Land-use/cover classification in a heterogeneous coastal landscape using RapidEye imagery: evaluating the performance of random forest and support vector machines classifiers
Elhadi Adam; Onisimo Mutanga; John Odindi; Elfatih M. Abdel‐Rahman · 2014 · International Journal of Remote Sensing
WASTE classifies this as Negative / Null Result Report · AI classification, approximate
The study found no significant effect — useful as a negative control or null benchmark for your own design.
Abstract (excerpt)
Mapping of patterns and spatial distribution of land-use/cover (LULC) has long been based on remotely sensed data. In the recent past, efforts to improve the reliability of LULC maps have seen a proliferation of image classification…
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Metadata source: OpenAlex · DOI 10.1080/01431161.2014.903435