activity
20172022
most citedAutomated Monitoring Cropland Using Remote Sensing Data: Challenges and Opportunities for Machine Learning

1 citations · 3 across the 8 of their papers we have counts for

collaborators

15 papers

cs.LG20211 cited

Heterogeneous Stream-reservoir Graph Networks with Data Assimilation

Shengyu Chen, Alison Appling, Samantha Oliver +5

Accurate prediction of water temperature in streams is critical for monitoring and understanding biogeochemical and ecological processes in streams. Stream temperature is affected…

cs.LG2021

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…

cs.CV2021

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…

cs.CV2021

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…

cs.CV2021

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…

cs.LG2021

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…