Computer Vision in Real Estate (2022 Guide)

For many years, humanity had dreamed of the utopian goal of building intelligent machines that could think and act like humans. One of the most exciting concepts was to enable computers to "see" and understand their environment. The historical fiction of yesterday and what people dreamed of has now turned into a contemporary reality.

Computer vision technology has made great strides toward inclusion in our daily lives due to artificial intelligence and computational capacity developments. By 2023, it is anticipated that the computer vision market will be worth $48.6 billion.

 Here's what we'll cover:

• What is Computer Vision in AI and Machine Learning?
• What about the Computer Vision's Role in AI?
• Top 3 Most Popular Computer Vision Applications
• Computer Vision in Real Estate
• Applications and Benefits of the Computer Vision in Real Estate
  

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What is Computer Vision in AI and Machine Learning?

It is creating digital systems that can process, analyze, and grasp visual input (images or videos) in the same way that people can is the focus of the computer science subfield known as computer vision.

The basis of computer vision is the instruction of computers to comprehend and interpret pixel-level images. In terms of technology, machines retrieve visual information, process it, and analyze the outcomes using complex software algorithms.

Computer vision is understanding pictures and movies as they appear in digital representations. In Machine Learning (ML) and AI, computer vision is used to train the model to recognize specific patterns and store the information in its artificial memory, which may then be used to forecast outcomes in real-world scenarios.

Building a model that can function without human intervention is the aim of using computer vision technology in machine learning and artificial intelligence. For application in a real-world situation, digital photographs must be collected, processed, analyzed, and understood throughout the entire process.

What about the Computer Vision's Role in AI?

There are many sectors where computer vision is being used as an applied study. It is essential for helping machines recognize different kinds of things in their natural environments, whether through machine learning or AI research.

The single technology that offers AI-enabled devices an advantage to operate well—from basic household chores to recognizing people in photos, spotting objects in autonomous vehicles, and engaging in combat—is computer vision.

Artificial intelligence is increasingly being used in new fields, including manufacturing, robotics, agriculture, automotive, healthcare, and other areas of autonomous flight, like drones and retail.

If you want to read more about what Computer Vision is, you can check out this article: Computer Vision AI on EDGE .

Top 3 Most Popular Computer Vision Applications

1. Transportation

The transportation industry's growing demands have fueled technological advancement in this field, with computer vision at its core.

The Intelligent Transportation System (ITS) has emerged as a crucial area for advancing transportation effectiveness, efficiency, and safety, from driverless vehicles to parking occupancy detection.

Let's take a look at some of this industry's most widely used computer vision applications.

Self Driving Cars

Autonomous vehicles won't be science fiction in 2022. In fact, the dependability and safety of self-driving cars are already being tested and improved by thousands of engineers and developers worldwide.

Computer vision is used to identify and categorize things (such as traffic lights or road signs), build 3D maps, or estimate motion. It also played a significant role in the development of autonomous cars.

Pedestrian detection

Due to its potential influence on the design of pedestrian protection systems and smart cities, pedestrian identification and tracking have become crucial computer vision research topics.

Using cameras, it leverages differences in body posture, clothing, occlusion, lighting conditions, and background clutter to automatically identify and locate pedestrians in photos or videos.

Applications for pedestrian detection can be found in areas including traffic control, autonomous driving, and efficient transit.

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2. Manufacturing 

With computer vision at its core, the manufacturing sector has already embraced a wide range of automation solutions.

Automation of quality control reduces safety hazards, boosts productivity, and increases effectiveness.

Defect Inspection

Large-scale manufacturing facilities frequently struggle to detect defects in their produced goods with 100% accuracy.

Real-time data can be gathered by camera-based systems, which then analyze it using computer vision and machine learning algorithms and compare the results to a predetermined set of quality standards. It aids in more effectively recognizing both macro and micro-level manufacturing line faults.

Reading Text and Barcodes 

OCR, a computer vision technology, may be successfully used to detect, validate, convert, and translate barcodes into the legible text because most products have barcodes on their packaging. Labels or boxes photographed can have their text retrieved and cross-referenced with databases using OCR.

This process aids in the detection of products with incorrect labels, the provision of expiration date information, the publication of product amount information, and the tracking of packages throughout the entire product creation process.

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Now, let's move on to the third and in-depth subject: the Real Estate Industry.

Computer Vision in Real Estate

The use of computer vision in the real estate industry is widespread, and its various applications can benefit both customers and businesses greatly. Computer vision has the potential to upend the real estate industry by assisting in content search and discovery as well as producing suggestions, metadata, content moderation, and damage analysis. Running a successful real estate firm requires images and videos.

It is used for a variety of purposes and can often significantly affect decision-making. Many companies, such as Amazon Rekognition and Cameralyze, offer ready-made solutions for integrating computer vision into existing business databases. 

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Subsequent analysis can empower real estate professionals during a product's pre-purchase, purchase, and post-purchase stages.

Let's look at how Computer Vision is applied to Real Estate and its benefits.

Applications and Benefits of the Computer Vision in Real Estate

• Unlock listing data through photos
  

Hundreds of property characteristics generally not recorded as part of the listing information can be revealed from listing images with the aid of computer vision. Without entering the property, users can comprehend subjective elements like the quantity and quality of the natural light and the size of the view.

• Convert picture searches to listing searches
  

Instead of listing properties using measurable property parameters like beds, baths, living areas, etc., as has historically been done, users will be able to advertise properties using unique image characteristics.

• Quickly find matching properties.
  

The image-based search could locate assets that exhibit comparable traits across a range of attributes. With more accurate attribute descriptions, agents would be able to match properties, increasing efficiency, and customer satisfaction.

• Create property information for listings using only the photographs
  

Agents will soon be able to provide complete property data and a list of amenities broken down by room by simply submitting a set of images. In order to develop listing information that can be uploaded straight into an MLS or a listing platform, computer vision will be used to recognize the items present in these photographs, demarcate them, and create listing information.

• Comparing homes based on their rooms
  

Computer vision can compare different properties side by side in terms of square footage, the distance between windows and doors, the amount of natural light, etc. If there are multiple photos of the same room, they can be grouped for easier comparison and evaluation. Due to the ability to process photographs in real-time, all of this is achievable even for listings that were entered years ago. This might give potential buyers of real estate an engaging user experience.

Executive summary

Disruptive technology with several uses in the real estate industry is computer vision. Computer vision and artificial intelligence can assist real estate advisors in providing effective and efficient services to clients in a property transaction's pre-purchase, purchase, and post-purchase processes, from assisting in content search and discovery to generating recommendations and content moderation. Real estate organizations are already using technologies from businesses like Cameralyze and Amazon Rekognition to integrate computer vision with their current picture and video databases.

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