Helping People with Visual Impairment to Easily Use Buses through Computer Vision

Helping People with Visual Impairment to Easily Use Buses through Computer Vision

In this 8-weeks challenge, 50 collaborators worked on solving a significant daily-life mobility & orientation challenge for people who are blind or visually impaired using AI solutions

 

 

The Problem

People with visual impairment face an enormous amount of challenges daily. One of the biggest challenges is in orientation & mobility – the ability to walk safely and accurately from point A to point B.

Public transportation, such as buses, is the main tool for individuals to navigate and get to where they want.

In this challenge, together with RenewSenses LTD, an Israeli company developing assistive technologies for people who are blind, we will assist people with visual impairment in their experience of catching a bus. 

Results from this challenge will be directly tested with people with visual impairment through pilots the company is conducting – enabling fast feedback and creating a highly meaningful challenge that will impact the daily-life independence of many.

The challenge inckudes integrating the solution into the company’s app. The project needs Computer Vision, Object Detection or OCR knowledge, also image data preparation methods, and/or mobile deployment experience.

 

The Project Outcomes

  1. Detecting whether a bus is arriving through real-time object recognition (considering that bus might look different in different countries
  2. Determining if it’s the right bus the user needs through OCR for the text on the bus.
  3. Determining whether a seat is empty/not empty to assist users in finding an empty seat on the bus using object detection.

 

Challenge deliverables is tested directly with a big community of people with visual impairment. The need for this kind of solution is huge and the impact of the developed algorithm will get to users’ hands immediately.

Find more information on how an Omdena project works

 

Using Satellite Imagery to Detect and Assess the Damage of Armyworms in Farming

Using Satellite Imagery to Detect and Assess the Damage of Armyworms in Farming

50 Omdena’s collaborators worked to build AI solutions to detect the spread of armyworms, estimate the damage they cause and save the agriculture industry.

 

 

The Problem

Armyworms are caterpillar pests of grass pastures and cereal crops, where they attack grains and feed on leaves. A very hungry caterpillar is rampaging through crops across the world, leaving a trail of destruction in its wake. The fall armyworm, also known as Spodoptera frugiperda (fruit destroyer), loves to eat corn but also plagues many other crops vital to human food security, such as rice and sorghum.

This invasive eating machine originated in the Americas, but in the last few years, it has gone global. It was reported in Africa in 2016 with a noticeable outbreak in Mali, Ivory Coast, and Ouganda. 

Another type of invasive insect is the Desert Locust which is one of the species of short-horned grasshoppers (Acridoidea).
During plagues, Desert Locusts may spread over an enormous area of some 29 million square kilometers.

The spread of these pests into the world will cause huge pressure on the global food production systems.

 

The Project Outcomes

Using satellite images, the team was able to detect and identify the damage assessment of either or all together (depending on data availability):

  • Fall Armyworm
  • Africa Armyworm
  • Locust Desert, surge/outbreak in Mali (or Ivory Coast or Ouganda) in order to design an insurance product.

 

 

Find more information on how an Omdena project works
Improving Transparency and Democratizing Access to Public Sector Contracts using NLP

Improving Transparency and Democratizing Access to Public Sector Contracts using NLP

In a two-month challenge, a global team of 50 Omdena’s collaborators contributed to help democratize access to contract opportunities buried within public government documents and improve public sector transparency.

 

The Problem

There are a lot of signals that indicate upcoming opportunities hidden within the public government documents. Early notification is seen as key to giving companies without an existing relationship (and the related insider info) an opportunity to participate in these contracts. The ultimate desire is for better communities in which to raise our families and build our businesses by improving a broken procurement process where incumbents win the majority of the contracts.

In order to never miss an opportunity about upcoming contract opportunities, policy changes, or any important local government topics, Ontopical combs publicly available information from government departments then processes and analyzes text and transcripts to deliver unique insights. The bad news is that sorting through that online information isn’t easy. There is no one place collecting relevant information.

 

The Project Outcomes

Ontopical has gathered years of Municipal Council meeting agendas, minutes, and videos. Using these council meeting minutes, agendas & video transcriptions as well as data from Request for Proposal (RFP) posting sites, as training data, train a model to recognize upcoming business opportunities in meeting agendas, minutes, and videos LONG BEFORE any RFP is posted. This will help democratize access to upcoming contract opportunities by providing all with the advance notice required to submit a strong proposal. This will greatly help to make the process of submitting proposals in response to RFPs more transparent.

 

Find more information on how an Omdena project works

 

Predicting Climate & Geopolitical Risks for Financing Water & Energy Needs in West & Central Africa

Predicting Climate & Geopolitical Risks for Financing Water & Energy Needs in West & Central Africa

In this high-impact two-month Omdena Challenge, a global team of 50 AI changemakers collaborates to help public and private decision-makers quantify the damage and destruction of some big threats like climate change and geopolitical risks like wars.

 

This project uses different levels of experience in Remote Sensing, GeoSpatial Data, and Machine Learning and Deep Learning (Convolutional neural network for GeoSpatial Data processing).

 

The problem & background 

Finz. focuses to help global decision-makers, such as government entities, banks, or foundations, to finance autonomous and light units for water or energy efficient accesses, in urban and rural contexts.

It is now widely accepted that climate change or geopolitical issues pose serious threats to the availability of essential-to-life resources, such as water or energy. 

In emerging countries (Africa or Southeast Asia), access to resources for human or animal consumption, and agriculture needs to secure food, are keys. 

In developed countries, climate change threatens the availability of water (e.g California) and will figure a big issue for all inhabitants on Earth tomorrow. 

We believe to help how decision-makers act and decide with accuracy in predicting risks of damage /destruction on assets or population that could be a catalyst factor to adjust essential-to-life needs with decisions.

 

The project goals

Building models that predict information for private and public financial decision-makers, to: 

  • Quantify percentage of damage /destruction of

       (The destruction of assets 

1- In urban areas: constructions, and commodities warehouses

2- In natural/rural zones crops)

 

  1. Climate change factors, such as major climatic events involving water resources. 
  2. Geopolitical threats, such as wars, riots, rebellions, and refugee’s displacements.

 

This project will focus on 3 main parts of Africa

  1. Togo 
  2. North of Ivory Coast /Burkina-Faso Boundaries 
  3. Cameroon – Central Region

 

The project expected outcome

When we provide the model with the geographic coordinates (latitude and longitude) of an area. The model predicts for the area whose coordinates (latitude and longitude) have been provided, the percentage of damage/destruction of : 

  • Assets destruction like constructions, commodities warehouses, forests, crops, fields, and coastal areas.
  • Population damages like displacement of people, and injuries or deaths.

 

 

Why join? The uniqueness of Omdena AI Challenges

A collaborative experience you never had in your working life! For the next eight weeks, you will not only build AI solutions to make a real-world impact but also go through an entire data science project lifecycle. This covers problem scoping, data collection, and preparation, as well as modeling for deployment.

And the best part is that you will join a global and collaborative team of changemakers. Omdena AI Challenges are not a competition or hackathon but a real-world project that will take your experience of what is possible through collaboration to a new level.

Find more information on how an Omdena project works
Building A Recommendation System For Cognitive Exercises and Training Programs

Building A Recommendation System For Cognitive Exercises and Training Programs

In this two-month challenge, a team of 50 data scientists to build an AI model to recommend cognitive exercises and training programs based on capabilities baselines and should learn and refine the program through feedback.

 

This challenge requires different levels of experience in Machine Learning, Data Analytics, and Recommendation Systems.

 

The Problem

NESTRE is the first NEuro-STREngth company to leverage the science of neuroplasticity to successfully apply a proprietary and pioneering training method in the areas of brain health, mental wellness, and cognitive performance. NESTRE’s first digital solution is a neuro-strength training platform that provides cognitive assessments and training to users. NESTRE’s mission is to help people get better by providing access to affordable solutions and instilling hope for betterment through proven science.

 

 

The project goals

Build an AI model to recommend cognitive exercises and training programs given:

  • A subject’s baseline capabilities
  • Stated cognitive improvement goals
  • Available cognitive exercises
  • Subjects demographic information

The AI model should be able to learn and refine the program through feedback based on adherence to the program and subsequent outcomes.

 

 

Why join? The uniqueness of Omdena AI Challenges

A collaborative experience you never had in your working life! For the next eight weeks, you will not only build AI solutions to make a real-world impact but also go through an entire data science project lifecycle. This covers problem scoping, data collection, and preparation, as well as modeling for deployment.

And the best part is that you will join a global and collaborative team of changemakers. Omdena AI Challenges are not a competition or hackathon but a real-world project that will take your experience of what is possible through collaboration to a new level.

Find more information on how an Omdena project works