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the building damage predictions are shown as polygons with varying shades |
of red corresponding to increasing damage. The visibility of these predictions |
can be toggled in the interface so that a user can see the underlying imagery. |
to view the imagery and predictions, and doesn’t require users |
to have any formal GIS experience as all imagery is pre- |
rendered. Specifically, users can: |
1) Toggle back and forth between the pre- and post-disaster |
imagery to easily see the differences; |
2) Change the visibility of the damage predictions to see |
the extent of the damage; |
3) Show standard layers (e.g. OpenStreetMap or Esri World |
Imagery) for additional spatial context. |
This is implemented with open-source tools including: |
GDAL3, leaflet4, and Docker5. |
An instance of our visualizer is shown in Figure 6 for |
a scene from Jeremie, Haiti after the Haiti Earthquake in |
August, 2021. The tower of the Cathedral of Saint Louis Roi |
of France (middle of the scene) is classified as damaged by the |
model and can be seen to be destroyed. The code for running |
inference with our final building damage model, as well as |
setting up an instance of the building damage visualizer tool |
is publicly available6. |
Fig. 7. Full screenshots of pre- and post-disaster images shown partially in |
the building damage visualizer instance in Figure 6 for better visibility. 2021 |
Haiti Earthquakes. |
3https://gdal.org/programs/index.html |
4https://leafletjs.com/ |
5https://www.docker.com/ |
6https://github.com/microsoft/Nonprofits/IX. D EPLOYMENT AND APPLICATIONS |
Automating building detection and damage assessment has |
the potential to tremendously speed up disaster response |
processes [6], [27], which are of critical importance for |
humanitarian organizations to estimate the geographical extent |
and the severity of a disaster and plan accordingly [28]. To |
ensure that such an assessment can be delivered in time, |
this study’s stakeholder is implementing the proposed model |
within a scalable, distributed computing system. Within this |
system, satellite images are divided among many identical |
instances of the model, which process them in parallel. This |
guarantees a fixed computation time with any number and |
size of input satellite images. The model’s output is then |
shared with the wider humanitarian network in three ways: the |
aforementioned web visualizer, the open data-sharing platform |
“Humanitarian Data Exchange”, and via man-made maps |
(in digital or printed format), which can be directly sent |
to and used by first responders in the field. This ensures |
the rapid diffusion of information among all stakeholders |
involved in disaster response management. It is worth noting |
that our stakeholder’s experience with applying such tools in |
humanitarian settings and discussions with practitioners have |
highlighted the importance of two aspects. First, the value |
of automating damage assessment lies in speed, rather than |
accuracy. Regardless of visible damages, detailed ground-level |
inspections by trained personnel are still needed to assess |
the structural integrity of a building [29] and it is unlikely |
that remote sensing technology will replace that in the near |
future. For this reason, the focus of satellite-based damage |
assessments should be to provide broad numerical estimates |
as fast as possible, rather than building-level prescriptions. |
Secondly, while the immediate response is primarily informed |
by disaster impact (which can be quantified by the number of |
damaged buildings, among other metrics), long-term shelter |
recovery programs must take into account several other |
contextual factors, such as the socio-economic conditions of |
affected people and land ownership [30]. Because of this, |
information on building damage often needs to be combined |
with other data to be useful. Providing raw data including |
geo-referenced building footprint masks and corresponding |
damage levels to the humanitarian community is necessary to |
enable these analyses. Furthermore, as we discussed in section |
VI, trained models based on the proposed approach might |
not be robust to significant distribution shift across different |
geographies; as such, domain adaptation techniques need to be |
explored to address the data biases issues [31]. In this context, |
active learning and human-machine collaboration approaches |
have been discussed in [32] and [33]. |
X. C ONCLUSION |
Natural disasters’ frequency is growing; thus, the impact |
of such events on communities continues to increase. The |
strategic response of humanitarian organizations to allocate |
resources and save lives after disasters can be improved by |
using AI tools. We propose a convolutional neural network |
model that uses satellite images from before and after natural |
disasters to localize buildings using the UNet model and |
score their damage level on a scale of 1 (not-damaged) to 4 |
(destroyed) using a multi-class classifier. We showed that while |
our proposed model demonstrates decent performance, it also |
works three times faster than the fastest xView2 challenge |
winning solution and over 50 times faster than the slowest |
first place solution, which indicates a significant improvement |
from an operational perspective. We also developed a web- |
based visualizer that can display the before and after imagery |
along with the model’s building damage predictions on a |
custom map to allow better inspection of the impacted areas by |
decision-makers. This paper outlines results of a collaboration |
between Microsoft AI for Good/Humanitarian Action and 510 |
an initiative of the Netherlands Red Cross, to help inform |
field deployments using satellite imagery and AI technologies. |
Our solution outperforms stakeholder’s current baseline model |
significantly in terms of inference speed and segmentation |
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