With tree mortality rates rising across many regions of the world, efficient methods to map dead trees are becoming increasingly important to monitor forest dieback, assess ecological impacts, and guide management strategies. Deep learning-based pattern recognition combined with the high spatial detail of aerial images from drones or airplanes provides an avenue for mapping dead tree crowns or partial canopy dieback, collectively referred to as standing deadwood. However, current methods for mapping standing deadwood are limited to specific biomes or image resolutions. Here, we present a transformer-based semantic segmentation model that generalizes across forest biomes and a wide range of image resolutions (1–28 cm) for mapping both dead tree crowns and partial canopy dieback. Our approach combines a SegFormer-based transformer architecture for image feature extraction and Focal Tversky Loss to mitigate class imbalance. We used a globally distributed crowd-sourced dataset of 434 high-resolution aerial images and manual delineations of standing deadwood of vastly varying quality. The orthophotos span all major forest biomes and cover 10,778 hectares. To further mitigate imbalances across biomes, resolutions, deadwood occurrence, and image sources, we developed a four-dimensional sampling scheme that ensures balanced representation during training. The models were trained and evaluated using heterogeneous crowd-sourced data, which, as expected, negatively affects the F1-scores. A visual inspection on independent data highlights the very precise quality of the segmentation. Our analysis revealed resolution-dependent performance variations across biomes, suggesting a relationship between optimal mapping resolution and biome-specific characteristics. We make both our model and a machine-learning-ready dataset publicly available on deadtrees.earth to support future research in tree mortality mapping.