Building rooftop extraction from aerial imagery using low complexity UNet variant models-Reference-Cited by-同舟云学术

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Building rooftop extraction from aerial imagery using low complexity UNet variant models

Published:2024-02-06 Issue: Volume: Page:1-28
ISSN:1449-8596
Container-title:Journal of Spatial Science
language:en
Short-container-title:Journal of Spatial Science

Author:

Ramalingam Avudaiammal¹^ORCID,Srivastava Vandita²^ORCID,George Sam V¹^ORCID,Alagala Swarnalatha³^ORCID,Manickam Martin Leo¹^ORCID

Affiliation:

1. Electronics and Communication Engineering, St Joseph’s College of Engineering, Chennai, Tamil Nadu, India

2. Head & Scientist ‘SG’, Geoinformatics Department, Course Director, IIRS-ITC (Netherlands)-Joint Education Program, Indian Institute of Remote Sensing, Dehradun, Uttar Pradesh, India

3. Electronics and Communication Engineering, St. Peter’s College of Engineering and Technology, Chennai, Tamil Nadu, India

Funder

Indian Space Research Organisation

Publisher

Informa UK Limited

Link

https://www.tandfonline.com/doi/pdf/10.1080/14498596.2024.2302166

Reference51 articles.

1. Deep learning-based sentiment classification of evaluative text based on Multi-feature fusion

2. Building Footprint Extraction from High Resolution Aerial Images Using Generative Adversarial Network (GAN) Architecture

3. Short-time-series grassland mapping using Sentinel-2 imagery and deep learning-based architecture

4. Integrating semantic edges and segmentation information for building extraction from aerial images using UNet

5. RoadVecNet: a new approach for simultaneous road network segmentation and vectorization from aerial and google earth imagery in a complex urban set-up

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