Small Data, Big Maps: Training Geospatial ML Models When Samples Are Scarce

# Training AI Models When Real-World Data Is Hard to Get Satellite and aerial images are plentiful and cheap, but the actual ground-truth information that trains AI to interpret them—like whether a particular patch of land is forest or farmland—is expensive and difficult to collect. Researchers are developing techniques to build accurate AI mapping systems without needing massive amounts of this costly labeled data, which could make geospatial AI tools more accessible and affordable for environmental monitoring, urban planning, and other real-world applications.