A machine learning algorithm to detect fog from space

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dc.contributor.authorSvensson, Kevin
dc.contributor.authorJohansson, Nils
dc.contributor.departmentChalmers tekniska högskola / Institutionen för rymd-, geo- och miljövetenskapsv
dc.contributor.departmentChalmers University of Technology / Department of Space, Earth and Environmenten
dc.contributor.examinerEriksson, Patrick
dc.contributor.supervisorCeccobello, Chiara
dc.date.accessioned2024-08-20T05:50:04Z
dc.date.available2024-08-20T05:50:04Z
dc.date.issued2024
dc.date.submitted
dc.description.abstractFog detection is important for traffic safety. Detecting fog using machine learning on satellite data has been researched before, but not on a global scale using syn thetic data. The aim of the thesis is to use a synthetic dataset of simulated MODIS satellite data to determine the viability of machine learning algorithms for detecting fog in satellite images. The synthetic dataset we use is simulated using a fast ra diative transfer model called RTTOV by inputting various atmospheric information for different conditions. The dataset is tabular and no spatial or temporal relation ship exists between the data points meaning each pixel is treated independently. We use the synthetic data to train and evaluate numerous machine learning models including various implementations of XGBoost and feed forward deep neural net works. We also apply a model trained on synthetic data to a real MODIS image. We demonstrate that classification models can achieve good recall values on synthetic data when oversampling fog in the training data, the best being 0.87 recall with a deep neural network. However, we find that this comes at the cost of a large amount of false positives evident by the low precision value of 0.27. It is concluded that no model performed satisfactory results for replacing existing methods of fog detection. We identify the acquisition of supplemental labeled real satellite images as a possi bility for future improvement, allowing for spatial analysis which is impossible with the independent pixels of the synthetic dataset alone. However, this is a non-trivial task due to the challenges in obtaining and labeling a sufficiently large and diverse dataset of real satellite images.
dc.identifier.coursecodeseex30
dc.identifier.urihttp://hdl.handle.net/20.500.12380/308421
dc.language.isoeng
dc.setspec.uppsokLifeEarthScience
dc.subjectMODIS, fog, machine learning, nowcasting
dc.titleA machine learning algorithm to detect fog from space
dc.type.degreeExamensarbete för masterexamensv
dc.type.degreeMaster's Thesisen
dc.type.uppsokH
local.programmeComplex adaptive systems (MPCAS), MSc
local.programmeComputer science – algorithms, languages and logic (MPALG), MSc
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