3 papers
cs.CV2026
Better Together: Evaluating the Complementarity of Earth Embedding Models
Thijs L van der Plas, Jacob JW Bakermans, Vishal Nedungadi +3
Earth embedding models transform Earth observation data into embeddings uniquely tied to locations on the Earth's surface. These models are typically evaluated in isolation, compar…
cs.CV2026
Assessing the Effectiveness of Deep Embeddings for Tree Species Classification in the Dutch Forest Inventory
Takayuki Ishikawa, Carmelo Bonannella, Bas J. W. Lerink +2
National Forest Inventory (NFI) serves as the primary source of forest information, however, maintaining these inventories requires labor-intensive on-site campaigns by forestry ex…
cs.LG2025
Better, Not Just More: Data-Centric Machine Learning for Earth Observation
Ribana Roscher, Marc RuÃwurm, Caroline Gevaert +8
Recent developments and research in modern machine learning have led to substantial improvements in the geospatial field. Although numerous deep learning architectures and models h…