A new spatial affinity component for self-supervised pretraining leverages high-resolution imagery to enhance mid-resolution satellite image representations and segmentation performance.
Prithvi-eo-2.0: A versatile multi- temporal foundation model for earth observation applications,
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
fields
cs.CV 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
Transformer-based models deliver strong landslide segmentation on satellite images, and parameter-efficient fine-tuning matches full fine-tuning accuracy while cutting trainable parameters by up to 95%.
citing papers explorer
-
Cross-Scale Pretraining: Enhancing Self-Supervised Learning for Low-Resolution Satellite Imagery for Semantic Segmentation
A new spatial affinity component for self-supervised pretraining leverages high-resolution imagery to enhance mid-resolution satellite image representations and segmentation performance.
-
A Benchmark Study of Segmentation Models and Adaptation Strategies for Landslide Detection from Satellite Imagery
Transformer-based models deliver strong landslide segmentation on satellite images, and parameter-efficient fine-tuning matches full fine-tuning accuracy while cutting trainable parameters by up to 95%.