CNNs for remote extraction of urban features: A survey-driven benchmarking-Reference-Cited by-同舟云学术

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CNNs for remote extraction of urban features: A survey-driven benchmarking

Published:2024-12 Issue: Volume:255 Page:124751
ISSN:0957-4174
Container-title:Expert Systems with Applications
language:en
Short-container-title:Expert Systems with Applications

Author:

Neupane Bipul^ORCID,Aryal Jagannath^ORCID,Rajabifard Abbas^ORCID

Funder

University of Melbourne

Publisher

Elsevier BV

Reference216 articles.

1. An ensemble architecture of deep convolutional Segnet and Unet networks for building semantic segmentation from high-resolution aerial images;Abdollahi;Geocarto International,2022

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

3. Multi-scale feature map aggregation and supervised domain adaptation of fully convolutional networks for urban building footprint extraction;Aryal;Remote Sensing,2023

4. Beyond RGB: Very high resolution urban remote sensing with multimodal deep networks;Audebert;ISPRS Journal of Photogrammetry and Remote Sensing,2018

5. A joint Bayesian optimization for the classification of fine spatial resolution remotely sensed imagery using object-based convolutional neural networks;Azeez;Land,2022

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