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Deep Neural Networks for Quantitative Damage Evaluation of Building Losses Using Aerial Oblique Images: Case Study on the Great Wall (China)
Research article (Remote Sensing, 2021) · cited 14× · AI/ML
Deep Neural Networks for Quantitative Damage Evaluation of Building Losses Using Aerial Oblique Images: Case Study on the Great Wall (China)
Summary
Deep Neural Networks for Quantitative Damage Evaluation of Building Losses Using Aerial Oblique Images: Case Study on the Great Wall (China) is a scholarly article[1].
Key Facts
Deep Neural Networks for Quantitative Damage Evaluation of Building Losses Using Aerial Oblique Images: Case Study on the Great Wall (China)'s instance of is recorded as scholarly article[2].
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APA4ort.xyz Knowledge Graph. (2026). Deep Neural Networks for Quantitative Damage Evaluation of Building Losses Using Aerial Oblique Images: Case Study on the Great Wall (China). Retrieved May 24, 2026, from https://4ort.xyz/entity/deep-neural-networks-for-quantitative-damage-evaluation-of-building-losses-using-aerial-oblique-images-case-study-on-the
MLA“Deep Neural Networks for Quantitative Damage Evaluation of Building Losses Using Aerial Oblique Images: Case Study on the Great Wall (China).” 4ort.xyz Knowledge Graph, 4ort.xyz, 24 May. 2026, https://4ort.xyz/entity/deep-neural-networks-for-quantitative-damage-evaluation-of-building-losses-using-aerial-oblique-images-case-study-on-the.
BibTeX@misc{4ortxyz_deep-neural-networks-for-quantitative-damage-evaluation-of-building-losses-using-aerial-oblique-images-case-study-on-the_2026, author = {{4ort.xyz Knowledge Graph}}, title = {{Deep Neural Networks for Quantitative Damage Evaluation of Building Losses Using Aerial Oblique Images: Case Study on the Great Wall (China)}}, year = {2026}, url = {https://4ort.xyz/entity/deep-neural-networks-for-quantitative-damage-evaluation-of-building-losses-using-aerial-oblique-images-case-study-on-the}, note = {Accessed: 2026-05-24}}
LLM promptAccording to 4ort.xyz Knowledge Graph (aggregator of Wikidata, Wikipedia, and authoritative open-data sources): Deep Neural Networks for Quantitative Damage Evaluation of Building Losses Using Aerial Oblique Images: Case Study on the Great Wall (China) — https://4ort.xyz/entity/deep-neural-networks-for-quantitative-damage-evaluation-of-building-losses-using-aerial-oblique-images-case-study-on-the (retrieved 2026-05-24)
