**4.1 Data mining by machine learning**

In EO data mining, a number of researchers have already developed technologies for semantic image understanding [29, 30]. The available web engines are

focused on the everyday needs of a broad category of users [31]. A very popular satellite image data mining system is Tomnod from DigitalGlobe or Google Earth, which is targeting general user topics. Especially for EO, there are systems such as LandEX [32] which is a land cover management system, while GeoIRIS [33] is a system that allows the user to refine a given query by iteratively specifying a set of relevant and a set of nonrelevant images. A similar system is IKONA [34] which is using relevance feedback in order to analyze the content of very high-resolution EO images. Further, the knowledge-driven information mining (KIM) system [41] is an example of an active learning system providing semantic interpretation of image content. The KIM concept evolved into the TELEIOS prototype [36], complementing the scope of searching EO images with additional geo-information and in situ data. Finally, a cascaded active learning prototype [21] has been integrated into an operational EO system [20] to interpret the archives of TerraSAR-X images [37].

CANDELA is improving this cascaded active learning system by searching for dedicated algorithms for typical Earth observation images. Its implementation, test, and validation aim at automated knowledge extraction and image content interpretation. The targeted performance characteristics are verified for several typical use cases and tell us more about the potential of dedicated algorithms with respect to general machine learning.

**Figures 4–9** depict typical classification maps for TerraSAR-X and Sentinel-1 images together with their respective accuracy (e.g., precision/recall) for the cities of Venice, Italy, and Munich, Germany. Another example is the Dutch part of the Wadden Sea in the Netherlands. The results of the classification map and their accuracy are given in **Figures 10** and **11**.
