Technosignatures involves the detection of radio signals, lasers, atmospheric pollution, radiation leakage from megastructures or sidereal installations such as Dyson spheres, Shkadov thrusters with the power to alter the orbits of stars around the Galactic Centre, etc. Some authors have postulated the possibility of previous ancient civilizations indigenous to our solar system having left behind some techno-signatures that we might find. However, if we look for these techno-signatures, artificial structures or signs, our minds can easily become confused when confronted with the unexpected. In recent times we had news about several events that made us question its possible extraterrestrial intelligent origin: The Tabby star anomaly, Oumuamua object and the 16 days periodic radio patterns detected by CHIME/FRB. Facts open to interpretation, our brain interpretation. The question is whether our minds are ready and capable of finding and understanding such techno-signatures, or whether we need to wait for our consciousness to be able to apprehend and comprehend these features. Could we get some help from artificial intelligence (AI)?

Natural-like technosignatures candidates may represent a detection problem for both artificial systems and humans. We tested traditional computer vision models with natural formations with special characteristics, Ahuna Mons region in Ceres in this particular case. We looked if these artificial models may represent a trustful aid to human detection and identification of potential technosignatures in planetary surfaces. Ahuna Mons is a 4km particular geologic feature on the surface of Ceres of possibly cryovolcanic origin. The special characteristics of Ahuna Mons are also interesting in regard of its surrounding area, especially for the big crater besides. This crater possesses similarities with Ahuna Mons including diameter, age, morphology, etc. Under the cognitive psychology perspective and using current computer vision models we analyzed these two features on Ceres for comparison and pattern recognition similarities. Several algorithms were employed avoiding human cognitive bias. 3D analysis from images of both features characteristics are discussed. Results showed positive results for these algorithms about similarities of both features. Discussion is provided about implications of this pilot computer vision techniques experiment for Ahuna Mons and the potential cognitive bias problem of both human and Artificial Intelligence models and the risks for the search of technosignatures.