CV4Africa DLI Workshop, Accra, Ghana

September 09, 2023

The 1st CV4Africa Workshop will be held this year co-located with Deep learning Indaba 2023. The workshop will feature invited talks from prominent researchers and practitioners, a challenge and hand-on tutorials. We invite all members of the Computer Vision community to attend the workshop. Computer vision is used in various applications that impact African communities such as, precision agriculture, satellite imagery understanding, and medical image processing. It is concerned with the mathematical techniques that include both classical and machine learning based methods towards achieving the goal of scene and video understanding, and recovering the 3D shape and appearance of objects in images. Different sub-tasks in Computer Vision include optical flow, motion detection, tracking, segmentation/grouping, and 3D reconstruction among others. Although, it is widely used to serve our communities, there exists a current gap in the community based research that lacks focus on Computer Vision in Africa. We are mainly inspired by other grassroots initiatives in the African community for both natural language processing (Masakhane) and machine learning for health (Sisonke Biotik). We aim to launch a community which we call Ro’ya-CV4Africa that focuses on Computer Vision research for Africans and by Africans. We believe that this bottom-up community based approach is better able to bring researchers from varying parts of the society and is inclusive by design. Participatory research, unlike conventional research, defines the research process itself within a collaborative and accessible framework to all members of the community.



Abigail Annkah

Research engineer, Google AI Ghana


Karim Amer

Co-Founder & CTO VAIS, Egypt


Raesetje Sefala

Research fellow, DAIR institute


Raesetje Sefala:

(AI Research Fellow at the DAIR Institute)

Spatial Apartheid: Constructing a Visual Dataset to Study the Effects of Spatial Apartheid in SA

Abstract: The lack of ground truth data is usually a barrier when researchers are trying to train machine learning models for real-world applications. Spatial datasets in particular are both complex and expensive to create. In this talk I will be discussing how we constructed a visual dataset that allows us to classify neighbourhoods using machine learning and also allows us to study the effects of spatial segregation in South Africa. While datasets 7 times smaller than ours have cost over $1M to annotate, our dataset was created with highly constrained resources.

Bio: Raesetje is an AI Research Fellow at the Distributed AI Research(DAIR) institute. Her research focuses on creating ground truth datasets and using machine learning and other computational social science techniques to explore research questions with a societal impact. Her current research uses satellite imagery to study the legacy of spatial apartheid in South Africa, post-Apartheid. Her previous work involved partnering with various stakeholders and using machine learning techniques to study poverty and traffic safety in the urban parts of Nigeria and Jakarta, respectively.

Karim Amer:

(Co-Founder & CTO VAIS, Egypt)

Combining Deep Learning and Classical Approaches to Predict Tropical Storms Speeds using Satellite Data

Abstract: Tropical cyclones unfortunately can cause thousands of deaths and billions of dollars of damage, which makes their real time monitoring important to better assess their danger and help save people's lives. In this talk, Karim Amer will discuss his 2nd solution to the "Wind-dependent Variables: Predict Wind Speeds of Tropical Storms" challenge organized by Radiant Earth Foundation and NASA.

Bio: Karim is Co-Founder & CTO, VAIS, a deeptech startup that brings the power of Geo-spatial Intelligence and AI to the hands of African and Egyptian farmers. He has been applying AI for the last six years to challenging interdisciplinary problems in several areas including Agriculture, Earth Observation, Medical Imaging, Material Science, Bioinformatics, and Geophysics. He previously worked at Siemens Healthineers Technology Center, NJ, USA on the development of cutting-edge AI models that can be used in multiple clinical applications which turned into 3 patents (pending approval). Karim won several international AI competitions and has become the first Kaggle Master of Data Science Competitions from Egypt, among a few in the region and top 1% worldwide.

Abigail Annkah:

(Research engineer, Google AI Ghana)

Computer Vision for Remote Sensing: Past, Present and Future

Abstract: Remote sensing (RS) and computer vision (CV) are two rapidly developing fields with the potential to revolutionize our understanding of the world. RS provides information about objects or phenomena without direct contact, while CV extracts useful data from digital images or videos. The combination of CV and RS has led to significant advances in a variety of applications, including earth observation, natural disaster detection, military surveillance, urban planning, crop monitoring, and environmental assessment. In the past, the availability of data and computational resources limited the use of CV methods for RS. However, recent advances in Deep Learning have made it possible to develop more powerful and accurate CV algorithms for RS. The role of CV in RS is expected to continue to grow in the future. The availability of large datasets, pretrained models, and the development of new sensors will enable the development of even more advanced CV algorithms for RS. This talk will provide an overview of the past, present, and future of CV for RS. It will discuss the challenges and opportunities of this field, and highlight some of the recent advances in deep learning for RS, including some work from the Google Accra research lab.

Bio: Abigail Annkah is a Google AI Research Software Engineer in Ghana. She is passionate about using computer vision and optimization to address global challenges, such as bridging information gaps in developing regions, improving health, education, and agriculture. She is also actively involved in mentorship programs and is always looking for opportunities to collaborate with others who share her vision. Her undergraduate studies at the University of Ghana, Legon, in Accra provided her with a foundation in Mathematics and Statistics. She was awarded a full scholarship for a Master of Science in Machine Intelligence from the African Institute of Mathematical Sciences (AIMS) in Kigali, Rwanda. Prior to residency at Google, she worked on several projects, including the Application of Image Analysis to Advanced Complex Microbial Images, Spatio-temporal modeling and Malaria prediction in Sub-Saharan Africa, and Image Denoising using the Diffusion Equations with explicit numerical methods. She worked on mapping built-up environments in Africa using satellite imagery during her residency at Google, and is a contributor to Google's Open Buildings Dataset. In addition to her technical skills, Abigail is a strong advocate for diversity and inclusion in STEM fields. She is an ambassador for Mind the Gap , a Google initiative to encourage female students to pursue careers in STEM, and was mentor for the African Institute of Mathematical Sciences (AIMS) Ghana's Girls in Mathematical Sciences Program (GMSP) in the past. She was a member of a Rwandan team that engaged high school teachers and teacher in-training for problem solving for Mathematics students, and also participated in a pilot version of the Rwandan Mathematical Olympiad.

Program Schedule:

Room: Auditorium, Spetember 09, 2023
Zoom Link
Zoom Meeting ID: 932 4408 8288, Pass Code: 088848



Mennatullah Siam

Assistant Professor, Ontario Tech University, Canada


Idriss Tondji

Research and Teaching Assistant, AMMI/AIMS, Senegal


Mahmod Abdien

MSc student, Queen’s University, Remote-Egypt


Eman Ehab

Deep Learning Researcher, Nile University, Egypt


Gbetondji Dovonon

PhD Student, University College London, UK


Hakeem Omotayo

PhD student in Statistics, UC Davis, US


Oluwabukola Grace Adegboro

MSc, Ontario Tech University


Abigail Oppong

AI Ethics| Ariel Foundation Int.|NLP for Social Good and Fairness


Pimi Yvan

MSc student, AMMI/AIMS

i Image

Daniel Ajisafe

Graduate Research and Teaching Assistant, UBC, Canada


Zainab Akinjobi

Msc Applied Statistics, New Mexico State university, US


Ismaila Lukman

PhD student, University of Angers, France


The Legacy of Spatial Apartheid Machine Learning Challenge

The spatial apartheid dataset has high-resolution satellite images of neighborhoods in South Africa in 2011. It allows the training of a machine learning model that accurately predicts the class of a neighborhood (wealthy area, non-wealthy area, non-residential neighborhood, or background) based on the image. This prediction provides insights on the evolution of spatial apartheid over time and how the demographics and development of neighborhoods in South Africa have changed.

Challenge Dates:

  1. Start: July 1st
  2. Deadline: August 31

Challenge Prizes:

  1. 1st winner: 500$ and the opportunity to present in CV4Africa Workshop, DLI, Accra, Ghana, 2023 (remote or in-person).
  2. 2nd winner: 300$
  3. 3rd winner: 200$

More details.



Code of Conduct

Inspired by Masakhane CoC we follow a similar pledge: "In the interest of fostering an open and welcoming environment, we as contributors and maintainers pledge to making participation in our project and our community a harassment-free experience for everyone, regardless of age, body size, disability, ethnicity, sex characteristics, gender identity and expression, level of experience, education, socio-economic status, nationality, personal appearance, race, religion, or sexual identity and orientation." Examples of behavior that contributes to creating a positive environment include:
  1. Using welcoming and inclusive language
  2. Being respectful of differing viewpoints and experiences
  3. Gracefully accepting constructive criticism
  4. Focusing on what is best for the community
  5. Showing empathy towards other community members
Examples of unacceptable behavior by participants include:
  1. The use of sexualized language or imagery and unwelcome sexual attention or advances
  2. Trolling, insulting/derogatory comments, and personal or political attacks
  3. Public or private harassment
  4. Publishing others' private information, such as a physical or electronic address, without explicit permission
  5. Other conduct which could reasonably be considered inappropriate in a professional setting
  6. Being asked to stop a certain behaviour and giving a response of "just joking" instead of stopping such behaviour