A framework applies frequent itemset mining with the negFIN algorithm and unsupervised learning to identify cities sharing co-occurring land use patterns from Copernicus Urban Atlas data.
Soil salinity detection from satellite image analysis
2 Pith papers cite this work. Polarity classification is still indexing.
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A machine-learning framework maps soil salinity in Satkhira, Bangladesh, from field samples and Landsat indices, revealing expanding moderate-to-high salinity zones over the past decade.
citing papers explorer
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Exploring Urban Land Use Patterns by Pattern Mining and Unsupervised Learning
A framework applies frequent itemset mining with the negFIN algorithm and unsupervised learning to identify cities sharing co-occurring land use patterns from Copernicus Urban Atlas data.
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A Dynamic Learning Observatory Reveals the Rapid Salinization of Satkhira, Bangladesh
A machine-learning framework maps soil salinity in Satkhira, Bangladesh, from field samples and Landsat indices, revealing expanding moderate-to-high salinity zones over the past decade.