Red Cross challenge
Please note there are two challenges for the Red Cross. Make sure you select the correct one when registering.
Problem: Measuring the extent of building damage after a disaster can help humanitarian actors to quickly identify the areas for targeted distributions and affected groups. However, doing this assessment manually can be a slow and dangerous process as aid workers are required to go to affected areas which may still be dangerous or fragile. Remote sensors such as satellite imagery and UAVs are therefore used as alternatives to collect information from disaster areas.
While the remote sensors collect the information, humans are still being used to analyse it through visual interpretation. This method is open to mistakes and time pressure.
Identifying and classifying damage has already been tested using two approaches:
A Convolutional Neural Network approach based on outlines from Open Street Map
A histogram mapping approach derived from Univariate Image Differencing
Using Computer Vision, detect damage from the pictures directly and classify the level of damage.
Datasets: geographical subsection of the island of St Maartens, representative of the damage caused on the island
All other 34 UAV datasets
Building outlines from OpenStreetMap
2. Interpreting values for the classification of buildings without empirical testing.
[In this case, the histogram matching and Univariate Change Detection has already been performed on pre- and post event imagery and been aggregated on a building level. This has been achieved through the consideration of every layer separately for the RGB and HSV description of the imagery. The results are the median values of these pixels per building.]
Both algorithms will be graded on the Average F1 score, in which the categories might be weighted.
Problem: The Red Cross’s Missing Maps project helps map areas where humanitarian organisations are trying to meet the needs of vulnerable people.
Missing Maps uses imagery from various sources – mainly from Bing Maps and DigitalGlobe to plot buildings on Open Street Map. However, these sources of satellite imagery have different geo-references, resulting in misalignment of buildings on the Open Street Map. Misalignment ranges from less than a metre up to dozens of metres.
The Red Cross uses Open Street Map data as ground truth for remote sensing techniques and the misalignment therefore creates problems.
Outcome: To write an algorithm that detects misalignment between Open Street Map data and Bing satellite imagery and shifts the Open Street Map building outlines to fit the satellite imagery as accurately as possible.
Training sets from Malawi and The Hague