Observation is the foundation of any science. But understanding, analysing, processing and transforming it into data that improve everyday life is another history.
Human activities have reached a complexity level that is no longer possible to evaluate just from the ground.
Satellite imagery is not a human eye, but it started to take a significant role in clearing the perspective of this complexity. This vast amount of growing data requires machines as well as humans.
Companies and different types of organizations can now access this data, privileged to few just until recently, due to data democratization.
Geospatial Agility is about this data: access it and use it for economic, logistic or other humanitarian activities.
Content based image retrieval (CBIR) is a process framework for efficiently retrieving images from a collection by similarity.
The query relies on extracting the appropriate characteristics quantities describing the desired contents of images, not on keywords, tags or description associated with image.
Well, we know it’s mind blowing, but there is a good logic...you’ll see!
Geospatial Agility is applying the CBIR technique to data acquired via Earth Observation (EO) technologies (satellites, remote sensing), and it is a general-purpose image collections search engine. The service finds similar patches over large amounts of satellite images data, according to your query.
How the service works:
You define your area of interest - for example, river/road in a city, vessel containers, storage tanks, oilfields etc - and the service finds similar patches over large amounts of data. Each image patch is characterized based on specific descriptors adapted to EO image particularities - colours, shapes, texture, or any other information that can be derived from the image itself.
The algorithm searches for content similarities and delivers several visualization approaches from which you can narrow your findings. Search can be done both in the selected scene or in data base, and it is using optic and radar scenes.