Urban FloodingLandslide RiskForest Encroachment

Satellite Analysis for São Paulo, Brazil

Name: Satalyse
Author: Satalyse

São Paulo is Latin America's largest city and one of the densest metropolitan regions on Earth. Rapid peripheral expansion consumes Atlantic Forest remnants and drives urban flood risk, the region experiences severe flash flooding during summer rains, with damage that satellite-based before-and-after analysis can verify in hours rather than weeks.

-23.55°, -46.63°

Subtropical humid with a wetter summer (Nov–Mar) that concentrates flood events; urban heat island effect is measurable via satellite surface temperature differentials.

What you can analyze here

The Satalyse API covers every major satellite analysis type for São Paulo, Brazil. These endpoints are most relevant for this location:

Change Detection

POST /api/v1/satellite/detect-changes

Risk Scoring

POST /api/v1/satellite/score-risk

Code example

This snippet is pre-filled with São Paulo, Brazil's coordinates and calls the Change Detection endpoint.

sao-paulo_analysis.py

1"color:#c084fc">import requests
2
3response = requests.post(
4    "https://api.satalyse.dev/api/v1/satellite/detect-changes",
5    headers={"Authorization": "Bearer sat_sk_live_..."},
6    json={
7        "geometry": {
8            "type": "Polygon",
9            "coordinates": [[
10            [-46.66, -23.58],
11            [-46.6, -23.58],
12            [-46.6, -23.52],
13            [-46.66, -23.52],
14            [-46.66, -23.58]
15            ]]
16        },
17        "start_date": "2024-01-01",
18        "end_date":   "2025-01-01",
19        "sensitivity": "medium"
20    }
21)
22
23data = response.json()
24changes = data["changes_detected"]
25"color:#c084fc">print("color:#c084fc">f"Changed area: {changes['total_change_area_km2']} km²")
26"color:#c084fc">print("color:#c084fc">f"Change type:  {changes['change_type']}")
27"color:#c084fc">print("color:#c084fc">f"Confidence:   {changes['confidence_avg']:.0%}")

Data sources used for São Paulo, Brazil

Source	What it provides	Resolution
Sentinel-2	Multispectral optical imagery (NDVI, land cover)	10 m
Landsat 8	Long-term land surface reflectance and temperature	30 m
MODIS	Daily global coverage, fire, vegetation anomalies	250 m – 1 km
SRTM	Digital elevation model, terrain, slope, aspect	30 m
CHIRPS	Historical and near-real-time precipitation estimates	5 km
JRC Global Surface Water	Water presence history and seasonal flood extent	30 m