Urban FloodingLand Use ChangeWetland Loss

Satellite Analysis for Bengaluru, India

Name: Satalyse
Author: Satalyse

Bengaluru has grown from a city of 1.6 million to over 12 million in four decades, consuming surrounding wetlands and agricultural land at a pace visible from orbit. The loss of those natural buffers has directly contributed to repeated urban flooding events, making change detection and risk scoring essential tools for city planners and infrastructure investors.

12.97°, 77.59°

Semi-arid tropical with bimodal rainfall (Jun–Sep and Oct–Dec), creating distinct wet and dry seasons that are clearly separable in NDVI time series.

What you can analyze here

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

Change Detection

POST /api/v1/satellite/detect-changes

Risk Scoring

POST /api/v1/satellite/score-risk

Anomaly Detection

POST /api/v1/satellite/detect-anomalies

Code example

This snippet is pre-filled with Bengaluru, India's coordinates and calls the Change Detection endpoint.

bengaluru_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            [77.56, 12.94],
11            [77.62, 12.94],
12            [77.62, 13],
13            [77.56, 13],
14            [77.56, 12.94]
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 Bengaluru, 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