Omdena Chapter Page: Mexico

Omdena Mexico Chapter - Omdena Chapters

We will be running an AI project soon…. Stay Tuned!

Upcoming projects

Completed projects

Politics Fake News Detector in LATAM (Latin America)

The Background

Since the Cambridge Analytica scandal a pandora box has been opened around the world, bringing to light campaigns even involving our current Latinamerica leaders manipulating public opinion through social media to win an election. There is a common and simple pattern that includes platforms such as facebook and fake news, where the candidates are able to build a nefarious narrative for their own benefit. This fact is a growing concern for our democracies, as many of these practices have been widely spread across the region and more people are gaining access to the internet. Thus, it is a necessity to be able to advise the population, and for that we have to be able to quickly spot these plots on the net before the damage is irreversible.


The Problem


Once the capacity to somewhat detect irregularities in the news activity on the internet is developed, we might be able to counter the disinformation with the help of additional research. As we reduce the time spent in looking for those occurrences, more time can be used in validating the results and uncovering the truth; enabling researchers, journalists and organizations to help people make an informed decision whether the public opinion is true or not, so that they can identify on their own if someone is trying to manipulate them for a certain political benefit.

If  this matter isn’t tackled with enough urgency, we might see the rise of a new dark era in latin america politics, where many unscrupulous parties and people will manage to gain power and control the lives of many people. Therefore, the results of the project can provide support for both private and public companies on their future analysis and activities. Additionally, researchers and students could use the outcomes for their own research or use it for learning purposes.


Una vez contemos con la capacidad de detectar irregularidades en las noticias por internet, seremos capaces de contrarrestar la desinformación con la ayuda de investigaciones adicionales. Mientras reducimos el tiempo invertido en identificar estos patrones, podemos dedicar más tiempo a validar los resultados y buscar las verdades ocultas; habilitando a los investigadores, periodistas y organizaciones a que  ayuden a la población a tomar decisiones informados de la veracidad de la opinión pública, y que estos puedan identificar si alguien está tratando de manipularlos para beneficio político.

Si este problema no se trata con urgencia, podríamos ver el resurgir de una era oscura en la política latina, donde muchos partidos y personajes inescrupulosos tomarán el poder y el control de la vida de las personas. Por lo tanto, los resultados del proyecto pueden ser de provecho para los análisis y actividades futuros de entidades tanto públicas como privadas. Además de que tanto estudiantes como investigadores pueden hacer uso de los entregables para sus propias investigaciones y/o aprendizaje.


The Project Goals

To gather and clean datasets from different newspapers and new outlets in LATAM.

– To predict if there is a political affiliation in a certain topic on the news.

– To compare and determine if there is any irregularity between the information available respecting an specific news in the different news sources

– To understand and visualize the information patterns from the news by creating a visualization dashboard.


The Learning Outcomes

  • 1. How to gather and clean text datasets from news for data modeling.
  • 2. How to use data visualization tools for further app creation and data reporting.
  • 3. How to create a classification model with NLP libraries.


The Tasks & Timeline


Week 1 Week 2 Week 3 Week 4


– Data collection


– Data collection

– Data cleaning





– Topics Analysis

– Data cleaning



– Unsupervised Model Creation

Week 5 Week 6 Week 7 Week 8

Division by Branches


– Political Party Classifier (If feasible)

– Map Visualization



 – Streamlit App


– Streamlit App

– Deployment




Proposals of workshops topics for the challenge:

Select the workshops you would like us to organize for your project from listed down below and share your thoughts and needs during the kick-off meeting. If you would like us to organize a workshop on a topic not listed here, please mention it here and we will try to find a speaker for it. 

  • Data gathering and cleaning for News/text
  • Topic modeling NLP
  • Streamlit – creating interactive visualizations



Harnessing AI for Renewable Energy Access in Mexico

Harnessing AI for Renewable Energy Access in Mexico

The Background

In Mexico, the electricity production given by renewable energy is around 31%, where 4.3% comes from solar energy according to the Energy Secretariat (2020). Mexico’s government objective for 2050 is to generate 50% of the electricity from renewable energy.

The Problem

The main objective of this project is to locate with data science the best solar energy spots with public spatial demographic data and satellite images.

The project results will be made open source. The deliverables of the projects will be useful for further research and decision-making for private companies, public institutions, and policymakers like SENER, ANES, ASOLMEX, Solar Power Europe, Tesla, GIZ, etc.

The Project Goals

1. To get a comparison of nighttime satellite imagery against the geographic location of the population.

2. To make a grid coverage analysis and machine-learning-driven heatmaps to identify sites that are most suitable for solar panel installation.

3. To create an interactive map with a list of the top Mexico regions with a high demand for electricity.


The Learning Outcomes

At the end of the project the collaborators will learn how to:

1. Extract satellite images.

2. Analyze spatial demographic data.

3. Create heat maps that interpret insights of solar energy spots.

4. Create an interactive map with satellite images.


Fighting illegal dumping in Mexico through building a predictive model

Fighting illegal dumping in Mexico through building a predictive model

The Background

Environmental conservation has many different factors, and in Mexico, one of the most influential is the relationship between dumping sites and garbage collection. Many places in Mexico are severely affected illegally, and some of them are regions of nature that must be protected


The Problem

Build machine learning models on illegal dumping(s)in Mexico to see if there are any patterns to help understand what causes illegal dumping(s), predict potential dumpsites, and eventually how to avoid them.


The Project Goals

1. To identify and visualize in a map spatial patterns of existing TrashOut dumpsites.

2. To predict potential dumpsites using Machine Learning and visualize them with a heatmap.

3. To understand and describe patterns of existing dumpsites to prevent future potential illegal dumping(s) with an unsupervised model.


The Learning Outcomes:

1. How to use Google Earth Engine for further visualization and data modeling.

2. How to visualize certain regions in a map and create filtered visualizations.

3. How to create a heat map based on ML predictions. 

4. How to describe unsupervised models applied to illegal dumpings.

5. How to train and deploy image recognition models


The Tasks & Timeline

Week 1 Week 2 Week 3 Week 4

-Satellite image collection

-Data collection

-Image preprocessing

– Exploratory Data Analysis(EDA)

-Interactive map with spatial patterns.

-ML prediction for dumpsite sites. 

-Unsupervised model for illegal dumpings.

-Start building Streamlit WebApp (Optional)

-Visualize ML prediction

-Gathering all the information into a report.

-Deploy the App in Cloud Application Platforms(Optional)


Link to Original Project:

Chapter’s Partner
Mexico Chapter Lead

Mario Rodriguez

Mario has +4 years of experience in BI, digital transformation, product and project management, software development, and AI for social good projects. He is passionate about creating an impact to the world and the community with applied science and technology. Currently, he is studying a master’s degree in Mexico at UAEM, where he is specializing in computer vision for sign language detection. In addition, he’s been volunteering in Omdena as the Mexico Chapter Lead, in Global Shapers Community as a member of Cancun Hub, and in PMI Mexico Chapter as the BI Coordinator.

Eduardo Padron

Eduardo Padron is a Data Manager at a NGO that helps to create evidence with Data Analysis for health. Previously, Eduardo worked as a maker for the University of Guanajuato. He graduated with honors from the University of Guanajuato with a degree in Hydraulic Engineering and a Master’s in Water Science for his work in prototyping smart valves, GUI, and smart devices for irrigation. His most recent achievement is to be one of the winners of the Tensorflow Microcontroller Challenge.

He has been collaborating on projects related to IA and ML-like the Omdena AI with impact startup Dryad Networks for detecting forest fires by applying supervised machine learning techniques.

In his free time is taking courses to overpass his knowledge about IA, ML, DIY culture, and IoT to make his personal projects and ideas, after putting his knowledge into a blog for people with the same interest.