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

: Wickert, Lorenz; Bogen, Manfred; Richter, Marvin

Volltext urn:nbn:de:0011-n-6335567 (455 KByte PDF)
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Erstellt am: 30.3.2021

Sensors & Transducers Journal 249 (2021), Nr.2, S.45-53
ISSN: 2306-8515
ISSN: 1726-5479
European Commission EC
H2020; 769373; FORESEE
Future proofing strategies FOr RESilient transport networks against Extreme Events
Zeitschriftenaufsatz, Elektronische Publikation
Fraunhofer IAIS ()
artificial intelligence (AI); machine learning (ML); deep learning; convolutional neural network (CNN); Remote Sensing (RS); Dwelling Detection; object detection; lessons learned

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.