Footprints (V2)
Retrieves footprints data based on batches of U.S. location inputs. The data extraction is footprint based, meaning it retrieves data on a specific requested building.
POST https://us-api.geox.ai/v2/footprints
Request Overview
- Batch Support: Process up to 500 locations in a single batch.
- Tracking: For each requested location, you can provide an
input_idfor tracking results. This field can be left empty if not needed. - Geocoding Confidence: V2 provides a
geocoding_confidencescore per building (typically 0.79-0.99).
Request Body
| Name | Type | Description |
|---|---|---|
locations | array[Location] | Required. List of up to 500 location objects. |
Location Object
| Field | Type | Description |
|---|---|---|
input_id | string | Optional tracking ID for the location. |
address | string | Address in free text format. |
coordinates | object | Lat/Lng object: {"lat": float, "lng": float}. |
Response Schema
The response includes batch processing metadata and a list of results. Each result item contains tracking information and the combined footprint/parcel data object.
{
"meta": { ...Meta Object },
"data": [
{
"input_id": "string",
"parcel_id": "string",
"confidence": float,
"footprint": { ...Footprint Object }
}
]
}Example Usage
Example Request
- cURL
- JavaScript
- Python
curl -X POST "https://us-api.geox.ai/v2/footprints" \
-H "Authorization: Bearer YOUR_TOKEN" \
-H "Content-Type: application/json" \
-d '{
"locations": [
{
"input_id": "req_1",
"address": "1600 Amphitheatre Parkway, Mountain View, CA"
}
]
}'
fetch('https://us-api.geox.ai/v2/footprints', {
method: 'POST',
headers: {
'Authorization': 'Bearer YOUR_TOKEN',
'Content-Type': 'application/json'
},
body: JSON.stringify({
locations: [
{ input_id: 'req_1', address: '1600 Amphitheatre Parkway, Mountain View, CA' }
]
})
})
.then(response => response.json())
.then(data => console.log(data));
import requests
url = "https://us-api.geox.ai/v2/footprints"
payload = {
"locations": [
{"input_id": "req_1", "address": "1600 Amphitheatre Parkway, Mountain View, CA"}
]
}
headers = {
"Authorization": "Bearer YOUR_TOKEN",
"Content-Type": "application/json"
}
response = requests.post(url, json=payload, headers=headers)
print(response.json())
Example Response
{
"data": [
{
"input_id": "1",
"parcel_id": "3300300021307",
"confidence": 0.99,
"footprint": {
"footprint_id": "33003000213071380000",
"center_long": -71.11866172659494,
"center_lat": 43.97804211973135,
"state": "New Hampshire",
"county": "Carroll",
"crs": 4326,
"footprint_area": 3809.29,
"occupancy": "Cell phone accessory store",
"land_use_description": "commercial",
"roof_type": "gable",
"roof_material": "metal",
"roof_condition": "fair",
"tarp_evidence": false,
"flat_area": 1645.9,
"hip_area": 446.38,
"building_height": 39.07,
"number_of_stories": 4,
"square_footage": 15237.16,
"tree_overhang_percent": 0,
"distance_to_tree": 6.46,
"image_date": "2024-06-25T00:00:00.000Z",
"model_run_date": "2024-12-08T00:00:00.000Z",
"pool_count": 0,
"pool_enclosure_count": 0,
"temporary_pool_count": 0,
"trampoline_count": 0,
"geometry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
}
}
],
"meta": {
"summary": {
"ok": 6,
"failed": 0
}
}
}