[Sympathisants1-haiti] ISTEAH

[Samuel Pierre]

Félicitations aux étudiants *Wougens Vincent* (mathématiques) et *Amos 
Noël* (génie informatique), ainsi qu’au professeur *Jean-Eugène Piou*!

Dirigé par le professeur associé à l’ISTEAH, *Jean-Eugène Piou, ing., 
Ph.D.*, du Massachusets Institute of Technology (MIT), le travail de 
recherche de deux étudiants de maîtrise de l’ISTEAH a fait l’objet de 
deux articles scientifiques dans une prestigieuse conférence de 
l’Institute of Electrical and Electronics Engineers (IEEE) à New York, 
du 26 au 29 novembre 2022. Toutes nos félicitations aux étudiants 
*Wougens Vincent* (mathématiques) et *Amos Noël* (génie informatique), 
ainsi qu’au professeur *Jean-Eugène Piou*!

_Voici un résumé du premier article en question_ :

*A Deep Learning Application to the 2004 Flooding of the City of 
Gonaïves in Haiti *

Wougens Vincent^1 , Amos Noel^1 and Jean E. Piou^2 , Senior Member, IEEE

1: Graduate Student at *Institut des Sciences, des Technologies et des 
Etudes Avancées d’Haïti (ISTEAH*), Saint-Marc, Haiti

2: Research Scientist in Cambridge, MA, USA

**

**

*Abstract:*

In this paper, a deep learning network is considered to study the impact 
of the flooding on the city of Gonaives that occurred on 18 September 
2004. Satellite images collected before and after the disaster are 
segmented then partitioned into 2 and 3 classes and later used to train 
and test the deep learning network, to estimate the damage that was 
inflicted upon the land topography and infrastructures of the city. 
Experiments conducted on the data revealed high performance of the 
network to identify areas buried in muddy water and regions of the city 
that did not suffer any damages. Data augmentation scenarios carried out 
on the training set translated into higher performance of the network on 
the test data than the result achieved when it was trained solely on the 
original images. The practicality of the deep learning to locate areas 
that were severely affected by the flood and estimate its impact on the 
residents of the city of Gonaïves is revealed.

*Published in: *2022 IEEE 13th Annual Ubiquitous Computing, Electronics 
& Mobile Communication Conference (UEMCON) 
<https://ieeexplore.ieee.org/xpl/conhome/9965569/proceeding>

*Date of Conference: *26-29 October 2022

*Date Added to IEEE /Xplore/: *01 December 2022

*ISBN Information:*

*DOI: *10.1109/UEMCON54665.2022.9965684 
<https://doi.org/10.1109/UEMCON54665.2022.9965684>

*Publisher: *IEEE

*Conference Location: *New York, NY, NY, USA

_Voici un résumé du deuxième article en question_ :


  *Analyses of 2010 and 2021 Earthquakes that Struck Haiti Using a Deep
  Learning Network*

Wougens Vincent^1 , Amos Noel^1 and Jean E. Piou^2 , Senior Member, IEEE

1: Graduate Student at *Institut des Sciences, des Technologies et des 
Etudes Avancées d’Haïti (ISTEAH*), Saint-Marc, Haiti

2: Research Scientist in Cambridge, MA, USA

*Abstract:*

Presented in this paper are results from analyses conducted on satellite 
imagery collected on the 12 January 2010 earthquake with a moment 
magnitude (Mw) of 7.0 that devastated Port-au-Prince, the capital of 
Haiti, and the 14 August 2021 earthquake with moment magnitude (Mw) of 
7.2 that struck the southwestern peninsula of Haiti. A deep learning 
network architecture that constitutes the main engine of this research 
investigation to carry out analyses from images collected before and 
after the earthquakes is considered. The images are segmented, labelled 
then randomly chosen to form training and testing datasets that are 
partitioned into 3 classes. Localization of regions deeply affected by 
the impacts of the earthquakes and identification of land areas, road 
infrastructures and buildings severely damaged are useful information 
that the DL network provides to demonstrate its effectiveness. Data 
augmentation on the training side is considered to enhance the 
performance of the DL network on testing and shows its usefulness to 
reduce performance accuracy error and make the DL network a suited 
component in damage assessment. So, in the future, the DL network can 
integrate recovery and relief efforts as a tool that one can leverage on 
to limit the impacts of natural disasters on human lives.

*Published in: *2022 IEEE 13th Annual Ubiquitous Computing, Electronics 
& Mobile Communication Conference (UEMCON) 
<https://ieeexplore.ieee.org/xpl/conhome/9965569/proceeding>

*Date of Conference: *26-29 October 2022

*Date Added to IEEE /Xplore/: *01 December 2022

*ISBN Information:*

*DOI: *10.1109/UEMCON54665.2022.9965694 
<https://doi.org/10.1109/UEMCON54665.2022.9965694>

*Publisher: *IEEE

*Conference Location: *New York, NY, NY, USA
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