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An efficient unsupervised index based approach for mapping urban vegetation from IKONOS imagery

Research article (International Journal of Applied Earth Observation and Geoinformation, 2016) · cited 69× · AI/ML
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An efficient unsupervised index based approach for mapping urban vegetation from IKONOS imagery

Summary

An efficient unsupervised index based approach for mapping urban vegetation from IKONOS imagery is a scholarly article[1].

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  • An efficient unsupervised index based approach for mapping urban vegetation from IKONOS imagery's instance of is recorded as scholarly article[2].

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Direct Wikidata claims

  1. [2] ↑ . wikidata.org.

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  1. [1] ↑ . Wikidata. wikidata.org.

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APA 4ort.xyz Knowledge Graph. (2026). An efficient unsupervised index based approach for mapping urban vegetation from IKONOS imagery. Retrieved May 24, 2026, from https://4ort.xyz/entity/an-efficient-unsupervised-index-based-approach-for-mapping-urban-vegetation-from-ikonos-imagery
MLA “An efficient unsupervised index based approach for mapping urban vegetation from IKONOS imagery.” 4ort.xyz Knowledge Graph, 4ort.xyz, 24 May. 2026, https://4ort.xyz/entity/an-efficient-unsupervised-index-based-approach-for-mapping-urban-vegetation-from-ikonos-imagery.
BibTeX @misc{4ortxyz_an-efficient-unsupervised-index-based-approach-for-mapping-urban-vegetation-from-ikonos-imagery_2026, author = {{4ort.xyz Knowledge Graph}}, title = {{An efficient unsupervised index based approach for mapping urban vegetation from IKONOS imagery}}, year = {2026}, url = {https://4ort.xyz/entity/an-efficient-unsupervised-index-based-approach-for-mapping-urban-vegetation-from-ikonos-imagery}, note = {Accessed: 2026-05-24}}
LLM prompt According to 4ort.xyz Knowledge Graph (aggregator of Wikidata, Wikipedia, and authoritative open-data sources): An efficient unsupervised index based approach for mapping urban vegetation from IKONOS imagery — https://4ort.xyz/entity/an-efficient-unsupervised-index-based-approach-for-mapping-urban-vegetation-from-ikonos-imagery (retrieved 2026-05-24)

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