5 papers · 1 filter
Towards Knowledge Guided Pretraining Approaches for Multimodal Foundation Models: Applications in Remote Sensing
Praveen Ravirathinam, Ajitesh Parthasarathy, Ankush Khandelwal +2
Self-supervised learning has emerged as a powerful paradigm for pretraining foundation models using large-scale data. Existing pretraining approaches predominantly rely on masked r…
Combining Satellite and Weather Data for Crop Type Mapping: An Inverse Modelling Approach
Praveen Ravirathinam, Rahul Ghosh, Ankush Khandelwal +3
Accurate and timely crop mapping is essential for yield estimation, insurance claims, and conservation efforts. Over the years, many successful machine learning models for crop map…
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…