Platform, systems, and methods for identifying property characteristics and property feature maintenance through aerial imagery analysis

What is this patent about?

’029 is related to the field of automated property assessment and risk management. Traditionally, evaluating the physical state of a building—such as the structural integrity of a roof or the quality of exterior cladding—required manual on-site inspections or the review of outdated municipal records. This process was often slow, subjective, and expensive for insurance carriers and real estate investors who need to quantify risk across large portfolios of properties. The invention seeks to modernize this by leveraging high-resolution imagery and computer vision to remotely diagnose property health.

The underlying idea behind ’029 is the use of a dual-stage machine learning pipeline to transform raw pixels from aerial imagery into actionable financial and risk metrics. Rather than just identifying that a building exists, the system first isolates specific architectural features, such as rooftop shapes, and then subjects those specific regions to a secondary analysis to detect signs of wear, weathering, or damage. By combining the structural type of a feature with its current physical state, the system can generate highly specific predictions regarding how a property will perform during a catastrophe or what it will cost to restore.

The claims of ’029 focus on a computer-implemented method and system that receives a property identifier and uses processing circuitry to extract pixel groupings from aerial images. The independent claims require a two-step classification process: a first machine learning classifier identifies the specific property characteristic, while a second machine learning classifier determines the condition of that identified characteristic. This specific sequence allows the system to calculate real-time outputs, specifically disaster risk estimates and replacement costs, which are then delivered back to a user through a graphical interface.

In practice, the invention functions by first aligning aerial photos with municipal shape maps to ensure the analysis is focused on the correct parcel boundaries. Once the property is isolated, the system uses deep learning architectures, such as Network in Network (NIN), to categorize features like gable or hip roofs. Following this, the system performs a color histogram analysis to evaluate pixel intensity distributions. A new roof typically exhibits sharp contrasts and uniform colors, whereas a deteriorating roof shows blurred edges and patchy, asymmetrical pixel distributions that the system flags as a high-risk condition.

This approach differs from prior solutions by moving beyond simple object detection to provide a qualitative assessment of maintenance levels. While older systems might identify a roof's presence, ’029 integrates the vulnerability profiles of specific shapes—such as the high wind sensitivity of a gambrel roof—with the detected wear and tear of that specific instance. By automating this link between visual condition and actuarial risk, the system enables insurance providers to generate real-time cost estimates and verify repairs without the latency of a human inspector.