AI-Powered Flood Detection for NYC Streets
What it is for
- FloodVision supports flood risk management by detecting flooding and estimating flood depth at road locations across New York City, helping DEP identify and corroborate flooding hot spots in priority areas.
- Run by
- Department of Environmental Protection (DEP)
- Where
- No fixed location
- Kept
- Not stated by the NYC Office of Technology and Innovation.
- Shared with
- Accountable organization
- Your copy
- You cannot see the data it holds about you. What you can do
What it is for
FloodVision automatically analyzes live road camera footage from the Department of Transportation to detect flooding and estimate water depth across New York City. The system operates fully autonomously and provides flood-detection data to the Department of Environmental Protection to support decisions about flood response priorities. No personal or identifying information about individuals is collected or used — the system only observes road conditions.
What it collects and what happens to it
Data taken in
- Images and video footage from Department of Transportation road cameras at specific NYC street locations, capturing road surface and water conditions. No identifying information about individuals is collected.
Processing
- Computer vision models trained on road camera images and video analyze frames to detect flood presence and estimate water depth. Implemented on Google Cloud Platform's AI cloud offerings by ClearObject.
What it does
- The system automatically processes DOT road camera images and video to detect the presence of flooding and estimate water depth, without human review of each camera frame.
- The system scores and classifies detected flood conditions (presence and approximate depth), producing data points that DEP staff use alongside other information to make flood response decisions.
Outputs
- The system outputs whether flooding has been detected at a given road camera location and the approximate depth of the flood at that location. This information is provided to DEP to inform flood response decisions.
- Flood detection results serve as one of several data points that helps corroborate flooding hot spots in priority areas, functioning as an advisory input to DEP's data-driven decision-making process rather than a binding determination.
Run by
- The NYC Department of Environmental Protection's Bureau of Sustainability deploys and operates FloodVision to receive flood-detection information and support data-driven decisions about flooding hot spots in priority areas.
Built by
- ClearObject is a professional services vendor that implemented FloodVision using Google Cloud Platform's AI cloud offerings.
Kept for
Not stated by the NYC Office of Technology and Innovation.
Shared with
- The flood detection outputs are provided to DEP for internal operational use. No public-facing access mechanism or individual data access portal is described in the register entry.
- Flood detection data is provided to the NYC Department of Environmental Protection to inform data-driven decisions about flood response.
Stored
Not stated by the NYC Office of Technology and Innovation.
How to read the colours
Can it identify you?
- Anonymized data
- Data about people with the link to who is broken. Stripped of identifiers, blurred, aggregated, or noised so this system can’t reasonably tie a record back to an individual.
- Pseudonymous data
- Each person’s data is tied to a token (hash, ID, template) that lets this system recognise the same person across events, but the token itself doesn’t reveal a name. Reidentification is possible with extra information.
- Identifiable data
- The data either contains a direct identifier (name, address, account name, recognisable face or voice, plate number) or carries a token this system uses to look up legal identity during processing.
Who completes the loop?
- Human decides
- This mode suggests; a person decides what to do next. The AI is always advisory — a human is in the loop on every decision. Example: a triage tool ranks cases for a clinician who chooses which to see first.
- Human executes
- This mode decides; a person carries out the result. Example: an optimizer plans the day’s trash-collection routes, and drivers run them.
- Autonomous
- This mode decides and acts on its own. No person reviews each decision or carries out the resulting action.
Definitions from the DTPR standard. Amber is about your data, violet about who decides. The fuller the shape and the deeper the colour, the more identifying the data or the less a person is involved.
- AI registerNYC Algorithmic Tools Compliance Report — FloodVisionNYC Office of Technology and Innovation, Algorithmic Tools Compliance Report, row id nyc__row__3d51e6781420, reported 2025.
- AI registerNYC Algorithmic Tools Compliance Report — FloodVision
- AI registerNYC Algorithmic Tools Compliance Report — FloodVision
- AI registerNYC Algorithmic Tools Compliance Report — FloodVision
- AI registerNYC Algorithmic Tools Compliance Report — FloodVision
- AI registerNYC Algorithmic Tools Compliance Report — FloodVision
- AI registerNYC Algorithmic Tools Compliance Report — FloodVision
- AI registerNYC Algorithmic Tools Compliance Report — FloodVision
- AI registerNYC Algorithmic Tools Compliance Report — FloodVision
- AI registerNYC Algorithmic Tools Compliance Report — FloodVision
- AI registerNYC Algorithmic Tools Compliance Report — FloodVision
- AI registerNYC Algorithmic Tools Compliance Report — FloodVision
- AI registerNYC Algorithmic Tools Compliance Report — FloodVision
- AI registerNYC Algorithmic Tools Compliance Report — FloodVision
- Register entryPublished by the NYC Office of Technology and Innovation. Reference 5444f342. This disclosure was drafted with AI assistance.Schema: ai@2026-05-06-beta
What you can do
Ask about this system
Questions go to the NYC Office of Technology and Innovation, not the vendor.
Your rights
- Right to Algorithmic TransparencyInformation about FloodVision's operation is publicly disclosed through the NYC Algorithmic Tools Compliance Report, available at the NYC Open Data portal. The register describes the system's purpose, inputs, outputs, vendor, and autonomy level.
Risks and safeguards
- Civil liberties harmContinuous road camera surveillance infrastructure, even when configured for flood detection only, could be repurposed or expanded to monitor individuals or groups.Safeguard: The register explicitly states no identifying information is collected or disclosed. The system's stated purpose is limited to detecting road flooding conditions, and outputs are restricted to flood presence and depth at geographic locations.