Enhancing explainability of deep learning models for point cloud analysis: a focus on semantic segmentation-Reference-Cited by-同舟云学术

Enhancing explainability of deep learning models for point cloud analysis: a focus on semantic segmentation

Published:2024-09-02 Issue:1 Volume:17 Page:
ISSN:1753-8947
Container-title:International Journal of Digital Earth
language:en
Short-container-title:International Journal of Digital Earth

Author:

Matrone Francesca¹^ORCID,Paolanti Marina²^ORCID,Frontoni Emanuele²^ORCID,Pierdicca Roberto³^ORCID

Affiliation:

1. Department of Environment, Land and Infrastructure Engineering (DIATI), Politecnico di Torino, Torino, Italy

2. Department of Political Sciences, Communication and International Relations, Università di Macerata, Macerata, Italy

3. Department of Construction, Civil Engineering and Architecture (DICEA), Università Politecnica delle Marche, Ancona, Italy

Publisher

Informa UK Limited

Link

https://www.tandfonline.com/doi/pdf/10.1080/17538947.2024.2390457

Reference62 articles.

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2. Ahsan Md Manjurul Kishor Datta Gupta Mohammad Maminur Islam Sajib Sen Md Lutfar Rahman and Mohammad Shakhawat Hossain. 2020. “Study of Different Deep Learning Approach with Explainable AI for Screening Patients with COVID-19 Symptoms: Using CT Scan and Chest X-ray Image Dataset.” Preprint arXiv:2007.12525.

3. Armeni Iro Ozan Sener Amir R. Zamir Helen Jiang Ioannis Brilakis Martin Fischer and Silvio Savarese. 2016. “3D Semantic Parsing of Large-Scale Indoor Spaces.” In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Las Vegas NV USA June 27–30 1534–1543. IEEE. https://doi.org/10.1109/CVPR.2016.170.

4. An Improved Explainable Point Cloud Classifier (XPCC)

5. Explainable Artificial Intelligence for Machine Learning-Based Photogrammetric Point Cloud Classification