A 10-meter global terrace mapping using sentinel-2 imagery and topographic features with deep learning methods and cloud computing platform support

• We compare eight State-of-the-Art deep learning models in identify global terrace. • The first Global Terrace Map (GTM) was generated at 10-meter resolution for 2022. • Global terrace area was estimated at 853,161 km 2 , ∼5.1 % of global total cropland. Terrace agriculture plays a vital role in mountainous regions by preventing soil erosion, optimizing land use, and supporting local ecosystems. However, research on the global distribution of terraces is limited due to the lack of unified automatic identification model. Despite the rapid advancements in deep-learning architectures in recent years, their performance in extracting terrace maps still needs investigation. To address this limitation, this study compares the performance of eight state-of-the-art deep learning models, including UNet, HRNet, DeepLabv3+, TransUNet, Segmenter, PVT v2, Swin-Unet, and PerSAM. Sentinel-2 imagery was selected for its spectral properties, while Digital Elevation Model (DEM) imagery was chosen for detailed topographic information. UNet outperformed others in terrace identification, achieving an overall accuracy of 92.8 % and a mean Intersection over Union (MIoU) of 75.9 %. The entire data processing workflow, using Google Earth Engine for data acquisition, Google Drive for storage, Google Earth Pro for computational capabilities, and the T4 GPU in cloud computing resources, requires approximately 625 h. As a result, the Global Terrace Map (GTM) was generated at 10-meter resolution for 2022. The total terrace area was estimated at 853,161 km2, accounting for about 5.1 % of global cropland. The countries with the most extensive terraced areas, as identified, are China (298,908 km 2 , 18 % of total cropland), Ethiopia (127,266 km 2 , 47 %), Kenya (36,385 km 2 , 37 %), India (34,485 km 2 , 2 %), and Democratic Republic of the Congo (31,422 km 2 , 21 %). This pioneering global terrace map is anticipated to bridge a significant data gap in the field of resilient agriculture. It will offer invaluable insights into the spatial distribution and attributes of terraced farming systems, along with their roles in enhancing food security and promoting environmental sustainability.