

PulseSatellite
PulseSatellite is a collaborative web-based tool that combines cutting edge artificial intelligence with human expertise to extract the most relevant information from satellite imagery for use in humanitarian contexts.
PulseSatellite is a collaborative web-based tool that combines cutting edge artificial intelligence with human expertise to extract the most relevant information from satellite imagery for use in humanitarian contexts.
Every day, millions of images are captured from space by an ever-growing number of satellites. In humanitarian crises or conflict areas, accurate and timely satellite image analysis is key to supporting critical operations on the ground.
Use cases include: monitoring population displacement, settlement mapping, damage assessment, flood assessment and identifying the direct impact of earthquakes, volcanoes, cyclones and landslides.
Until recently, this type of analysis was done by human analysts who spent hours in front of a map manually counting and classifying structures and other elements.
PulseSatellite is a tool to analyze satellite imagery assisted by neural networks that seeks to incorporate humans-in-the-loop at different stages in the model inference process to enable optimal results and expert validation.
We opened up the beta version of the tool to UN agencies and the interest we received was overwhelming.
We’re currently evaluating submissions of UN partners whose projects we hope will yield interesting results and good lessons learned.
As we work to raise more funding, we plan to make the tool available to other humanitarian practitioners.
This video is a deep dive into how PulseSatellite can be used to count settlements in refugee camps.
Identifying and counting structures in refugee settlements
Inferring the size and proximity of roofs for slum mapping
Producing real-time maps of flooded areas for damage assessment
Counting and classifying structures in a refugee settlement is a common analysis task for humanitarian agencies. In practice, this is currently manually done by human expert analysts using satellite imagery. A single settlement may have tens of thousands of structures, and identifying each of them can take several days.
For camp mapping, we have a Mask R-CNN model (He et al. 2017) trained on images from 12 settlements, where the image of each has been split into 300×300 pixel tiles and annotated by human experts. Once the model has been run on an unseen camp, the analyst can inspect the result in the tool and correct the outputs on a subset of tiles. An adaptation stage can then be performed to fine-tune the model to the unseen image and increase performance.
In recent independent tests, camp completion rates increased from 77.3% to 94.7% after adaptation, with a final user accuracy of 94.4% -in line with humanitarian performance requirements described in (Quinn et al. 2018).
Improvised shelters, often known as ‘slums’, found on the outskirts of cities around the world are largely unmapped and misunderstood.
We have developed a methodology similar to that used in flood detection whereby we train an image segmentation model to identify pixels in the image belonging to slum dwellings. Current models have been trained on slums in Uganda in collaboration with Pulse Lab Kampala; however, due to lack of similarity between slum regions, models for different slums will have to be developed.
With better mapping of slum dwellings, we hope to help UNOSAT, and other agencies, to more rapidly assess complex shelter coverage while enabling better and more timely care to be given to those in need.
Rapid analysis of regions affected by natural disasters is essential for response planning. Current methods for rapid flood mapping require an analyst to perform a series of manual pixel intensity thresholding and image processing operations s, taking several hours to be completed.
The PulseSatellite tool uses a U-Net model (Ronneberger, Fischer, and Brox 2015) to produce a flood map in the order of minutes. The model has been trained and tested on images from Bangladesh and Somalia achieving overall accuracy scores of over 90%, in line with current manual methods.
“PulseSatellite is an exciting example of a system combining AI and human experience to greatly improve the use of satellite imagery in the UN system. By employing artificial intelligence and a great and intuitive interface, the power of satellite imagery is much more accessible to UN colleagues.
“PulseSatellite is a promising tool that will allow UN agencies to use their own models while including the analyst during on-the-fly training and evaluation. It will also help to scale the deployment phase providing a user-friendly interface.”
“We wanted to build a web-based tool that is tailored to the needs of UN agencies and that is easy to use. The collaborative nature of the system is what hopefully makes it invaluable, both because it allows teams across time zones and geographies to work on the same project, and because it encourages teams to upload and use their own models, as well as share them with others.
If you are from a UN Entity and would like to try the tool (once we open stage 2 of testing in the upcoming months), if you are a donor interested in taking the tool to the next level, or if you are an AI expert wanting to improve existing or add new models, please get in touch.