Machine Learning Maps Habitat Loss Drivers in Zambia
A Random Forest model identifies cultivated land and proximity to settlements as the primary drivers of habitat loss in Zambia's ...
A Random Forest model identifies cultivated land and proximity to settlements as the primary drivers of habitat loss in Zambia's ...
Researchers have unveiled MiRAGE, a parameter-efficient AI framework that adapts its computational depth to each satellite image and uses CLIP ...
A newly launched journal outlines how environmental DNA, sensor networks, artificial intelligence, and community engagement could transform the monitoring and ...
A new national-scale mapping effort using high-resolution satellite imagery has produced Indonesia's first government-endorsed extent maps of coral reefs, seagrass ...
A new review argues that the optical fingerprints of dissolved organic matter in the hypersaline Arabian Gulf could transform ecosystem ...
A TRACE2025 keynote shows how satellite remote sensing, fused with field data and machine learning, is transforming the study of ...
A 2003 ASTER image predicted the exact locations of iron pits opened years later around Keban, revealing how band ratios, ...
Using Sentinel-2 imagery, object-based image analysis and a random forest classifier, researchers produced the first high-accuracy map of Bai Tu ...
A collaboration between an Italian art high school and CNR researchers shows how satellite imagery and environmental data can be ...
A new study of northern Hebei, China, reveals that forest and grassland gains and losses produce unequal and sometimes reversed ...
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© 2025 Scienmag - Science Magazine