# Construction Incident Photo Sorter & Claims Review Queue (`armourylabs/construction-incident-photo-classifier`) Actor Organises construction incident photo batches for insurance claims. Classifies each photo by damage type and location zone from its metadata, assigns reviewer priority, and outputs a ranked human-review queue. Metadata triage only — no damage or liability assessments are made. - **URL**: https://apify.com/armourylabs/construction-incident-photo-classifier.md - **Developed by:** [Christopher Smith](https://apify.com/armourylabs) (community) - **Categories:** Automation, Developer tools, Other - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing from $30.00 / 1,000 photo classifieds 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/docs.md): ```bash npm install apify-client ``` In Python projects, use official [Python client library](https://docs.apify.com/api/client/python/docs.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/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 ## Construction Incident Photo Sorter & Claims Review Queue Turn hundreds of unorganised construction incident photos into a **ranked, structured review queue** for insurance claims — sorted by damage type, location zone, and reviewer priority, with the highest-risk photos on top. Built for insurance claims adjusters, self-insured construction firms, and loss-adjustment teams who receive multi-hundred-photo batches per incident and sort them by hand. ### What does the Incident Photo Sorter do? 1. **Classifies each photo** into one of eight damage types — structural collapse, fire, water, weather, equipment, surface, fall debris, electrical — using its filename, description, tags, and notes (metadata, not pixels). 2. **Identifies the location zone** — roof, foundation, facade, interior, excavation, elevated works, and more. 3. **Assigns reviewer priority** (high / medium / low) from damage severity and urgency keywords. 4. **Groups photos** by damage type, zone, and priority for instant drill-down. 5. **Produces a ranked review queue** so the highest-risk photos reach a human reviewer first. **All outputs are metadata triage only.** Every damage assessment must be confirmed by a qualified assessor. ### How do I sort incident photos with it? 1. Provide the photo metadata list (filename, description, optional tags/notes) and a `claim_id`. 2. Choose an `output_format`: `full`, `queue_only`, or `summary_only`. 3. Run. Every photo classified becomes one dataset row; your chosen output shape lands in the run's `OUTPUT` key-value record. ### Input example ```json { "claim_id": "CLM-2026-001", "photos": [ {"filename": "collapse_north.jpg", "description": "roof truss collapsed after storm, severe damage"}, {"filename": "flood_basement.jpg", "description": "flooding in basement, burst pipe"} ] } ```` ### Output example (one dataset row per photo) ```json { "photo_id": "collapse_north", "filename": "collapse_north.jpg", "damage_type": "structural_collapse", "location_zone": "roof", "reviewer_priority": "high" } ``` ### Pricing (pay-per-event) | Event | Price | |---|---| | Actor start | USD $0.10 per run | | Photo classified | USD $0.03 per photo (one dataset row each) | You pay only for photos actually classified. A small builder's weekly incident batch of ~100 photos works out to roughly **USD $13/month**; a 1,000-photo loss-adjustment batch costs $30.10. No subscription. ### FAQ **Does it analyse the actual images?** No — it classifies from photo **metadata** (filename, description, tags, notes). That makes it fast, cheap, and private: no images ever leave your storage. **Is this AI image recognition?** No. It is deterministic keyword and rule matching — auditable and repeatable, which matters in a claims file. **Does it decide liability or WHS compliance?** No. This tool organises photos for human review only. No damage assessments, WHS compliance judgements, or liability determinations are made by the software. All findings must be confirmed by a qualified assessor before any claim decision is taken. **What if a photo has no useful metadata?** It still gets classified (to the closest match or a low-confidence default) and counted, so nothing silently disappears from the batch. ### Support & feedback Spotted a damage type or zone the rules miss? Open an issue on this Actor's **Issues** tab — rule additions are cheap and ship fast. # Actor input Schema ## `photos` (type: `array`): List of photo objects to classify. Each photo should include a filename and ideally a description of what the photo shows. ## `claim_id` (type: `string`): Your internal reference number for the claim or incident batch (e.g. CLM-2024-001). Used only as a label in the output. ## `output_format` (type: `string`): Controls the detail level of the output. 'full' returns all classified photos, groupings, review queue and summary. 'queue\_only' returns only the ranked review queue. 'summary\_only' returns groupings and summary counts. ## Actor input object example ```json { "photos": [ { "filename": "DEMO_SAMPLE_do_not_use__north_wall_collapse.jpg", "description": "DEMO SAMPLE - automated-test data, NOT a real incident. Roof truss collapsed onto the north wall after a storm.", "tags": [ "collapse", "structural", "DEMO" ] }, { "filename": "DEMO_SAMPLE_do_not_use__level2_water_leak.jpg", "description": "DEMO SAMPLE - automated-test data, NOT a real incident. Burst pipe flooding the level 2 corridor, saturated ceiling.", "tags": [ "water", "leak", "DEMO" ] }, { "filename": "DEMO_SAMPLE_do_not_use__plantroom_fire.jpg", "description": "DEMO SAMPLE - automated-test data, NOT a real incident. Charred electrical panel and smoke damage in the plant room.", "tags": [ "fire", "smoke", "DEMO" ] } ], "claim_id": "DEMO-SAMPLE-not-a-real-claim", "output_format": "full" } ``` # 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 = { "photos": [ { "filename": "DEMO_SAMPLE_do_not_use__north_wall_collapse.jpg", "description": "DEMO SAMPLE - automated-test data, NOT a real incident. Roof truss collapsed onto the north wall after a storm.", "tags": [ "collapse", "structural", "DEMO" ] }, { "filename": "DEMO_SAMPLE_do_not_use__level2_water_leak.jpg", "description": "DEMO SAMPLE - automated-test data, NOT a real incident. Burst pipe flooding the level 2 corridor, saturated ceiling.", "tags": [ "water", "leak", "DEMO" ] }, { "filename": "DEMO_SAMPLE_do_not_use__plantroom_fire.jpg", "description": "DEMO SAMPLE - automated-test data, NOT a real incident. Charred electrical panel and smoke damage in the plant room.", "tags": [ "fire", "smoke", "DEMO" ] } ], "claim_id": "DEMO-SAMPLE-not-a-real-claim" }; // Run the Actor and wait for it to finish const run = await client.actor("armourylabs/construction-incident-photo-classifier").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 = { "photos": [ { "filename": "DEMO_SAMPLE_do_not_use__north_wall_collapse.jpg", "description": "DEMO SAMPLE - automated-test data, NOT a real incident. Roof truss collapsed onto the north wall after a storm.", "tags": [ "collapse", "structural", "DEMO", ], }, { "filename": "DEMO_SAMPLE_do_not_use__level2_water_leak.jpg", "description": "DEMO SAMPLE - automated-test data, NOT a real incident. Burst pipe flooding the level 2 corridor, saturated ceiling.", "tags": [ "water", "leak", "DEMO", ], }, { "filename": "DEMO_SAMPLE_do_not_use__plantroom_fire.jpg", "description": "DEMO SAMPLE - automated-test data, NOT a real incident. Charred electrical panel and smoke damage in the plant room.", "tags": [ "fire", "smoke", "DEMO", ], }, ], "claim_id": "DEMO-SAMPLE-not-a-real-claim", } # Run the Actor and wait for it to finish run = client.actor("armourylabs/construction-incident-photo-classifier").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 '{ "photos": [ { "filename": "DEMO_SAMPLE_do_not_use__north_wall_collapse.jpg", "description": "DEMO SAMPLE - automated-test data, NOT a real incident. Roof truss collapsed onto the north wall after a storm.", "tags": [ "collapse", "structural", "DEMO" ] }, { "filename": "DEMO_SAMPLE_do_not_use__level2_water_leak.jpg", "description": "DEMO SAMPLE - automated-test data, NOT a real incident. Burst pipe flooding the level 2 corridor, saturated ceiling.", "tags": [ "water", "leak", "DEMO" ] }, { "filename": "DEMO_SAMPLE_do_not_use__plantroom_fire.jpg", "description": "DEMO SAMPLE - automated-test data, NOT a real incident. Charred electrical panel and smoke damage in the plant room.", "tags": [ "fire", "smoke", "DEMO" ] } ], "claim_id": "DEMO-SAMPLE-not-a-real-claim" }' | apify call armourylabs/construction-incident-photo-classifier --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=armourylabs/construction-incident-photo-classifier", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Construction Incident Photo Sorter & Claims Review Queue", "description": "Organises construction incident photo batches for insurance claims. Classifies each photo by damage type and location zone from its metadata, assigns reviewer priority, and outputs a ranked human-review queue. Metadata triage only — no damage or liability assessments are made.", "version": "0.1", "x-build-id": "w3fA1jgo3PZggm5Up" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/armourylabs~construction-incident-photo-classifier/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-armourylabs-construction-incident-photo-classifier", "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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/armourylabs~construction-incident-photo-classifier/runs": { "post": { "operationId": "runs-sync-armourylabs-construction-incident-photo-classifier", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/armourylabs~construction-incident-photo-classifier/run-sync": { "post": { "operationId": "run-sync-armourylabs-construction-incident-photo-classifier", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": [ "photos" ], "properties": { "photos": { "title": "Photos", "type": "array", "description": "List of photo objects to classify. Each photo should include a filename and ideally a description of what the photo shows.", "items": { "type": "object", "properties": { "filename": { "title": "Filename", "description": "The photo filename (e.g. collapse_north.jpg). Used as the unique identifier.", "type": "string" }, "description": { "title": "Description", "description": "Free-text description of what the photo shows. The more detail, the more accurate the classification.", "type": "string" }, "tags": { "title": "Tags", "description": "Short keyword tags to supplement the description (e.g. [\"fire\", \"level 3\"]).", "type": "array", "items": { "type": "string" } }, "notes": { "title": "Adjuster Notes", "description": "Any additional notes recorded by the adjuster at the time of photo capture.", "type": "string" } }, "required": [ "filename" ] } }, "claim_id": { "title": "Claim ID", "type": "string", "description": "Your internal reference number for the claim or incident batch (e.g. CLM-2024-001). Used only as a label in the output." }, "output_format": { "title": "Output Format", "enum": [ "full", "queue_only", "summary_only" ], "type": "string", "description": "Controls the detail level of the output. 'full' returns all classified photos, groupings, review queue and summary. 'queue_only' returns only the ranked review queue. 'summary_only' returns groupings and summary counts.", "default": "full" } } }, "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 } } } } } } } } } } ```