Cyclone Storm SurgeUrban FloodingCoastal Erosion

Satellite Analysis for Chennai, India

Name: Satalyse
Author: Satalyse

Chennai's catastrophic 2015 floods, which inundated 80% of the city and caused an estimated $3 billion in losses, exposed how dramatically urbanisation had destroyed natural drainage through the Adyar and Cooum river basins. Satellite flood extent mapping, coastal storm surge risk assessment, and monitoring of encroachment on flood-plain buffer zones are now central to the city's resilience agenda.

13.08°, 80.27°

Tropical semi-arid with the northeast monsoon (Oct–Dec) as the primary rainfall driver, unique among major Indian cities, creating a distinct storm flood window.

What you can analyze here

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

Risk Scoring

POST /api/v1/satellite/score-risk

Change Detection

POST /api/v1/satellite/detect-changes

Code example

This snippet is pre-filled with Chennai, India's coordinates and calls the Risk Scoring endpoint.

chennai_analysis.py

1"color:#c084fc">import requests
2
3response = requests.post(
4    "https://api.satalyse.dev/api/v1/satellite/score-risk",
5    headers={"Authorization": "Bearer sat_sk_live_..."},
6    json={
7        "geometry": {
8            "type": "Polygon",
9            "coordinates": [[
10            [80.24, 13.05],
11            [80.3, 13.05],
12            [80.3, 13.11],
13            [80.24, 13.11],
14            [80.24, 13.05]
15            ]]
16        },
17        "asset_type": "agricultural",
18        "location_context": "rural",
19        "risk_factors": ["flood"],
20        "time_horizon_days": 365
21    }
22)
23
24data = response.json()
25"color:#c084fc">print("color:#c084fc">f"Risk score:  {data['overall_risk_score']}/100")
26"color:#c084fc">print("color:#c084fc">f"Risk level:  {data['overall_risk_level']}")

Data sources used for Chennai, India

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