SpaceBus 2025: AI and space data in Africa

I. The challenges and applications of spatial data (AI and spatial data in Africa)

Easy access to spatial data opens up a vast field of opportunities to improve land management and respond to the crucial challenges of sustainable development in Africa. 

It is essential to understand how this data, when combined with advanced analytics tools, includingartificial intelligence (AI), can transform current practices in strategic sectors.

1. Agriculture and food security

Spatial data, from satellite images, allows in particular:

• Monitor crop developments and anticipate yields:
  Studies have demonstrated the value of using high-resolution satellite images to observe the evolution of plantations over large areas. For example, A study analyzed the mapping of cashew plantations in Benin using deep learning algorithms, enabling the identification of crop trends and expanding areas, which contributes to better agricultural planning.
• Early detection of risks linked to climatic hazards:
  Applying Artificial Intelligence (AI) models to this data helps detect water stress and other risk signals. This helps agricultural services respond quickly to preserve crops and ensure food security.

2. Management of natural resources and the environment

Satellite image analysis offers multiple applications for environmental monitoring:

• Water resource monitoring:
  By analyzing the variation of watercourses and reservoirs via geospatial data, it becomes possible to detect anomalies, for example a reduction in the surface area of ​​water bodies or pollution problems, thus facilitating proactive management of this resource.
• Deforestation detection:
  Les données spatiales permettent de suivre la couverture forestière. Des projets en Afrique se sont appuyés sur ces méthodes pour repérer les zones de déforestation illégale, contribuant ainsi à la préservation de la biodiversité et à la lutte contre le changement climatique.

3. Urban planning and regional development

Faced with the challenges of rapid urbanization, optimizing the use of spatial data is essential:

• Improved urban planning:
  Research conducted on high-resolution urban mapping in Africa using deep learning techniques has made it possible to accurately distinguish between urban, rural and uninhabited areas. Ce type d’analyse contribue à une planification plus fine et plus réactive des infrastructures urbaines.
• Sustainable land management:
  By ensuring up-to-date land use mapping, for example, public decision-makers and local authorities can better secure land titles and resolve land use conflicts.

4. Integration with artificial intelligence

Integrating AI to enhance this spatial data can enable:

• Predictive analysis to guide decisions:
  Using machine learning algorithms, spatial data analysis can generate predictions about crop evolution, urban expansion, and environmental degradation. This approach, demonstrated by various case studies, provides decision-makers with precise indicators to optimize the management of their territories.
• Automation and dematerialization of processes:
  AI can automate the collection, processing and synthesis of data from satellites. Projects such as the monitoring crops in Togo during the pandemic, have shown that accurate mapping could be achieved in less than 10 days thanks to the integration of automated tools.
• User-friendly interfaces for quick decision making:
  The development of intuitive interfaces, which integrate this AI-processed data, allows users – even those without advanced technical skills – to access detailed reports tailored to their needs, thus strengthening their ability to make informed decisions.

II. Challenges to overcome to maximize the impact of spatial data (AI and spatial data in Africa)

Although the use of spatial data for public management and local development offers many promises, several challenges remain to be overcome to maximize its impact. 

In particular, the local communities and the public decision-makers must face obstacles that can hinder the optimal exploitation of these technologies.

1. Access and quality of data

One of the major challenges in the adoption of spatial data remains access to reliable and up-to-date information. 

If initiatives like the SpaceBus 2025 and the programs of the NASA can facilitate access to satellite images, many regions, particularly in Africa, face a lack of infrastructure to access this data continuously and efficiently.

• Limited access to up-to-date data
  According to an African Union report on access to spatial data, less than 30% of local governments in Africa have access to regularly updated geospatial data, which hinders their effective management of natural resources and urban planning. This lack of access leads to decision-making based on outdated information, which can harm the quality of public policies (see African Union – Report on Access to Data).

2. Technical skills and training

Another major obstacle is the lack of technical skills within local governments. Public decision-makers and local government officials are not always trained to fully exploit remote sensing and geospatial mapping tools.

• Lack of geospatial training:

The UN report on geospatial systems indicates that developing countries, particularly those in sub-Saharan Africa, are suffering from a lack of specialized training in geospatial technologies. The report proposes strategies to strengthen local skills through dedicated educational programs.

3. Data integration and interoperability

One of the major challenges for the local communities and the public decision-makers in the use of spatial data is the integration and interoperability of systems. Indeed, most local administrations have disparate databases and siloed IT systems, making it difficult to implement cross-analysis of geospatial data with other types of critical data, such as those relating to urban infrastructure management, health, or agriculture.

• Systems integration problem

Managing geospatial data requires seamless synchronization with other types of administrative data to enable effective analyses. However, a report published by theFood and Agriculture Organization of the United Nations (FAO) shows that in several African countries, data from various sources (satellites, environmental sensors, etc.) are often not compatible with the systems used by local administrations. This lack of interoperability prevents a comprehensive and integrated analysis of environmental, agricultural and urban challenges (FAO Report).

• The infrastructure challenge: storing and processing geospatial data in the AI ​​era

Another challenge lies in the infrastructure needed to store and process this massive data, especially satellite data, which requires large storage capacities and expensive equipment for training AI models, such as GPUs.

III. Building the Future: Overcoming the Challenges of Exploiting Geospatial Data

(AI and spatial data in Africa)

1. Improved access to up-to-date data through the integration of AI

Access to up-to-date geospatial data is a key challenge, particularly in regions where infrastructure is lacking. To address this, artificial intelligence could enable:

• to improve the quality of geospatial data in real time thanks to data cleaning and validation algorithms, integrating AI to detect errors and anomalies in satellite data.
• to optimize accessibility to geospatial data through easy-to-use interfaces for local authorities, by integrating this data into user-friendly, secure platforms accessible on mobile and online.

2. Training and support for the development of geospatial skills

Lack of technical skills is a major obstacle to the exploitation of geospatial data in local administrations. 

Training on AI and geospatial : It would be beneficial for stakeholders to be trained by experts on the use of artificial intelligence for the analysis of geospatial data, adapted to the needs of public decision-makers and local technicians. 

These trainings could cover topics such as mapping, spatial analysis and environmental data management.

• Practical workshops and educational materials : By offering practical training accompanied by modules and educational materials adapted to local realities. This would maximize the impact of investments in technology.

3. Development of interoperable solutions for the integration of spatial data

One of the key challenges is the integration of geospatial data with other types of data, such as those related to infrastructure or natural resource management. It would therefore be wise to have: 

• Interconnected platforms : This would facilitate the management of different types of data (satellite images, environmental sensors, infrastructure databases, etc.), allowing for cross-analysis and more complete analysis.
• Centralized system solutions :

Centralized systems would provide a more comprehensive overview and optimize the use of geospatial data for more accurate and responsive decision-making analyses. 

For example, in water management or urban planning, these solutions could help monitor and predict infrastructure needs, while ensuring more efficient management of local resources.