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Corals are the primary habitat-building life-form on reefs that support aquarter of the species in the ocean. A coral reef ecosystem usually consists ofreefs, each of which is like a tall building in any city. These reef-buildingcorals secrete hard calcareous exoskeletons that give them structural rigidity,and are also a prerequisite for our accurate 3D modeling and semantic mappingusing advanced photogrammetric computer vision and machine learning. Underwatervideography as a modern underwater remote sensing tool is a high-resolutioncoral habitat survey and mapping technique. In this paper, detailed 3D meshmodels, digital surface models and orthophotos of the coral habitat aregenerated from the collected coral images and underwater control points.Meanwhile, a novel pixel-wise semantic segmentation approach of orthophotos isperformed by advanced deep learning. Finally, the semantic map is mapped into3D space. For the first time, 3D fine-grained semantic modeling and rugosityevaluation of coral reefs have been completed at millimeter (mm) accuracy. Thisprovides a new and powerful method for understanding the processes andcharacteristics of coral reef change at high spatial and temporal resolutionunder climate change.

Combining Photogrammetric Computer Vision and Semantic Segmentation for Fine-grained Understanding of Coral Reef Growth under Climate Change