5 papers
LandSegmenter: Towards a Flexible Foundation Model for Land Use and Land Cover Mapping
Chenying Liu, Wei Huang, Xiao Xiang Zhu
Land Use and Land Cover (LULC) mapping is a fundamental task in Earth Observation (EO). However, current LULC models are typically developed for a specific modality and a fixed cla…
Adaptive Gradient Calibration for Single-Positive Multi-Label Learning in Remote Sensing Image Scene Classification
Chenying Liu, Gianmarco Perantoni, Lorenzo Bruzzone +1
Multi-label classification (MLC) offers a more comprehensive semantic understanding of Remote Sensing (RS) imagery compared to traditional single-label classification (SLC). Howeve…
Hierarchical Semi-Supervised Active Learning for Remote Sensing
Wei Huang, Zhitong Xiong, Chenying Liu +1
The performance of deep learning models in remote sensing (RS) strongly depends on the availability of high-quality labeled data. However, collecting large-scale annotations is cos…
Towards a Unified Copernicus Foundation Model for Earth Vision
Yi Wang, Zhitong Xiong, Chenying Liu +8
Advances in Earth observation (EO) foundation models have unlocked the potential of big satellite data to learn generic representations from space, benefiting a wide range of downs…
CromSS: Cross-modal pre-training with noisy labels for remote sensing image segmentation
Chenying Liu, Conrad Albrecht, Yi Wang +1
We explore the potential of large-scale noisily labeled data to enhance feature learning by pretraining semantic segmentation models within a multi-modal framework for geospatial a…