Current aerial imagery can describe a property’s roof, yard, and surroundings in enough detail to support quoting, renewal, and claims before anyone visits the site.
Insurers, real-estate teams, and home-service businesses all need a consistent view of what sits on a land parcel. Mapizy uses machine learning on aerial and satellite imagery to automate discovery of changes to the natural and built environment, then turns those detections into property attributes that can be queried, reported, and integrated through a web app or API.
Key takeaways
Urban 5 cm imagery
High-granularity attributes are created using 5 cm aerial images for 80% of the Australian urban environment. Remote properties are covered with high-resolution satellite imagery.
About 3 months
Geospatial analytics are created from the latest available images, with a stated three-month currency so decisions are based on recent evidence.
30+ attributes
Property analytics cover roof quality and material, solar panels, pools, trees, turf, tree overhang, and other parcel features used in inspection-quality review.
Quote to claim
The same attribute set supports underwriting, automated renewal checks, portfolio alerts, and pre- and post-event damage assessment.
What the report is looking at
A property risk view is not a single score. It is a bundle of observable features and context:
- Building and roof condition, shape, and material
- Added structures and amenities such as pools, sheds, and solar panels
- Vegetation that can create tree-overhang or bushland-distance exposure
- Change since the last image date, used for renewal and portfolio monitoring
Mapizy’s Property Analytics platform is designed to create those parcel-level insights with a single click, measure change over a chosen interval, and export reports. Urban Analytics then rolls similar attributes up to suburbs, LGAs, or a user-defined area of interest.
Methodology
Source imagery
Use the most current aerial coverage where it exists, and high-resolution satellite imagery for remote areas.
Detect features
Computer vision identifies roofs, pools, solar, vegetation, and other parcel objects rather than leaving review to manual photo inspection.
Measure change
Compare attributes across image dates so renewal and portfolio teams can see what was added, removed, or degraded.
Deliver insight
Expose results in Mapizy Studio, through API integration, and as filters for portfolio risk alerts or marketing selections.
How teams use the attributes
Underwriting and quoting
Current roof, amenity, and hazard-context attributes can be used to independently verify customer data and assemble a quote without waiting for a physical inspection. Mapizy’s insurance workflow is built around data-driven risk assessment and API integration into existing quoting systems.
Renewal and claims
Change analytics can notify a carrier when a property adds a pool, loses roof quality, or crosses a tree-overhang or bushland-distance threshold. After flood or bushfire events, pre- and post-event analytics are intended to support faster damage assessment.
Home-service teams use the same roof, solar, and pool attributes to find buildings that match a service offer, while real-estate teams use parcel history and suburb-level growth context for valuation. See Insurance & Real Estate and Home Service & Urban Planning.
Facts in this report are drawn from Mapizy’s published product pages. Coverage, currency, and attribute lists describe the platform as documented on the website. They are not independent audit results or customer performance metrics. Image date, cloud cover, and resolution still bound what can be seen. Diagrams on this page are conceptual.
Related reports and products
See property analytics in the platform
Request a demo to review parcel attributes, change reports, and portfolio filters on your own area of interest.
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