Resolving relations, handling inconsistent tagging, and managing spatial partitions at scale requires dedicated infrastructure. DataFlirt processes the raw XML/PBF streams and delivers clean, query-ready spatial formats so your GIS engineers can focus on analysis.
Everything supported by our openstreetmap.org scraper — rendered SPA elements, auth walls, rate-limit evasion and beyond.
Open-source tooling on proven cloud infra — no vendor lock-in, full observability.
We utilise PostGIS and GDAL for heavy geometry processing, resolving complex multipolygon relations and verifying spatial integrity before delivery.
For API-based extraction, we distribute requests across residential proxies to avoid rate limits, while heavy batch jobs process raw PBF files on high-memory ECS clusters.
Airflow orchestrates minutely or hourly diff processing, applying changesets directly to your target database to maintain a live replica of OSM.
Data delivered to where your team already works — no new tooling required.
About openstreetmap.org scraping, legality, and pipeline operations.
Ask us directly →Yes. OpenStreetMap data is open and licensed under the Open Data Commons Open Database License (ODbL). You are free to copy, distribute, transmit, and adapt the data, provided you attribute OpenStreetMap and its contributors.
Public Overpass API servers enforce strict rate limits, memory quotas, and timeouts. DataFlirt provides dedicated infrastructure, allowing you to extract massive datasets, entire countries, or complex relations without throttling.
Yes. We can establish a secure connection to your PostgreSQL/PostGIS instance and handle the geometry resolution, schema mapping, and data insertion automatically.
We apply custom normalisation rules during extraction. For example, standardising variations of opening hours, phone number formats, and address fields into clean, typed database columns.
We support minutely, hourly, or daily diff processing by consuming OSM replication files. This ensures your local database remains synchronised with the live map without full re-downloads.
Yes. You can provide custom GeoJSON polygons or bounding boxes, and we will configure the pipeline to extract only the nodes, ways, and relations that intersect your defined area.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a global POI dataset or live road network updates for a specific city, we scope, build, and operate the pipeline. Tell us what you need.
