Several global and regional efforts have been undertaken to map human-made settlements and their characteristics, including building material, area, volume, and population. However, given the unprecedented amount of Earth observation data and processing power available, there is a timely need for developing novel approaches for mapping these characteristics at higher spatial and temporal resolution. Such information is key to effectively answering questions related to population growth, pollution, disaster management, risk assessments, spatial planning, and even generating business cases in peri-urban and rural areas. While such data is available from mapping agencies or commercial companies in some countries, there are many countries where this is not the case. The main objective of this study is to propose an Inception-ResNet inspired deep learning approach to estimate the characteristics and location of human-made structures, including estimates of population, based on Earth observation data from the Copernicus Programme. The study investigates the effects on prediction accuracy using data from different orbital directions and interferometric coherence from Sentinel 1 data and different band combinations of Sentinel 2 data as model input variables. The model is trained and evaluated on a nationwide Danish case study, where the national mapping agency provides high-quality open data on human-made structures, which serves as the ground truth data for the study. Our findings reveal that it is possible to design models that, on average, perform within 2.6% total absolute percentage error for area predictions, 7.7% for volume and 17% for population at 10 by 10 m scale using only Copernicus data and deep learning models. The models achieved 98.68% binary accuracy for extracting structural area when all test sites were merged. Combining Sentinel 1 and 2 input variables yielded the best results, while adding interferometric coherence did not significantly improve accuracy. Furthermore, including data from both orbital directions of the Sentinel 1 constellation significantly improved model performance.