Small Woody Features (SWF) represent some of the most stable vegetated linear and small landscape features providing numerous ecological and socio-cultural functions, which can be grouped in four main categories: soil and water conservation, climate protection and adaptation, support to biological diversity, and cultural identity. Copernicus Land monitoring service, through the High-Resolution Layers, aims to map those SWFs at Pan- European level (39 countries, 6 million square kilometers) with the use of more than 37,000 Very High Spatial Resolution (VHSR) Earth Observation (EO) scenes. This unprecedented mapping exercise is focused on the extraction of SWF with a maximum width of 30m and a minimum length of 50m for linear features and a minimum and maximum area of 200 and 5,000m respectively. The main outputs are vector and raster products from the Pan-European coverage of the VHSR image data available from the European Space Agency (ESA) Copernicus Space Component Data Access (CSCDA) VHR IMAGE 2015 dataset. To fulfill this goal with a semi-automated approach, we developed a classification processing chain with the goal of very high computer efficiency and accuracy, using Object Based Image Analysis (OBIA) approach and Cloud-computing solutions. This highly efficient methodology based on differential attribute profiles (DAP) and classical classifier such as Random-Forest is particularly adapted to this exercise since it combines the use of spatial information and the spectral signature of each pixel. In this paper, we present the detailed methodology validated at Pan-European scale with various VHSR data source and landscape characteristics as well as full production results and internal validation.