Integrating semantic edges and segmentation information for building extraction from aerial images using UNet-Reference-Cited by-同舟云学术

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

Published:2021-12 Issue: Volume:6 Page:100194
ISSN:2666-8270
Container-title:Machine Learning with Applications
language:en
Short-container-title:Machine Learning with Applications

Author:

Abdollahi Abolfazl,Pradhan Biswajeet^ORCID

Funder

University of Technology Sydney

Publisher

Elsevier BV

Subject

General Medicine

Reference43 articles.

1. Integrated technique of segmentation and classification methods with connected components analysis for road extraction from orthophoto images;Abdollahi;Expert Systems with Applications,2021

2. Urban vegetation mapping from aerial imagery using explainable AI (XAI);Abdollahi;Sensors,2021

3. An ensemble architecture of deep convolutional segnet and unet networks for building semantic segmentation from high-resolution aerial images;Abdollahi;Geocarto International,2020

4. RoadVecNet: a new approach for simultaneous road network segmentation and vectorization from aerial and google earth imagery in a complex urban set-up;Abdollahi;GIScience & Remote Sensing,2021

5. Building footprint extraction from high resolution aerial images using generative adversarial network (GAN) architecture;Abdollahi;IEEE Access,2020

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1. EUNet: Edge-UNet for Accurate Building Extraction and Edge Emphasis in Gaofen-7 Images;Remote Sensing;2024-06-29

2. Building Detection and Segmentation Method in Aerial Photograph Based on Mask R-CNN;2024 4th International Conference on Computer Communication and Artificial Intelligence (CCAI);2024-05-24

3. Multi-Scale Feature Fusion Attention Network for Building Extraction in Remote Sensing Images;Electronics;2024-02-28

4. Building rooftop extraction from aerial imagery using low complexity UNet variant models;Journal of Spatial Science;2024-02-06

5. Object segmentation for image indexing in large database;Journal of King Saud University - Computer and Information Sciences;2024-02