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DE-Net: Deep Encoding Network for Building Extraction from High-Resolution Remote Sensing Imagery paper by Independent / Community
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Paper

DE-Net: Deep Encoding Network for Building Extraction from High-Resolution Remote Sensing Imagery

by Independent / Community 006176244e7819623a3753b2e709f19ce7b8dff5
Free2AITools Nexus Index
69.3
S: Semantic 50

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A: Authority 85
P: Popularity 61
R: Recency 100
Q: Quality 65
Tech Context
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Deep convolutional neural networks have promoted significant progress in building extraction from high-resolution remote sensing imagery. Although most of such work focuses on modifying existing image segmentation networks in computer vision, we propose a new network in this paper, Deep Encoding Network (DE-Net), that is designed for the very problem based on many lately introduced techniques in image segmentation. Four modules are used to construct DE-Net: the inceptionstyle downsampling mod...

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Semantic Scholar 63 Citations
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Registry ID 006176244e7819623a3753b2e709f19ce7b8dff5
License ArXiv
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BibTeX 
@misc{006176244e7819623a3753b2e709f19ce7b8dff5,
  author = {Unknown},
  title = {DE-Net: Deep Encoding Network for Building Extraction from High-Resolution Remote Sensing Imagery Paper},
  year = {2026},
  howpublished = {\url{https://api.semanticscholar.org/006176244e7819623a3753b2e709f19ce7b8dff5}},
  note = {Accessed via Free2AITools.}
}
APA Style
Unknown. (2026). DE-Net: Deep Encoding Network for Building Extraction from High-Resolution Remote Sensing Imagery [Paper]. Free2AITools. https://api.semanticscholar.org/006176244e7819623a3753b2e709f19ce7b8dff5

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βš–οΈ Free2AITools Nexus Index V2.0

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Semantic (S) 50

Query-time baseline · scored live at search

Authority (A) 85
Popularity (P) 61
Recency (R) 100
Quality (Q) 65

πŸ’¬ Index Insight

FNI V2.0 for DE-Net: Deep Encoding Network for Building Extraction from High-Resolution Remote Sensing Imagery: Authority (A:85), Popularity (P:61), Recency (R:100), Quality (Q:65). Semantic (S) is a query-time baseline scored live at search.

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πŸ“ Executive Summary

"Deep convolutional neural networks have promoted significant progress in building extraction from high-resolution remote sensing imagery. Although most of such work focuses on modifying existing image segmentation networks in computer vision, we propose a new network in this paper, Deep Encoding Network (DE-Net), that is designed for the very problem based on many lately introduced techniques in image segmentation. Four modules are used to construct DE-Net: the inceptionstyle downsampling mod..."

❝ Cite Node 

@article{Unknown2026DE-Net:,
  title={DE-Net: Deep Encoding Network for Building Extraction from High-Resolution Remote Sensing Imagery},
  author={},
  note={Indexed by Free2AITools},
  year={2026}
}

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πŸ“Š Research Signals

πŸ“ˆ63CitationsSemantic Scholar
πŸ›οΈ85AuthorityFNI pillar
⏱️100RecencyFNI pillar
βœ…65QualityFNI pillar
πŸ—‚οΈautomation workflowField

🏷️ Research Topics

vision modelsimage generation
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semantic_scholar
author
Unknown
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ArXiv
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paper, research, academic

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