Omdena Chapter Page: Philippines

Omdena Philippines Chapter - Omdena Chapters

Welcome to the Philippines Chapters!

There are 2 active chapters in the Philippines:

  1. Manila, Philippines
  2. Malolos, Philippines
  3. National Capital Region, Philippines

Apply here to be a chapter lead for other cities and/or universities in Philippines

Upcoming Projects

Project Starts:
All Data Science Skills Welcome!

Malolos, Philippines Chapter – Detection and Prediction of Soil Nutrient Deficiency using Satellite Imagery Part 1

The Background 

Without life there is no soil and without soil there is no lifeAccording to World Bank Group, The Philippines is one of the most natural hazard-prone countries in the world. The social and economic cost of natural disasters in the country is increasing due to population growth, change in land-use patterns, migration, unplanned urbanization, environmental degradation, and global climate change. Agriculture in the Philippines are no exception to climate change such as soil nutrition deficiency that could lead to wide food insecurity for the next decades.



All of the world’s topsoil could be gone within 60 years, according to UN FAO (Scientific American, 2014)

A study claims that by 2045, we’ll producing 40 percent less food on the planet and our population will grow to 9.2 billion people. 40 percent less food means, about 1.5 to 1.7 billion people can die in 6 months (as per estimation)

Given that by 2045, 29.5 millions Filipinos will be added to the country’s growing population. In other words, millions of people will go deeper into hunger and malnutrition and we cannot wait until this disaster happen. We can start on this challenge and help our policymakers create a policy that helps our community to protect the soil



  1. Extract and process the satellite data available from our partner institutions and organizations (NASA, UNWP, FAO, Worldbank, local organizations and other possible sources). Here are the suggested data to be extracted from the satellite imagery:
    1. Nitrogen
    2. Phosporous
    3. Soil Erosion
    4. Precipitation
  2. Educate our volunteers about how to extract and process a satellite Imagery
  3. Ideate and create a solution pipeline to build a dashboard that detects and predicts soil nutrient deficiency. Here the some guidelines for this goal:
    1. What other data do we need to build the model?
    2. What model(s) to utilize?
    3. What technology to utilize?
  4. Prepare the volunteers for the upcoming part 2 of the challenge



  1. Satellite Imagery Data collection
  2. Satellite Imagery/Remote Sensing Data Pre-Processing
  3. Labelling of Data




Week 1

Week 2

Week 3

Week 4

Week 5

-Understanding the problem

-Data Gathering

-Workshop about How to collect GIS data and technology to use

-Understanding the problem

-GIS Data cleaning

-Workshop about How to clean GIS  data and technology to use

-Satellite Imagery Pre-Processing and Data labeling

-Workshop about How to pre-process Satellite Imagery  data and technology to use

–Ideate and create a solution pipeline to build a dashboard that detects and predicts soil nutrient deficiency

-Part 1 Documentation and Part 2 Integration


Ongoing and Completed Projects

Completed Project(s)

Increasing Renewable Energy Access in Philippines through AI


The past few months have been really hot in the Philippines with temperatures up to 37 degrees Celsius. More people are reporting an increase in their electricity bills and institutions are looking to greener sources of energy.

The Problem

This initiative’s goal is to use Philippine satellite data in conjunction with other relevant dataset to identify sites that are most suitable for solar panel installation as a greener energy source through machine learning and coverage analysis. 

The project results will be made open source. The aim being to help connect and encourage organisations to use AI tools to understand and plan in transition for green energy. We also hope to encourage citizen science by open sourcing the dataset and code.

The Project Goals

1. Web App containing Map of the sites

2. GitHub Repo with open source code

3. Curated dataset hosted in AWS or Google for open access




Source Code:

Link to the Original project: Harnessing AI for Renewable Energy access in Africa

Philippines Chapter Leads

Albert Yumol

Albert ‘Bash’ Yumol is a tech activist based in the Philippines promoting data-driven and evidence-based solutions to various organizations and communities.
He has 8++ years of experience with data science both in academic research and industry applications.
He is an advocate for using technology (mostly open-source) to promote social development and nationalism and also teaches data science and its applications focusing on human-centered design and data ethics.

Marjorie Jasmine C. Racelis

Marjorie Jasmine Racelis studies Bachelor of Science in Computer Science at Pamantasan ng
Lungsod ng Maynila. Learning computer systems, different programming languages, word
processing and other Microsoft Office applications. The course served to deepen her interest
and opened her mind to how powerful technology field is, and how it can change the world.
Joined two academic organizations. The PLM Computer Science Society as Public Relations
Officer and a Creative Head. The Google Developer Student Club as a Junior Officer at
Operations Department. Looking forward to the challenges that life will bring her and would
welcome opportunities to concentrate on the subject she’s passionate about. Aware of the
demands and challenges, but her enthusiasm, maturity, and strong determination surely would
make her a successful person.

Jester Carlos

Jester is a Junior Business Analyst aspiring to accelerate smart society and clean energy with sustainability and through equity using data, AI, and imagination. He has an interest in computer vision and NLP, loves innovation and community science.

Armielyn Carabot Obinguar

Armielyn Obinguar is a STEM and Women’s Empowerment Advocate and promotes more inclusive opportunities for women and men in industries and in many organizations using Data Science and Artificial Intelligence to solve various community problems.

She joined several projects in Omdena, including in the areas of computer vision, natural language processing, and remote sensing, in many local chapters and AI challenges. She is also a mentor in AI at her university as they are building self-driving cars for the next few years. She is also passionate about bringing technologies to universities and making data-driven policies for the betterment of society.