Building assessment through the use of satellite Synthetic Aperture Radar (SAR), Very High Resolution (VHR) imaging and artificial intelligence has proven to be a valuable tool in the response to humanitarian crises. In the aftermath of natural disasters or other crisis events, rapid and accurate assessment of building damage is of paramount importance for the effective delivery of aid and for ensuring the safety of affected populations. The integration of these technologies allows for a more comprehensive, efficient and accurate assessment of building structures. The ability to quickly estimate the area and population involved, assess building damage avoiding on-site inspections can help to prioritize rescue and aid efforts and ensure that the most critical needs are addressed first.
The platform MASAI – Map for damage Assessment with Satellite Artificial Intelligence developed in the project, is designed to support regional, national and international organisations that intervene in emergencies (i.e., humanitarian crisis or natural disasters).
MASAI project manages the automation of the damage assessment process on buildings and infrastructures thanks to the implementation of AI algorithms, favouring the optimization in terms of time and resources of the procedure, useful for timely intervention planning and as support in emergency management. Here below the main key benefits of the proposed service:

  1. MASAI is aimed at supporting humanitarian crisis, both during and after the conflicts (e.g., Ukrainian crisis).
  2. It could be widely used as a tool in damage assessment procedures during humanitarian crisis and in any post-war or post-disaster reconstruction program. Damage buildings assessment plays a prominent role in resettlement and reconstruction of urban infrastructures. The data-analysis process of this assessment is essential to any post-disaster program.
  3. MASAI will be developed to be easily adaptable to different contexts, e.g., natural disasters that due to climate change effects are in constant grow, adapting to different types of emergencies that CRI manages. Moreover, the use of satellite SAR, VHR imaging and artificial intelligence in building assessment can reduce the amount of time and labour required to complete the assessment resulting in significant cost savings.
  4. The process automation is not affected by constraints related to the geographical area of reference and allows to limit the subjectivity of the operators, favouring the reproducibility of the results and making a more consistent comparison among different images over time.ù
  5. The information gathered by the exploitation of MASAI platform can be used to inform decisions regarding the relocation of affected populations and the allocation of resources for assessment, reconstruction and repair efforts.
  6. MASAI project allows to combine the radar and optical techniques, exploiting on one hand the total independence from the meteorological conditions (e.g., cloud cover) and on the other hand a high spatial resolution. Moreover, taking advantage of using of two satellite sources, the web-platform allows an increase in the temporal resolution of the results available to the users.


Finally, the proposed service is structured in three levels:

  1. A large-scale screening SAR layer;
  2. A high-resolution optical layer;
  3. An optional human validated image one.


This peculiarity allows to extend the territorial context of interest and to limit human work (third level) to the areas really affected.
A cross-cutting benefit of MASAI project is that it will create a working environment to accelerate collaborative knowledge generation and integration. This collaborative problem-solving environment enables finding win-win outcomes while tackling key industrial and humanitarian crisis challenges and problems. In this line, partners will improve their own innovation capacity and will be open to new business opportunities. The platform could also allow the display of outputs produced through open standard (as indicated by the European directive INSPIRE) such as Web Feature Service (WFS), Web Map Service (WMS), Web Coverage Service (WCS), Web Map Tile Service (WMTS) and Web Processing Service (WPS) to adapt and interact with different systems.
MASAI platform will be developed to be easily adaptable to different contexts, so as well as entities similar to CRI, the other potential users of our service could be organisations that manages both natural disasters as earthquakes or due to climate change effects and anthropogenic disasters as war in the Ukraine case. In addition to improving the effectiveness of humanitarian responses, MASAI can also provide valuable information for planning and preparation for future crisis events. By collecting data on building damage and degradation, aid organisations can better understand the risks associated with different types of structures.