scholarly journals Corrigendum to ‘A machine learning approach for estimation of shallow water depths from optical satellite images and sonar measurements’ [Journal of Hydroinformatics 15(4), 1408–1424. https://doi.org/10.2166/hydro.2013.234]

2019 ◽  
Keyword(s):  
Machine Learning ◽  
Shallow Water ◽  
Satellite Images ◽  
Learning Approach ◽  
Machine Learning Approach ◽  
Optical Satellite Images ◽  
Water Depths

A Machine Learning Approach to Cloud Masking in Sentinel-3 SLSTR Data

2020 ◽  
Author(s):  
Samuel Jackson ◽  
Jeyarajan Thiyagalingam ◽  
Caroline Cox
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Land Surface ◽  
Satellite Images ◽  
Binary Classification ◽  
Gradient Boosting ◽  
Learning Approach ◽  
Persistent Problem ◽  
Machine Learning Approach ◽  
Level 1

<p><span>Clouds appear ubiquitously in the Earth's atmosphere, and thus present a persistent problem for the accurate retrieval of remotely sensed information. The task of identifying which pixels are cloud, and which are not, is what we refer as the cloud masking problem. The task of cloud masking essentially boils down to assigning a binary label, representing either "cloud" or "clear", to each pixel. </span></p><p><span>Although this problem appears trivial, it is often complicated by a diverse number of issues that affect the imagery obtained from remote sensing instruments. For instance, snow, sea ice, dust, smoke, and sun glint can easily challenge the robustness and consistency of any cloud masking algorithm. The cloud masking problem is also further complicated by geographic and seasonal variation in acquired scenes. </span></p><p><span>In this work, we present a machine learning approach to handle the problem of cloud masking for the Sea and Land Surface Temperature Radiometer (SLSTR) on board the Sentinel-3 satellites. Our model uses Gradient Boosting Decision Trees (GBDTs), to perform pixel-wise segmentation of satellite images. The model is trained using a hand labelled dataset of ~12,000 individual pixels covering both the spatial and temporal domains of the SLSTR instrument and utilises the combined channels of the dual-view swaths. Pixel level annotations, while lacking spatial context, have the advantage of being cheaper to obtain compared to fully labelled images, a major problem in applying machine learning to remote sensing imagrey.</span></p><p><span>We validate the performance of our mask using cross validation and compare its performance with two baseline models provided in the SLSTR level 1 product. We show up to 10% improvement in binary classification accuracy compared with the baseline methods. Additionally, we show that our model has the ability to distinguish between different classes of cloud to reasonable accuracy.</span></p>

A Machine-Learning Approach to Removing Highly Variable Surface-Waves for a Large Shallow-Water Survey Offshore Abu Dhabi

2020 ◽  
Author(s):  
Al Hajeri Badreia ◽  
Cambois Guillaume ◽  
Mahgoub Mohamed ◽  
Al Sarmi Mohamed ◽  
Domont Sonia ◽  
...  
Keyword(s):  
Machine Learning ◽  
Surface Waves ◽  
Shallow Water ◽  
Abu Dhabi ◽  
Learning Approach ◽  
Variable Surface ◽  
Machine Learning Approach

1552-P: Machine Learning Approach to Decision-Making for Initial Insulin Use in Japanese Patients with Type 2 Diabetes

Diabetes ◽  
2020 ◽  
Vol 69 (Supplement 1) ◽  
pp. 1552-P
Author(s):  
KAZUYA FUJIHARA ◽  
MAYUKO H. YAMADA ◽  
YASUHIRO MATSUBAYASHI ◽  
MASAHIKO YAMAMOTO ◽  
TOSHIHIRO IIZUKA ◽  
...  
Keyword(s):  
Machine Learning ◽  
Type 2 Diabetes ◽  
Decision Making ◽  
Japanese Patients ◽  
Learning Approach ◽  
Machine Learning Approach ◽  
Insulin Use

A Machine Learning Approach to Jet-Surface Interaction Noise Modeling

2020 ◽  
Author(s):  
Clifford A. Brown ◽  
Jonny Dowdall ◽  
Brian Whiteaker ◽  
Lauren McIntyre
Keyword(s):  
Machine Learning ◽  
Surface Interaction ◽  
Learning Approach ◽  
Noise Modeling ◽  
Machine Learning Approach
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