AI Superior

A leading real estate online platform required a solution that would facilitate the value assessment of urban zones within a city. To provide industry professionals with more accurate insights, we developed a segmentation model to assist in this process. We develop a solution that facilitates the assessment of prices for different areas within a city. The project involved analyzing the city map using a semantic segmentation approach to generate a detailed segmentation map. This map classified pixels into predefined classes such as roads, residential houses, infrastructure, green areas, and open land areas. Historical maps from different years were also incorporated to gain insights into the city's evolution over time, particularly focusing on the availability of open areas for real estate development. These insights allowed the real estate company to assess the value and desirability of specific regions within the city and make informed price evaluations based on their expertise and market knowledge.

Our client, a solar energy company, faced the challenge of time-consuming manual methods for analyzing residential roofs for solar panel installations. To improve overall efficiency and accuracy, we developed an automated solution that enables efficient planning for solar panel implementation. The solution utilises deep learning models to accurately detect and segment roofs into distinct areas, and estimates the area and dimensions of each segment, enabling optimal solar panel placement on residential roofs

Cities face numerous challenges in maintaining and inspecting their road infrastructure, particularly when it comes to efficiently detecting and evaluating road damage, such as potholes. Manual inspections are time-consuming, subjective, and often result in delays in identifying and repairing road defects. To address this challenge, we developed a platform that utilizes deep learning segmentation models to accurately detect and evaluate potholes and road damage. The platform accepts video footage or individual frames as input and applies a deep learning model to accurately segment potholes. Key features such as size and area are extracted for each segmented pothole, providing essential information for estimating severity levels. The platform also includes a scalable GIS application for visualizing road damage, customizable notifications for critical potholes, and filtering capabilities to prioritize repairs. Plus, it can be integrated into a real-time video processing pipeline for continuous monitoring and instant detection.

Our customer, an Ophthalmology Centre, faced the challenge of accurately estimating the volume of fat and muscle in the human eye. This information was crucial for monitoring eye health, evaluating the effectiveness of interventions, and conducting comparative analyses. We employed deep learning techniques to develop an advanced model capable of accurately segmenting fat and muscle tissue in each slice of an MRI orbit scan. By leveraging the model’s capabilities, we achieved highly precise volume estimation for the eye’s anatomical structures. Our deep learning model was designed to handle different views of the eye, including Coronal, Sagittal, and Axial, ensuring comprehensive analysis of the orbital region.

We developed a drone-based application for a city municipality to automate construction site inspections and detect various types of construction debris. The solution significantly reduces human involvement in the inspection process and decreases average inspection costs.