Forest and non-forest trees and shrubs (hereafter collectively referred to as trees), are the basis for the functioning of tree-dominated ecosystems, and are regularly monitored at country scale via forest inventories. However, traditional inventories and large-scale forest mapping projects are expensive, labour-intensive and time-consuming, resulting in a trade-off between the details recorded, spatial coverage, accuracy, regularity of updates, and reproducibility. Also, forest inventories typically do not account for individual trees outside forests, although these trees play a vital role in sustaining communities through food supply, agricultural support, among other benefits. Moreover, the alarming rate of tree cover loss resulting from different natural and human-induced processes has brought both political and economic motives to attract efforts for landscape restoration especially in Africa. Nevertheless, currently, there is no accurate and regularly updated monitoring platform to track the progress and biophysical impact of such ongoing initiatives. Recent approaches counting trees in satellite images in Africa used very costly commercial images, were limited to isolated trees in savannas excluding small trees, and did not cover other complex and heterogeneous ecosystems such as forests. Here, we make use of novel deep learning techniques and publicly available aerial imagery, and introduce an accurate and rapid method to map the crown size, number of trees inside and outside forests, and corresponding carbon stock, regardless of tree size and ecosystem types in Rwanda. The applied deep learning model follows a UNet architecture and was trained using 67,088 manually labeled tree crowns. We mapped over 200 million individual trees in forests, farmlands, wetlands, grasslands, and urban areas, and found about 67.2% of the mapped trees outside forests. An average tree density of 94.6 and 70.8 trees per ha, and average crown size of 38.7 m2 and 15.2 m2 were mapped inside and outside forests, respectively. In savannas we found 64 trees per ha with an average crown size of 15.6 m2. In farmlands we found 79.6 trees per ha with an average crown size of 16.3 m2. We expect methods and results of this kind to become standard in the near future, enabling tree inventory reports to be of unprecedented accuracy.