Abstract
This data article describes an open dataset of thermal images of buffalo udders curated for pixel-level segmentation, the first publicly accessible open dataset on the topic. Images were acquired in situ at a commercial farm in southern Italy over >1 year during routine robotic milking, using a fixed, rear-facing long-wave infrared camera ( 640 × 480 resolution) positioned at approximately 1 m, with emissivity set to 0.98. Environmental measurements (ambient temperature and relative humidity) from a nearby station were recorded at capture time and used for temperature compensation before min-max conversion to single-channel 8-bit PNGs. The release contains 2148 image-mask pairs organised in predefined train/validation/test splits. Two veterinarians produced udder masks with polygonal annotation following a shared protocol and resolving disagreements by consensus. The dataset is intended to support reproducible research on udder-region segmentation, a necessary preprocessing step before extracting udder skin surface temperature (USST) in mastitis-oriented studies of dairy animals but not limited to only mastitis. Beyond udder health, thermal computer vision tasks in veterinary science include behaviour monitoring and localisation of anatomical regions for temperature tracking. Labelled thermal segmentation data can also serve as source material for pretraining and transfer to such applications.