@INPROCEEDINGS{8729787,
  author={A. {Diegues} and J. {Pinto} and P. {Ribeiro} and R. {Frias} and d. C. {Alegre}},
  booktitle={2018 IEEE/OES Autonomous Underwater Vehicle Workshop (AUV)}, 
  title={Automatic Habitat Mapping using Convolutional Neural Networks}, 
  year={2018},
  volume={},
  number={},
  pages={1-6},
  abstract={Habitat mapping is an important task to manage ecosystems. This task becomes most challenging when it comes to marine habitats as it is hard to get good images in underwater conditions and to precisely locate them. In this paper we present a novel technique for performing habitat mapping automating all phases, from data collection to classification, lowering costs and increasing efficiency throughout the process. For mapping habitats in a vast coastal region, we use visible light cameras mounted on autonomous underwater vehicles, capable of collecting and geo-locating all acquired data. The optic images are enhanced using Computer Vision techniques, to help specialists identify the habitats they contain (during training phase). In a later stage, we employ convolutional neural networks to automatically identify habitats in all imagery. Habitats are classified according to the European Nature Information System, an European classification standard for habitats.},
  keywords={autonomous underwater vehicles;cameras;computer vision;convolutional neural nets;geophysical image processing;image classification;image sensors;oceanographic techniques;remote sensing;remotely operated vehicles;underwater conditions;data collection;autonomous underwater vehicles;convolutional neural networks;automatic habitat mapping;marine habitats;data classification;coastal region;visible light cameras;optic image enhancement;computer vision techniques;European Nature Information System;European classification standard;Training;Data models;Europe;Biology;Image color analysis;Rocks;Sea measurements;Convolutional Neural Networks;Computer Vision;Marine Habitat Mapping;European Nature Information System;Autonomous Underwater Vehicles},
  doi={10.1109/AUV.2018.8729787},
  ISSN={2377-6536},
  month={Nov},}