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Weakly Supervised Classification Using Group-Level Labels
Guruprasad Nayak, Rahul Ghosh, Xiaowei Jia +1
In many applications, finding adequate labeled data to train predictive models is a major challenge. In this work, we propose methods to use group-level binary labels as weak super…
Clustering augmented Self-Supervised Learning: Anapplication to Land Cover Mapping
Rahul Ghosh, Xiaowei Jia, Chenxi Lin +2
Collecting large annotated datasets in Remote Sensing is often expensive and thus can become a major obstacle for training advanced machine learning models. Common techniques of ad…
CalCROP21: A Georeferenced multi-spectral dataset of Satellite Imagery and Crop Labels
Rahul Ghosh, Praveen Ravirathinam, Xiaowei Jia +3
Mapping and monitoring crops is a key step towards sustainable intensification of agriculture and addressing global food security. A dataset like ImageNet that revolutionized compu…
Attention-augmented Spatio-Temporal Segmentation for Land Cover Mapping
Rahul Ghosh, Praveen Ravirathinam, Xiaowei Jia +3
The availability of massive earth observing satellite data provide huge opportunities for land use and land cover mapping. However, such mapping effort is challenging due to the ex…
Land Cover Mapping in Limited Labels Scenario: A Survey
Rahul Ghosh, Xiaowei Jia, Vipin Kumar
Land cover mapping is essential for monitoring global environmental change and managing natural resources. Unfortunately, traditional classification models are plagued by limited t…