Lessons Learned on Conducting Dwelling Detection on VHR Satellite Imagery for the Management of Humanitarian Operations

The management of humanitarian operations in highly intense situations like migration movements happening at borders often lack current and sufficient information. Satellites do provide large-scale information fast. When dealing with a migration situation, satellite images now can give information about where refugees are before they arrive at a border, giving first responders urgently needed lead time for contingency and capacity planning. Dwelling Detection, a method conducted on satellite images of refugee camps, is able to count the dwellings in a camp. From that, the number of inhabitants in a camp can be derived for forecasting purposes. To count the dwellings, object detection machine learning methods can be used. In Wickert et al. [ASPAI' 2020, 1,2020], a dwelling detection workflow using a Faster R-CNN is described. The workflow contains a newly developed annotation method, an inhabitant estimate for analyzed camps and a global confidence factor indicating the quality of the analysis of the image and the estimate of the inhabitants. In this actual extension of Wickert et. al. [ASPAI 2020, 1, 2020], lessons learned from multiple training and testing runs are documented, following a detailed analysis of those tests and validations in Wickert et. al. [ISPRS 2020, 2, 2020]. In this extended article we conclude that the workflow produces results that can be used in humanitarian operations. We further document our lessons learned in developing a dwelling detection workflow and we provide recommendations for training a dwelling detection classifier. We advise humanitarian operators to build a dwelling detection classifier following our recommendations and use satellite images in actual humanitarian operations. This approach can reduce stress for all people involved in a humanitarian (crisis) situation and lead to better decisions in intense migration situations.