What to look for in AI-Imagery for asset management
AI-analysed satellite imagery is appearing everywhere in asset management, from road maintenance to utilities. Supply is growing fast, but quality varies enormously. For municipalities, provinces and asset managers considering satellite data, it pays to look beyond the attractive pictures. The criteria below help you make a well-founded choice.
1. How current is the data really?
A satellite image from two years ago says little about the situation today. Ask about the actual capture frequency: is an area recaptured every year, or is the dataset a snapshot that quickly goes out of date? For asset management, recency is often more important than resolution alone, because outdated data leads to the wrong decisions
2. Is raw data turned into usable information?
A satellite image only becomes valuable once AI translates it into concrete asset information: location, type and condition. Providers that deliver imagery alone push the analysis work back onto your own organisation. Choose a party that has already made that translation.
3. Does the detection match your own asset catalogue?
Not every AI solution recognises the same objects. Check which asset types are actually detected and whether that matches what your organisation manages, from traffic signs to green spaces. A generic model that is not tuned to day-to-day practice quickly produces noise instead of insight.
4. Where is the data stored, and who has access to it?
For data about public space, data sovereignty plays an ever-larger role. Check whether the data is stored on Dutch or European servers and whether that meets your organisation’s requirements and the procurement frameworks you work with.
5. Is change detection between captures reliable?
The real value of repeated satellite capture lies in signalling change: new assets, objects that have disappeared, or a condition that deviates. Ask in detail how accurately a provider recognises those changes, and how much manual checking is still needed before the results are usable.
6. Does the cost model fit the way the data is used?
Pay-per-click or paying per download adds up quickly as soon as several teams work with the same data. A licence model per concurrent user gives more predictability and encourages broad use across the organisation instead of limited use for cost reasons.
AI-InfraSolutions combines nationwide, annual capture with AI-driven asset detection on Dutch servers, so municipalities and asset managers do not have to piece together separate data sources themselves. Curious what that would look like for your organisation? Get in touch for a demo.