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The purpose of this paper is to analyze the development of a novel approach for automated terrain mapping a robotic vehicles path tracing.

The approach includes stitching of images, obtained from unmanned aerial vehicle, based on ORB descriptors, into an orthomosaic image and the GPS-coordinates are binded to the corresponding pixels of the map. The obtained image is fed to a neural network MASK R-CNN for detection and classification regions, which are potentially dangerous for robotic vehicles motion. To visualize the obtained map and obstacles on it, the authors propose their own application architecture. Users can any time edit the present areas or add new ones, which are not intended for robotic vehicles traffic. Then the GPS-coordinates of these areas are passed to robotic vehicles and the optimal route is traced based on this data

The developed approach allows revealing impassable regions on terrain map and associating them with GPS-coordinates, whereas these regions can be edited by the user.

The total duration of the algorithm, including the step with Mask R-CNN network on the same dataset of 120 items was 7.5 s.

Creating an orthophotomap from 120 images with image resolution of 470 × 425 px requires less than 6 s on a laptop with moderate computing power, what justifies using such algorithms in the field without any powerful and expensive hardware.

The purpose of this paper is to review and analyze how the development and utilization of artificial intelligence (AI) technologies for social responsibility are defined in the national AI strategies of the USA, Russia and China.

The notion of responsibility concerning AI is currently not legally defined by any country in the world. The authors of this research are going to use the methodology, based on Luciano Floridi’s Unified framework of five principles for AI in society, to determine how social responsibility is implemented in the AI strategies of the USA, Russia and China.

All three strategies for the development of AI in the USA, Russia and China, as evaluated in the paper, contain some or other components aimed at achieving public responsibility and responsible use of AI. The Unified framework of five principles for AI in society, developed by L. Floridi, can be used as a viable assessment tool to determine at least in general terms how social responsibility is implied and implemented in national strategic documents in the field of AI. However, authors of the paper call for further development in the field of mutually recognizable ethical models for socially beneficial AI.

This study allows us to better understand the linkages, overlaps and differences between modern philosophy of information, AI-ethics, social responsibility and government regulation. The analysis provided in this paper can serve as a basic blueprint for future attempts to define how social responsibility is understood and implied by government decision-makers.

The analysis provided in the paper, however general and empirical it may be, is a first-time example of how the Unified framework of five principles for AI in society can be applied as an assessment tool to determine social responsibility in AI-related official documents.