Proposes ISensD and ESensI methods to increase robustness of multi-sensor EO models to missing sensors, with experiments on three temporal datasets showing ensemble models are most robust.
Title resolution pending
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
years
2024 2verdicts
UNVERDICTED 2representative citing papers
SAM achieves ~58% accuracy delineating field boundaries from SkySat imagery without training, with gains from multi-date inputs and varied sizes, establishing proof-of-concept for data-scarce agriculture mapping.
citing papers explorer
-
Increasing the Robustness of Model Predictions to Missing Sensors in Earth Observation
Proposes ISensD and ESensI methods to increase robustness of multi-sensor EO models to missing sensors, with experiments on three temporal datasets showing ensemble models are most robust.
-
Investigating the Segment Anything Foundation Model for Mapping Smallholder Agriculture Field Boundaries Without Training Labels
SAM achieves ~58% accuracy delineating field boundaries from SkySat imagery without training, with gains from multi-date inputs and varied sizes, establishing proof-of-concept for data-scarce agriculture mapping.