Tamil Nadu Water Body API: lakes, eris and tanks near a plot

Example

curl -X POST https://verified.realestate/api/v1/tools/water_body \
  -H "Authorization: Bearer $LANDLENS_KEY" \
  -H "Content-Type: application/json" \
  -d '{"lat":12.901,"lng":80.2279,"radius_m":1000}'

{
  "ok": true,
  "status": "ok",
  "data": {
    "found": true,
    "mode": "nearby",
    "radius_m": 1000,
    "count": 12,
    "nearest_waterway": {"name": "Buckingham Kaalvaai", "type": "canal", "distance_m": 1201, "nearest_lat": 12.9001903204401, "nearest_lng": 80.23893697277},
    "distance_basis": "the supplied point (the parcel centre), not the parcel boundary",
    "water_bodies": [
      {"id": 101830, "name": "Water Body #101830", "type": "Water Body", "distance_m": 307, "district": "Chennai", "centre_lat": 12.898548701677, "centre_lng": 80.2260463030045},
      {"id": 101815, "name": "Water Body #101815", "type": "Water Body", "distance_m": 442, "district": "Chennai", "centre_lat": 12.8964445484237, "centre_lng": 80.2251186072213}
    ],
    "extent_missing_for": 10,
    "names_are_classifications": ["Pennar Cauvery"]
  },
  "credits_charged": 1
}

A real answer from the API for a point in Sholinganallur, Chennai, trimmed to two of the ten water bodies listed, with the reading notes left out.

What it returns

One call. mode says which of the three answers you got.

Inputs

The JSON body of the POST is the arguments. Send lat and lng, or water_body_id, or district. An id wins over a point, and a point over a district.

Code

Set LANDLENS_KEY to your key. In an AI app, connect https://verified.realestate/mcp once and ask in plain words instead. In Postman, download this API's collection, import it (File, Import) and set apiKey.

curl -X POST https://verified.realestate/api/v1/tools/water_body \
  -H "Authorization: Bearer $LANDLENS_KEY" \
  -H "Content-Type: application/json" \
  -d '{"lat":12.901,"lng":80.2279,"radius_m":1000}'

import os, requests

r = requests.post(
    "https://verified.realestate/api/v1/tools/water_body",
    headers={"Authorization": f"Bearer {os.environ['LANDLENS_KEY']}"},
    json={
        "lat": 12.901,
        "lng": 80.2279,
        "radius_m": 1000
    },
    timeout=30,
)
body = r.json()
print(body["status"], body["credits_charged"], body["data"])

const res = await fetch("https://verified.realestate/api/v1/tools/water_body", {
  method: "POST",
  headers: {
    Authorization: `Bearer ${process.env.LANDLENS_KEY}`,
    "Content-Type": "application/json",
  },
  body: JSON.stringify({
    "lat": 12.901,
    "lng": 80.2279,
    "radius_m": 1000
  }),
});
const body = await res.json();
console.log(body.status, body.credits_charged, body.data);

Is there a lake or eri near my plot at 12.9010, 80.2279? How far is the nearest one, and how big is it?

Responses

Every answer has the same envelope. What status can say for this call:

Where the data comes from

Who uses it

Limits and good practice

• Distance here is from the point, while zone_check measures from the parcel boundary. The two numbers differ and both are right.
• Most records carry no extent, so area is often absent. It is never reported as zero.
• "Pennar Cauvery" and similar labels are river basin classifications. Describe such a row as a mapped water body, not by that name.
• Small channels and vaikkals are often missing from the polygon register.
• This is the map, not a verdict. For whether the plot sits in or within 15 m of a water body, use zone_check.

Pricing

Questions

Related APIs