The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume XLIII-B3-2020
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B3-2020, 233–240, 2020
https://doi.org/10.5194/isprs-archives-XLIII-B3-2020-233-2020
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B3-2020, 233–240, 2020
https://doi.org/10.5194/isprs-archives-XLIII-B3-2020-233-2020

  21 Aug 2020

21 Aug 2020

SUPPORTING THE MANAGEMENT OF HUMANITARIAN OPERATIONS CONCERNING MIGRATION MOVEMENTS WITH REMOTE SENSING

L. Wickert, M. Bogen, and M. Richter L. Wickert et al.
  • Fraunhofer IAIS, Schloss Birlinghoven, 53757 Sankt Augustin, Germany

Keywords: Monitoring and Management, Remote Sensing (RS), Artificial Intelligence (AI), Machine Learning (MS), Convolutional Neural Networks (CNNs), Faster R-CNN, Object Detection, Dwelling Detection

Abstract. The various forms of humanitarian operations include operations concerning the management of migrant movements and refugees. Managing those operations is non-trivial. A large number of refugees have to be welcomed, registered, forwarded, and be given supplies and accommodation. This is due to a lack of current and sufficient information about the refugees, making planning and execution of operations challenging, expensive and cumbersome. The earlier information about the refugees is available, the better. The method “Dwelling Detection”, conducted on satellite imagery of refugee camps, can provide large-scale heads-up information fast, complementing information already available to operators at the ground. With “Dwelling Detection”, dwellings in a camp and their extent are detected using machine learning methods. An estimate of inhabitants of the camp is computed using the number and the extent of the detected dwellings. Our workflow uses a Faster R-CNN, an object detection network. To train the network, we developed a fast training data annotation workflow. We use the dwellings detected by the faster R-CNN to estimate a number of inhabitants. The quality of the analysis can be evaluated by a confidence-metric, computed out of the results of the Faster R-CNN. The results can be used in humanitarian operations. We tested the workflow using different configurations and data. From those tests, we give recommendations on how to build a dwelling detection classifier. We propose to humanitarian operators to build a dwelling detection classifier according to our recommendations and use satellite images in actual humanitarian operations. This could help to reduce stress for all people involved in a humanitarian (crisis) situation.