Scale-mae: A scale-aware masked autoencoder for multiscale geospatial representation learning
Large, pretrained models are commonly finetuned with imagery that is heavily augmented to
mimic different conditions and scales, with the resulting models used for various tasks with …
mimic different conditions and scales, with the resulting models used for various tasks with …
Creating xBD: A dataset for assessing building damage from satellite imagery
We present a preliminary report for xBD, a new large-scale dataset for the advancement of
change detection and building damage assessment for humanitarian assistance and …
change detection and building damage assessment for humanitarian assistance and …
Quantified Task Misalignment to Inform PEFT: An Exploration of Domain Generalization and Catastrophic Forgetting in CLIP
Foundations models are presented as generalists that often perform well over a myriad of
tasks. Fine-tuning these models, even on limited data, provides an additional boost in task …
tasks. Fine-tuning these models, even on limited data, provides an additional boost in task …
[PDF][PDF] Scale-MAE: A Scale-Aware Masked Autoencoder for Multiscale Geospatial Representation Learning
Large, pretrained models are commonly finetuned with imagery that is heavily augmented to
mimic different conditions and scales, with the resulting models used for various tasks with …
mimic different conditions and scales, with the resulting models used for various tasks with …