Government / Infrastructure

Urban Expansion Tracking

Name: Satalyse
Author: Satalyse

Track how cities grow, quarter by quarter, kilometre by kilometre.

10 m

Spatial resolution

1 month

Minimum time window

~500 km² per call

Max polygon area

Urban, agricultural, vegetation, water

Change types detected

The Problem

City planners, infrastructure developers, and real estate investors need accurate, timely data on urban expansion to inform zoning decisions, infrastructure investment, and environmental impact assessments. Current cadastral surveys update infrequently, and ground-based monitoring of large metro areas is impractical. Unofficial construction and informal settlement growth often goes untracked for years.

How Satalyse Solves It

Satalyse's Change Detection endpoint identifies land-cover transitions, vegetation to impervious surface, agricultural land to residential, bare ground to structure, at 10-metre resolution between any two dates. Run quarterly scans over a city boundary polygon to build a timeline of growth vectors. Temporal Analysis completes the picture with annualized expansion rate trends and projected growth trajectories.

Change Detection

POST /api/v1/satellite/detect-changes

Detects new construction, paved surfaces, and land-cover transitions across city extents

Temporal Analysis

POST /api/v1/satellite/analyze-temporal-trends

Quantifies annualized urban expansion rates and identifies growth direction patterns

Code Example

urban-expansion-tracking.py

REQUEST

1"color:#c084fc">import requests
2
3"color:#4b5563"># Track urban growth "color:#c084fc">in Bangalore's northern corridor (2022 → 2024)
4response = requests.post(
5    "https://api.satalyse.dev/api/v1/satellite/detect-changes",
6    headers={"Authorization": "Bearer sat_sk_live_..."},
7    json={
8        "geometry": {
9            "type": "Polygon",
10            "coord">inates": [[
11                [77.520, 13.121], [77.612, 13.121],
12                [77.612, 13.058], [77.520, 13.058],
13                [77.520, 13.121]
14            ]]
15        },
16        "start_date": "2022-01-01",
17        "end_date":   "2024-01-01",
18        "sensitivity": "medium"
19    }
20)
21
22data = response.json()
23changes = data["changes_detected"]
24"color:#c084fc">print(f"New urban area: {changes['total_change_area_km2']:.2f} km²")
25"color:#c084fc">print(f"Growth rate:    {changes['percent_of_total_area']:.1f}% of corridor")
26"color:#c084fc">print(f"Primary change: {changes['change_type']}")

200 OKRESPONSE

{
  "request_id": "req_urban001",
  "status": "success",
  "timestamp": "2025-01-15T12:00:00Z",
  "location": { "area_km2": 36.8, "bbox": { "min_lon": 121.43, "min_lat": 31.14, "max_lon": 121.53, "max_lat": 31.24 } },
  "changes_detected": {
    "total_change_area_km2": 4.71,
    "percent_of_total_area": 12.8,
    "change_magnitude_avg": 25,
    "confidence_avg": 0.91,
    "change_type": "urban_development"
  },
  "change_geometry": {
    "changed_polygons": [
      {
        "id": "poly_1",
        "type": "Polygon",
        "coordinates": [[[...]]],
        "change_type": "urban_development",
        "change_magnitude": 25,
        "confidence": 0.91
      }
    ]
  },
  "api_metadata": { "version": "2.0", "execution_time_ms": 2450, "cost_credits": 16, "cache_hit": false }
}