title: Co-SemDepth: Fast Joint Semantic Segmentation and Depth Estimation on
    Aerial Images
summary: Understanding the geometric and semantic properties of the scene is crucial
  in autonomous navigation and particularly challenging in the case of Unmanned
  Aerial Vehicle (UAV) navigation. Such information may be by obtained by
  estimating depth and semantic segmentation maps of the surrounding environment
  and for their practical use in autonomous navigation, the procedure must be
  performed as close to real-time as possible. In this paper, we leverage
  monocular cameras on aerial robots to predict depth and semantic maps in
  low-altitude unstructured environments. We propose a joint deep-learning
  architecture that can perform the two tasks accurately and rapidly, and
  validate its effectiveness on MidAir and Aeroscapes benchmark datasets. Our
  joint-architecture proves to be competitive or superior to the other single and
  joint architecture methods while performing its task fast predicting 20.2 FPS
  on a single NVIDIA quadro p5000 GPU and it has a low memory footprint. All
  codes for training and prediction can be found on this link:
  https://github.com/Malga-Vision/Co-SemDepth
paper_authors._id: 67e4276e59b2d29cb2b20276
paper_authors.name: Yara AlaaEldin
paper_authors.status: extracted_confirmed
paper_authors.statusLastChangedAt: 2025-03-26T16:41:18.789Z
paper_authors._id: 6780384de11224514e5f1059
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/no-auth/Rxkp1GnjCf-u5AlE1-4gi.png
paper_authors.fullname: Yara AlaaEldin
paper_authors.type: user
paper_authors.user: yaraalaa0
paper_authors._id: 67e4276e59b2d29cb2b20277
paper_authors.name: Francesca Odone
paper_title: Co-SemDepth: Fast Joint Semantic Segmentation and Depth Estimation on
    Aerial Images
paper_summary: Understanding the geometric and semantic properties of the scene is crucial
  in autonomous navigation and particularly challenging in the case of Unmanned
  Aerial Vehicle (UAV) navigation. Such information may be by obtained by
  estimating depth and semantic segmentation maps of the surrounding environment
  and for their practical use in autonomous navigation, the procedure must be
  performed as close to real-time as possible. In this paper, we leverage
  monocular cameras on aerial robots to predict depth and semantic maps in
  low-altitude unstructured environments. We propose a joint deep-learning
  architecture that can perform the two tasks accurately and rapidly, and
  validate its effectiveness on MidAir and Aeroscapes benchmark datasets. Our
  joint-architecture proves to be competitive or superior to the other single and
  joint architecture methods while performing its task fast predicting 20.2 FPS
  on a single NVIDIA quadro p5000 GPU and it has a low memory footprint. All
  codes for training and prediction can be found on this link:
  https://github.com/Malga-Vision/Co-SemDepth
paper_ai_summary: A joint deep-learning architecture predicts depth and semantic maps using monocular cameras for real-time autonomous UAV navigation, outperforming other methods with high speed and low memory usage.
paper_ai_keywords: monocular cameras joint deep-learning architecture depth prediction semantic segmentation MidAir dataset Aeroscapes dataset NVIDIA quadro p5000 GPU real-time navigation low memory footprint