88 citations · 385 across the 34 of their papers we have counts for
35 papers
UAV3DCrop: Benchmarking 3D Reconstruction in Repeated Multi-Angle UAV Crop Surveys
Junxiong Zhou, Xuechen Li, Chonghao Qiu +11
Accurate 3D crop monitoring underpins data-driven precision agriculture by enabling field-scale analysis of plant structure, growth dynamics, and management response. Modern 3D rec…
Estimating Mutual Information between Time Series and Temporal Event Sequences Across Diverse Analysis Tasks
Haoji Hu, Huaqing Mao, Yijun Lin +4
Pairwise dependence measures such as correlation and causality are fundamental to temporal data mining, yet there is still no principled and robust way to quantify dependence betwe…
Hierarchical Conditional Multi-Task Learning for Streamflow Modeling
Shaoming Xu, Arvind Renganathan, Ankush Khandelwal +9
Streamflow, vital for water resource management, is governed by complex hydrological systems involving intermediate processes driven by meteorological forces. While deep learning m…
ExoTST: Exogenous-Aware Temporal Sequence Transformer for Time Series Prediction
Kshitij Tayal, Arvind Renganathan, Xiaowei Jia +2
Accurate long-term predictions are the foundations for many machine learning applications and decision-making processes. Traditional time series approaches for prediction often foc…
Knowledge-guided Machine Learning: Current Trends and Future Prospects
Anuj Karpatne, Xiaowei Jia, Vipin Kumar
This paper presents an overview of scientific modeling and discusses the complementary strengths and weaknesses of ML methods for scientific modeling in comparison to process-based…
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…
Koopman Invertible Autoencoder: Leveraging Forward and Backward Dynamics for Temporal Modeling
Kshitij Tayal, Arvind Renganathan, Rahul Ghosh +2
Accurate long-term predictions are the foundations for many machine learning applications and decision-making processes. However, building accurate long-term prediction models rema…
Time Series Predictions in Unmonitored Sites: A Survey of Machine Learning Techniques in Water Resources
Jared D. Willard, Charuleka Varadharajan, Xiaowei Jia +1
Prediction of dynamic environmental variables in unmonitored sites remains a long-standing challenge for water resources science. The majority of the world's freshwater resources h…
Entity Aware Modelling: A Survey
Rahul Ghosh, Haoyu Yang, Ankush Khandelwal +5
Personalized prediction of responses for individual entities caused by external drivers is vital across many disciplines. Recent machine learning (ML) advances have led to new stat…
STORM-GAN: Spatio-Temporal Meta-GAN for Cross-City Estimation of Human Mobility Responses to COVID-19
Han Bao, Xun Zhou, Yiqun Xie +2
Human mobility estimation is crucial during the COVID-19 pandemic due to its significant guidance for policymakers to make non-pharmaceutical interventions. While deep learning app…
Mapping smallholder cashew plantations to inform sustainable tree crop expansion in Benin
Leikun Yin, Rahul Ghosh, Chenxi Lin +9
Cashews are grown by over 3 million smallholders in more than 40 countries worldwide as a principal source of income. As the third largest cashew producer in Africa, Benin has near…
Task-Adaptive Meta-Learning Framework for Advancing Spatial Generalizability
Zhexiong Liu, Licheng Liu, Yiqun Xie +2
Spatio-temporal machine learning is critically needed for a variety of societal applications, such as agricultural monitoring, hydrological forecast, and traffic management. These…
Realization of Causal Representation Learning to Adjust Confounding Bias in Latent Space
Jia Li, Xiang Li, Xiaowei Jia +2
Causal DAGs(Directed Acyclic Graphs) are usually considered in a 2D plane. Edges indicate causal effects' directions and imply their corresponding time-passings. Due to the natural…
Mini-Batch Learning Strategies for modeling long term temporal dependencies: A study in environmental applications
Shaoming Xu, Ankush Khandelwal, Xiang Li +9
In many environmental applications, recurrent neural networks (RNNs) are often used to model physical variables with long temporal dependencies. However, due to mini-batch training…
Graph-Augmented Cyclic Learning Framework for Similarity Estimation of Medical Clinical Notes
Can Zheng, Yanshan Wang, Xiaowei Jia
Semantic textual similarity (STS) in the clinical domain helps improve diagnostic efficiency and produce concise texts for downstream data mining tasks. However, given the high deg…
Modeling Reservoir Release Using Pseudo-Prospective Learning and Physical Simulations to Predict Water Temperature
Xiaowei Jia, Shengyu Chen, Yiqun Xie +4
This paper proposes a new data-driven method for predicting water temperature in stream networks with reservoirs. The water flows released from reservoirs greatly affect the water…
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…
Robust Inverse Framework using Knowledge-guided Self-Supervised Learning: An application to Hydrology
Rahul Ghosh, Arvind Renganathan, Kshitij Tayal +6
Machine Learning is beginning to provide state-of-the-art performance in a range of environmental applications such as streamflow prediction in a hydrologic basin. However, buildin…
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…
Physics Guided Machine Learning Methods for Hydrology
Ankush Khandelwal, Shaoming Xu, Xiang Li +5
Streamflow prediction is one of the key challenges in the field of hydrology due to the complex interplay between multiple non-linear physical mechanisms behind streamflow generati…