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Found 7 papers, 2 papers with code
Neural Plasticity-Inspired Foundation Model for Observing the Earth Crossing Modalities
The development of foundation models has revolutionized our ability to interpret the Earth's surface using satellite observational data.
One for All: Toward Unified Foundation Models for Earth Vision
Current remote sensing foundation models typically specialize in a single modality or a specific spatial resolution range, limiting their versatility for downstream datasets.
Self-supervised Domain-agnostic Domain Adaptation for Satellite Images
A promising method to address this problem is domain adaptation, where the training and the testing datasets are split into two or multiple domains according to their distributions, and an adaptation method is applied to improve the generalizability of the model on the testing dataset.
Few-shot Object Detection in Remote Sensing: Lifting the Curse of Incompletely Annotated Novel Objects
In this context, few-shot object detection (FSOD) has emerged as a promising direction, which aims at enabling the model to detect novel objects with only few of them annotated.
EarthNets: Empowering AI in Earth Observation
Furthermore, a new platform for EO, termed EarthNets, is released to achieve a fair and consistent evaluation of deep learning methods on remote sensing data.
The Outcome of the 2022 Landslide4Sense Competition: Advanced Landslide Detection from Multi-Source Satellite Imagery
The objective of the competition is to automatically detect landslides based on large-scale multiple sources of satellite imagery collected globally.
Optimal Clustering Framework for Hyperspectral Band Selection
Band selection, by choosing a set of representative bands in hyperspectral image (HSI), is an effective method to reduce the redundant information without compromising the original contents.
