11 citations · 20 across the 15 of their papers we have counts for
6 papers · 1 filter
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…
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…
Automated Monitoring Cropland Using Remote Sensing Data: Challenges and Opportunities for Machine Learning
Xiaowei Jia, Ankush Khandelwal, Vipin Kumar
This paper provides an overview of how recent advances in machine learning and the availability of data from earth observing satellites can dramatically improve our ability to auto…