22 citations
- Jet Propulsion LaboratoryUS2 papers
- Adnet Systems (United States)US1 paper
- ARC Centre of Excellence for Mathematical and Statistical FrontiersAU1 paper
- Center for Astrophysics Harvard & SmithsonianUS1 paper
- Goddard Space Flight CenterUS1 paper
- Langley Research CenterUS1 paper
- Queensland University of TechnologyAU1 paper
- The Institute of Statistical MathematicsJP1 paper
- The University of TokyoJP1 paper
- Tokyo University of Information SciencesJP1 paper
- University at Albany, State University of New YorkUS1 paper
- University of New EnglandAU1 paper
4 papers
Harnessing Self-Supervised Deep Learning and Geostationary Remote Sensing for Advancing Wildfire and Associated Air Quality Monitoring: Improved Smoke and Fire Front Masking using GOES and TEMPO Radiance Data
Nicholas LaHaye, Thilanka Munashinge, Hugo Lee +4
This work demonstrates the possibilities for improving wildfire and air quality management in the western United States by leveraging the unprecedented hourly data from NASA's TEMP…
Fusing Multi- and Hyperspectral Satellite Data for Harmful Algal Bloom Monitoring with Self-Supervised and Hierarchical Deep Learning
Nicholas LaHaye, Kelly M. Luis, Michelle M. Gierach
We present a self-supervised machine learning framework for detecting and mapping the severity and speciation of harmful algal blooms (HABs) using multi-sensor satellite data. By f…
A Unified Method for Improved Inference in Random-effects Meta-analysis
Shonosuke Sugasawa, Hisashi Noma
Random-effects meta-analyses have been widely applied in evidence synthesis for various types of medical studies. However, standard inference methods (e.g. restricted maximum likel…
Ultrahigh Dimensional Variable Selection for Mapping Soil Carbon
Benjamin R. Fitzpatrick, David W. Lamb, Kerrie Mengersen
Modern soil mapping is characterised by the need to interpolate samples of geostatistical response observations and the availability of relatively large numbers of environmental ch…