Home ›
Entities
› academia
› Generalizing machine learning regression models using multi-site spectral libraries for mapping vegetation-impervious-soil fractions across multiple cities
Generalizing machine learning regression models using multi-site spectral libraries for mapping vegetation-impervious-soil fractions across multiple cities
Research article (Remote Sensing of Environment, 2018) · cited 52× · AI/ML
Generalizing machine learning regression models using multi-site spectral libraries for mapping vegetation-impervious-soil fractions across multiple cities
Summary
Generalizing machine learning regression models using multi-site spectral libraries for mapping vegetation-impervious-soil fractions across multiple cities is a scholarly article[1].
Key Facts
Generalizing machine learning regression models using multi-site spectral libraries for mapping vegetation-impervious-soil fractions across multiple cities'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). Generalizing machine learning regression models using multi-site spectral libraries for mapping vegetation-impervious-soil fractions across multiple cities. Retrieved May 24, 2026, from https://4ort.xyz/entity/generalizing-machine-learning-regression-models-using-multi-site-spectral-libraries-for-mapping-vegetation-impervious-so
MLA“Generalizing machine learning regression models using multi-site spectral libraries for mapping vegetation-impervious-soil fractions across multiple cities.” 4ort.xyz Knowledge Graph, 4ort.xyz, 24 May. 2026, https://4ort.xyz/entity/generalizing-machine-learning-regression-models-using-multi-site-spectral-libraries-for-mapping-vegetation-impervious-so.
BibTeX@misc{4ortxyz_generalizing-machine-learning-regression-models-using-multi-site-spectral-libraries-for-mapping-vegetation-impervious-so_2026, author = {{4ort.xyz Knowledge Graph}}, title = {{Generalizing machine learning regression models using multi-site spectral libraries for mapping vegetation-impervious-soil fractions across multiple cities}}, year = {2026}, url = {https://4ort.xyz/entity/generalizing-machine-learning-regression-models-using-multi-site-spectral-libraries-for-mapping-vegetation-impervious-so}, note = {Accessed: 2026-05-24}}
LLM promptAccording to 4ort.xyz Knowledge Graph (aggregator of Wikidata, Wikipedia, and authoritative open-data sources): Generalizing machine learning regression models using multi-site spectral libraries for mapping vegetation-impervious-soil fractions across multiple cities — https://4ort.xyz/entity/generalizing-machine-learning-regression-models-using-multi-site-spectral-libraries-for-mapping-vegetation-impervious-so (retrieved 2026-05-24)
