A Residual-Inception U-Net (RIU-Net) Approach and Comparisons with U-Shaped CNN and Transformer Models for Building Segmentation from High-Resolution Satellite Images

A Residual-Inception U-Net (RIU-Net) Approach and Comparisons with U-Shaped CNN and Transformer Models for Building Segmentation from High-Resolution Satellite Images

Building segmentation is crucial for applications extending from map production to urban planning. Nowadays, it is still a challenge due to CNNs’ inability to model global context and Transformers’ high memory need. In this study, 10 CNN and Transformer models were generated, and comparisons were re...

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Bibliographic Details
Main Authors: Batuhan Sariturk, Dursun Zafer Seker
Format: Article
Language:English
Published: MDPI AG 2022-10-01
Series:Sensors
Subjects:
building segmentation
CNN
Transformer
Inception
residual connections
satellite images
Online Access:https://www.mdpi.com/1424-8220/22/19/7624

Internet

https://www.mdpi.com/1424-8220/22/19/7624

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