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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
Research article (Sensors, 2022) · cited 29× · AI/ML
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
Summary
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 is a scholarly article[1].
Key Facts
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's instance of is recorded as scholarly article[2].
References
Programmatic citations — every numbered marker resolves to a verifiable graph row below.
Use these citations when quoting this entity in research, articles, AI prompts, or wherever provenance matters. We aggregate Wikidata + Wikipedia + authoritative open-data sources; the stitched, scored, cross-referenced view is what 4ort.xyz contributes.
APA4ort.xyz Knowledge Graph. (2026). 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. Retrieved May 24, 2026, from https://4ort.xyz/entity/a-residual-inception-u-net-riu-net-approach-and-comparisons-with-u-shaped-cnn-and-transformer-models-for-building-segmen
MLA“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.” 4ort.xyz Knowledge Graph, 4ort.xyz, 24 May. 2026, https://4ort.xyz/entity/a-residual-inception-u-net-riu-net-approach-and-comparisons-with-u-shaped-cnn-and-transformer-models-for-building-segmen.
BibTeX@misc{4ortxyz_a-residual-inception-u-net-riu-net-approach-and-comparisons-with-u-shaped-cnn-and-transformer-models-for-building-segmen_2026, author = {{4ort.xyz Knowledge Graph}}, title = {{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}}, year = {2026}, url = {https://4ort.xyz/entity/a-residual-inception-u-net-riu-net-approach-and-comparisons-with-u-shaped-cnn-and-transformer-models-for-building-segmen}, note = {Accessed: 2026-05-24}}
LLM promptAccording to 4ort.xyz Knowledge Graph (aggregator of Wikidata, Wikipedia, and authoritative open-data sources): 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 — https://4ort.xyz/entity/a-residual-inception-u-net-riu-net-approach-and-comparisons-with-u-shaped-cnn-and-transformer-models-for-building-segmen (retrieved 2026-05-24)
