Cabinet

Data annotation for real estate and PropTech

We prepare images, video, plans and documents for AI training — object classification, facade and interior annotation, room segmentation, defect detection and catalog automation.

From a test sample to a production-ready dataset.

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Real estate image annotation for Computer Vision training

Data quality defines AI accuracy in real estate

Problem

  • different angles and lighting;
  • a wide variety of building types;
  • visually similar rooms;
  • non-standard floor plans;
  • partial occlusions;
  • low-quality photos;
  • many small architectural details;
  • incomplete or poorly structured data.

Solution

  • mark buildings and building elements;
  • annotate facades and rooms;
  • segment individual zones;
  • classify images;
  • mark defects;
  • process plans and diagrams;
  • extract data from documents;
  • check annotation quality.

What is data annotation for real estate?

Data annotation for real estate is the preparation of photos, video, floor plans, diagrams and documents for artificial intelligence and computer vision.

Annotators can mark buildings, windows, doors, rooms, interior elements and other objects, classify images, segment zones and label defects.

These datasets are used in PropTech, classifieds, development projects, automatic valuation and technical inspection tools.

Real estate visuals vary widely. The same room type can look completely different, and building elements change with architecture, year of construction and shooting conditions.

AI training therefore needs shared annotation rules, a well-designed class structure and separate handling of hard cases.

Annotation types for real estate

Object detection

Bounding boxes for:

  • buildings;
  • windows;
  • doors;
  • balconies;
  • furniture;
  • fixtures;
  • equipment;
  • architectural elements.

Segmentation

Precise boundaries of:

  • facades;
  • walls;
  • floors;
  • ceilings;
  • rooms;
  • window openings;
  • interior elements;
  • sites and plots.

Object classification

Categories such as:

  • apartment;
  • house;
  • commercial space;
  • office;
  • warehouse;
  • new build;
  • secondary market;
  • interior type;
  • property condition.

Floor-plan annotation

Drawings and plans:

  • rooms;
  • doors;
  • windows;
  • walls;
  • functional zones;
  • dimensions and labels.

Defect annotation

Marking:

  • cracks;
  • damage;
  • chips;
  • moisture traces;
  • deformations;
  • other visual defects.

OCR and documents

Data from:

  • plans;
  • technical documentation;
  • cadastral materials;
  • reports;
  • tables;
  • diagrams;
  • explications.

Annotation examples

Different annotation types for computer vision tasks

ML Pipeline

Full data preparation cycle from raw data to model-ready output

1
Data
Collect and prepare source audio data.
Order data prep
2
Annotation
Annotation aligned with task requirements.
Order annotation
3
Quality Control
Multi-step consistency and QA checks.
Check quality
4
Dataset
Final dataset in required format.
Get dataset
5
Model Training
Ready for ML/AI production pipelines.

Quality control

How we keep real estate annotation stable

Real estate data is visually diverse. One room type can appear in dozens of variants, and architectural elements differ in shape, material and position.

Before scaling we agree annotation rules, class structure and borderline cases.

Annotation passes several review levels so the same approach holds across the whole dataset.

01
Consistent classification
We check classes and attributes of real estate objects.
02
Hard cases
Unusual rooms, complex facades, occlusions and ambiguous objects are reviewed separately.
03
Boundary accuracy
For segmentation and defects we check contour accuracy and annotation completeness.

Where real estate annotation is used

ClassifiedsAutomatic classification of listings and property photos.
PropTechAI services for property analysis and process automation.
DevelopmentBuildings, facades, rooms and construction data.
ValuationFeatures for automatic property valuation models.
Technical inspectionDamage and building defects.
Catalog automationProperty type, room type and characteristics from a photo.
Floor-plan analysisRooms, walls, doors and other plan elements.
Similar-property searchData for visual search and recommendation systems.

AI for real estate object analysis

Computer vision can analyze property photos and extract structured characteristics.

An AI model can determine:

Training such models requires a large set of annotated examples.

US-DATA helps define the class structure, prepare guidelines and annotate the required volume.

Facade and defect annotation

Computer vision is used not only to catalog properties, but also for technical analysis of buildings.

We can annotate:

Depending on the task we use bounding boxes, polygons or segmentation.

These datasets train automatic visual inspection and technical monitoring systems.

US-DATA advantages

Several data types

Photos, video, plans, diagrams and documents can be handled in one project.

Computer Vision expertise

We understand data requirements for detection, segmentation and classification.

Flexible class structure

We follow the customer's ontology or help formalize it.

Complex objects

Non-standard facades, interiors and architectural elements.

Scaling

Start with a pilot and increase volume after quality is confirmed.

Multi-step QA

We control accuracy and consistency throughout the project.

Result for your ML project

1

More accurate property classification

2

Correct facade and interior recognition

3

Automatic property characteristics

4

Defect and damage detection

5

A dataset ready for training and testing

Data security

Enterprise-grade data protection
Security & Compliance
NDA before the project starts.
Access rights separation.
Secure transfer and storage.
Work only in the agreed infrastructure.
Compliance with the customer's local law and applicable standards.
Staff access control for source materials.

Pricing

Expandable sections with indicative cost tables.

Calculate annotation cost

Choose parameters and get instant estimate

Segmentation
Bounding Box
Polygons
Classification
1,000 images

Our offer

Price per 1,000 units$150
Number of images1,000
Number of classes1
ComplexityLow
Project cost$150*
Detailed termsCreate request

* This estimate is not a public offer. Final cost is determined after technical analysis and data review.

News

Latest materials on data annotation and machine learning

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Need a dataset for an AI project in real estate?

Share a data sample and describe the task — we will estimate the volume, suggest an annotation approach and prepare a test batch.

Calculate costDiscuss project

Data annotation for real estate and PropTech

Artificial intelligence and computer vision are increasingly used in real estate, development and PropTech. AI models classify properties, analyze interior photos, recognize architectural elements, process floor plans and find visual building defects. Those systems need carefully annotated data.

US-DATA provides image annotation, video, plan and document labeling for real estate projects. Depending on the task we use bounding boxes, polygons, segmentation, classification and OCR.

Property photos can be annotated for buildings, rooms, windows, doors, furniture, interior and facade elements. The data supports automatic listing classification, property characteristics and computer vision training. Individual elements are found with object detection.

A separate direction is facade and defect annotation. AI models can search for cracks, damage and other visual signs of condition. These tasks especially need accurate image segmentation and shared annotation criteria.

US-DATA can also process floor plans, technical diagrams and documents. Text data annotation helps models recognize rooms, walls, doors, labels and extract structured information.

For classifieds and PropTech platforms, data annotation automates large volumes of listings and photos and improves search, recommendations and catalog structure.

US-DATA can join at any stage: a pilot, guidelines, an existing data pool, or scaling a process that already works.

If your project needs real estate data annotation, property classification, room segmentation, facade labeling or a PropTech dataset, US-DATA will prepare data in the agreed format for training, testing and further use of the ML model.