Africa is incredibly vulnerable to climate change, which is now becoming a defining challenge. The rampant effects of climate variability are turning hazards into disasters, with environmental degradation having a critical effect on agricultural production and economic productivity.
While the outlook may look bleak, the ongoing mitigation efforts are improving with the help of technology advancements. Every industry is having to make changes to address climate concerns, with a broader focus on how Artificial Intelligence (AI) and Earth Observation (EO) technologies (also known as AI-EO) can play a crucial role in combating climate change impacts and enhance disaster management operations and resilience.
Key AI-EO Applications
Integrating EO data with AI approaches has helped in developing climate monitoring and prediction models. This allows the assessment of climate change patterns and forecast of possible weather events, importantly providing early warnings to vulnerable populations. The use of EO is still providing both medium to long-term range assessment of global environmental change information and impact to both physical and economic livelihoods. In Africa, EO technology has been used to detect and analyse changes in land use and land cover, thus monitoring biodiversity loss. This allows for rapid responses to threats, such as deforestation and encroachment. The use of AI-EO in such scenarios provides capacity to perform large algorithmic analysis, using satellite information, to track changes in ecosystems. This can help to monitor protected areas and inform decision making.
In recent years, droughts, floods, and cyclones have become increasingly frequent and more intense due to climate change, posing a significant threat to African communities. The use of EO and drone imagery has provided accurate, live mapping of disasters and can aid emergency management for evolving situations and provide decision making support. The integration of AI algorithms with satellite imagery provides a platform to develop climate monitoring and prediction models.
Copernicus Sentinel2 images processed by Iban Ameztoy @i_ameztoy
Food security is a key economic booster in many countries in Africa, and a source of livelihood. However, with the ever-growing urgency of food insecurity and the threat of climate change, there is increasing interest in using AI-EO for agriculture, particularly in Africa. The continuous use of EO provides estimates in yield prediction, crop health analysis and cropland mapping. This information is invaluable to farmers so they can better understand how crops respond to variations in farming practices and the climate variability. Additionally, AI is being used in conjunction with satellite imagery analysis to manage climate risks, therefore preventing food supply chain disruptions and detecting diseases and pests early, through image-recognition algorithms. These approaches can enable farmers to take preventive measures and actionable decisions, such as when to plant, prune, and harvest.
Challenges and support needed in implementation of AI-EO
There is clearly positive activity in AI-EO in Africa, however uptake and development is still slow. This could be attributed to technological literacy and financial capacity, and that many EO host institutions are still navigating the adaptability of new technology. Additionally, while some industries have adapted to AI solutions, such as agriculture and natural hazard monitoring, others are still yet to be actively engaged in climate and AI topics. This may be due to lack of awareness and expertise in AI solutions, and the limited capacity by present EO organisations in understanding AI data and analysis.
We must harness adoption of AI-EO in Africa rapidly to be able to remedy the current and future effects of climate change on the environmental, social, and economic livelihoods on the African continent. The following can be investigated:
Partnership synergy between all stakeholders, including international organisations, space data providers and government agencies, to maximise the potential capability and applications resulting from integration of AI and EO.
Capacity building is key (especially for EO institutions) to identify how AI can be implemented, the challenges and benefits, and what resources and skills are required for successful AI-EO integration. The partnership synergy would be beneficial to identify capacity trainers, assess the impact of climate change, and plan how to build resilience.
In conclusion, the capability of AI-EO in building resilience to combat the effects of climate change in Africa CAN be achieved. To build an effective AI-EO approach there needs to be in-depth research and an assessment of climate change challenges and variability in Africa. This should encompass data privacy and security, data ownership, ethical standards on AI algorithms, and policy guidance on regulatory structures. For this to be achieved, scientists, industry professionals, regulators, and governments must collaborate to build resilience and sustainability in tackling climate change, and progress the integration of AI-EO technology.
About the Author
Stella Chelangat Mutai is a Geospatial consultant for The UN World Food Programme. Stella's work focuses on the Emergency, Monitoring and Evaluation department, covering the regional bureau of Southern and Eastern Africa.
Réseau's goal is to connect people with purpose and foster a more inclusive global dialogue.