# Photo Location Finder (`seemuapps/image-to-location`) Actor Identify the real-world location of any photo using AI — extract coordinates, country, city, landmarks, and reasoning from a single image. - **URL**: https://apify.com/seemuapps/image-to-location.md - **Developed by:** [Andrew](https://apify.com/seemuapps) (community) - **Categories:** Lead generation, Automation, Developer tools - **Stats:** 3 total users, 3 monthly users, 100.0% runs succeeded, NaN bookmarks - **User rating**: No ratings yet ## Pricing from $25.00 / 1,000 sucessful location founds This Actor is paid per event. You are not charged for the Apify platform usage, but only a fixed price for specific events. Learn more: https://docs.apify.com/platform/actors/running/actors-in-store#pay-per-event ## What's an Apify Actor? Actors are a software tools running on the Apify platform, for all kinds of web data extraction and automation use cases. In Batch mode, an Actor accepts a well-defined JSON input, performs an action which can take anything from a few seconds to a few hours, and optionally produces a well-defined JSON output, datasets with results, or files in key-value store. In Standby mode, an Actor provides a web server which can be used as a website, API, or an MCP server. Actors are written with capital "A". ## How to integrate an Actor? If asked about integration, you help developers integrate Actors into their projects. You adapt to their stack and deliver integrations that are safe, well-documented, and production-ready. The best way to integrate Actors is as follows. In JavaScript/TypeScript projects, use official [JavaScript/TypeScript client](https://docs.apify.com/api/client/js.md): ```bash npm install apify-client ``` In Python projects, use official [Python client library](https://docs.apify.com/api/client/python.md): ```bash pip install apify-client ``` In shell scripts, use [Apify CLI](https://docs.apify.com/cli/docs.md): ````bash # MacOS / Linux curl -fsSL https://apify.com/install-cli.sh | bash # Windows irm https://apify.com/install-cli.ps1 | iex ```bash In AI frameworks, you might use the [Apify MCP server](https://docs.apify.com/platform/integrations/mcp.md). If your project is in a different language, use the [REST API](https://docs.apify.com/api/v2.md). For usage examples, see the [API](#api) section below. For more details, see Apify documentation as [Markdown index](https://docs.apify.com/llms.txt) and [Markdown full-text](https://docs.apify.com/llms-full.txt). # README ## Photo Location Finder Find where any photo was taken — no EXIF GPS needed. Get the exact place name, latitude/longitude, country, region, city, recognized landmarks, and the visual clues that led to the answer. ### What you get - **locationName** — concise human-readable place ("Eiffel Tower from Pont de l'Alma, Paris, France") - **country**, **region**, **city** — administrative breakdown when known - **latitude**, **longitude** — best-guess coordinates of the camera position - **confidence** — `high` (specific spot), `medium` (city/region), or `low` (country/continent) - **landmarks** — array of recognized landmarks visible in the frame - **reasoning** — short explanation of the cues used (architecture, signage, vegetation, license plates, sun angle…) - **alternativeGuesses** — runner-up candidates with their own coordinates and confidence - **googleMapsUrl** — convenience link straight to the coordinates ### Use cases - **OSINT and journalism** — verify where a photo or video frame was taken - **Travel content** — auto-tag photo libraries with the exact location - **Real estate and insurance** — confirm a listing or claim photo's location - **Stock photo enrichment** — fill in missing geographic metadata at scale - **Investigative research** — pinpoint a scene from background details alone ### How to use 1. Upload one or more photos in the **Images** field (max 10 per run) 2. Run the actor — results appear in the **Dataset** tab, one record per image Images are processed in parallel, so 10 images take roughly the same wall time as 1. ### Output format One dataset record per image: ```json { "inputImageUrl": "https://api.apify.com/v2/key-value-stores/.../records/photo.jpg", "locationName": "Shibuya Center Gai, Tokyo, Japan", "country": "Japan", "region": "Tokyo", "city": "Tokyo", "latitude": 35.6598, "longitude": 139.6996, "confidence": "high", "landmarks": ["Shibuya Center Gai"], "reasoning": "The identification is confirmed by a prominent sign reading '渋谷センター街' (Shibuya Center-gai), a famous pedestrian shopping street in Tokyo. The dense neon signage, Japanese script, and crowded urban atmosphere are all characteristic of the Shibuya district.", "alternativeGuesses": [ { "locationName": "Kabukicho, Shinjuku, Tokyo, Japan", "latitude": 35.6941, "longitude": 139.7003, "confidence": "medium" }, { "locationName": "Dotonbori, Osaka, Japan", "latitude": 34.6687, "longitude": 135.501, "confidence": "medium" } ], "googleMapsUrl": "https://www.google.com/maps?q=35.6598,139.6996", "status": "success", "error": null } ```` ### Limits - Up to **10 images per run**. - Accuracy is highest for outdoor scenes with visible signage, recognizable architecture, or known landmarks. Indoor photos and featureless rural shots are hardest. - Performance is somewhat stronger for densely-photographed regions (North America, Europe, East Asia) than for less-photographed parts of the world. # Actor input Schema ## `imageFiles` (type: `array`): Upload up to 10 photos to geolocate. Each photo returns its own dataset record with location, coordinates, landmarks, and reasoning. ## Actor input object example ```json { "imageFiles": [ "https://upload.wikimedia.org/wikipedia/commons/thumb/a/a8/Tour_Eiffel_Wikimedia_Commons.jpg/640px-Tour_Eiffel_Wikimedia_Commons.jpg" ] } ``` # Actor output Schema ## `results` (type: `string`): One record per image: inputImageUrl, locationName, country, region, city, latitude, longitude, confidence, landmarks, reasoning, alternativeGuesses, googleMapsUrl, status (success|error), error. # API You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup. ## JavaScript example ```javascript import { ApifyClient } from 'apify-client'; // Initialize the ApifyClient with your Apify API token // Replace the '' with your token const client = new ApifyClient({ token: '', }); // Prepare Actor input const input = { "imageFiles": [ "https://upload.wikimedia.org/wikipedia/commons/thumb/a/a8/Tour_Eiffel_Wikimedia_Commons.jpg/640px-Tour_Eiffel_Wikimedia_Commons.jpg" ] }; // Run the Actor and wait for it to finish const run = await client.actor("seemuapps/image-to-location").call(input); // Fetch and print Actor results from the run's dataset (if any) console.log('Results from dataset'); console.log(`💾 Check your data here: https://console.apify.com/storage/datasets/${run.defaultDatasetId}`); const { items } = await client.dataset(run.defaultDatasetId).listItems(); items.forEach((item) => { console.dir(item); }); // 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/js/docs ``` ## Python example ```python from apify_client import ApifyClient # Initialize the ApifyClient with your Apify API token # Replace '' with your token. client = ApifyClient("") # Prepare the Actor input run_input = { "imageFiles": ["https://upload.wikimedia.org/wikipedia/commons/thumb/a/a8/Tour_Eiffel_Wikimedia_Commons.jpg/640px-Tour_Eiffel_Wikimedia_Commons.jpg"] } # Run the Actor and wait for it to finish run = client.actor("seemuapps/image-to-location").call(run_input=run_input) # Fetch and print Actor results from the run's dataset (if there are any) print("💾 Check your data here: https://console.apify.com/storage/datasets/" + run["defaultDatasetId"]) for item in client.dataset(run["defaultDatasetId"]).iterate_items(): print(item) # 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-start ``` ## CLI example ```bash echo '{ "imageFiles": [ "https://upload.wikimedia.org/wikipedia/commons/thumb/a/a8/Tour_Eiffel_Wikimedia_Commons.jpg/640px-Tour_Eiffel_Wikimedia_Commons.jpg" ] }' | apify call seemuapps/image-to-location --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=seemuapps/image-to-location", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Photo Location Finder", "description": "Identify the real-world location of any photo using AI — extract coordinates, country, city, landmarks, and reasoning from a single image.", "version": "1.0", "x-build-id": "Mb27P4UskHLD1PVg9" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/seemuapps~image-to-location/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-seemuapps-image-to-location", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for its completion, and returns Actor's dataset items in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/seemuapps~image-to-location/runs": { "post": { "operationId": "runs-sync-seemuapps-image-to-location", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK", "content": { "application/json": { "schema": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/seemuapps~image-to-location/run-sync": { "post": { "operationId": "run-sync-seemuapps-image-to-location", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } } }, "components": { "schemas": { "inputSchema": { "type": "object", "required": [ "imageFiles" ], "properties": { "imageFiles": { "title": "Images", "type": "array", "description": "Upload up to 10 photos to geolocate. Each photo returns its own dataset record with location, coordinates, landmarks, and reasoning.", "items": { "type": "string" } } } }, "runsResponseSchema": { "type": "object", "properties": { "data": { "type": "object", "properties": { "id": { "type": "string" }, "actId": { "type": "string" }, "userId": { "type": "string" }, "startedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "finishedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "status": { "type": "string", "example": "READY" }, "meta": { "type": "object", "properties": { "origin": { "type": "string", "example": "API" }, "userAgent": { "type": "string" } } }, "stats": { "type": "object", "properties": { "inputBodyLen": { "type": "integer", "example": 2000 }, "rebootCount": { "type": "integer", "example": 0 }, "restartCount": { "type": "integer", "example": 0 }, "resurrectCount": { "type": "integer", "example": 0 }, "computeUnits": { "type": "integer", "example": 0 } } }, "options": { "type": "object", "properties": { "build": { "type": "string", "example": "latest" }, "timeoutSecs": { "type": "integer", "example": 300 }, "memoryMbytes": { "type": "integer", "example": 1024 }, "diskMbytes": { "type": "integer", "example": 2048 } } }, "buildId": { "type": "string" }, "defaultKeyValueStoreId": { "type": "string" }, "defaultDatasetId": { "type": "string" }, "defaultRequestQueueId": { "type": "string" }, "buildNumber": { "type": "string", "example": "1.0.0" }, "containerUrl": { "type": "string" }, "usage": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "integer", "example": 1 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } }, "usageTotalUsd": { "type": "number", "example": 0.00005 }, "usageUsd": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "number", "example": 0.00005 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } } } } } } } } } ```