Fraunhofer-Gesellschaft

Publica

Hier finden Sie wissenschaftliche Publikationen aus den Fraunhofer-Instituten.

Dwelling Detection on VHR Satellite Imagery of Refugee Camps Using a Faster R-CNN

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

:
Fulltext urn:nbn:de:0011-n-5959003 (7.4 MByte PDF)
MD5 Fingerprint: 558a119e8a43be145d6ac05051fe6709
Created on: 21.7.2020


Yurish, S.Y. ; International Frequency Sensor Association -IFSA-, Brussels:
Advances in Signal Processing and Artificial Intelligence : Proceedings of the 2nd International Conference on Advances in Signal Processing and Artificial Intelligence 18 - 20 November 2020 Berlin, Germany
Barcelona: IFSA Publishing, 2020
ISBN: 978-84-09-21931-5
ISBN: 978-84-09-21930-8
pp.7-11
International Conference on Advances in Signal Processing and Artificial Intelligence (ASPAI) <2, 2020, Berlin>
Bundesministerium für Bildung und Forschung BMBF (Deutschland)
13N14716 bis 13N14723; HUMAN+
English
Conference Paper, Electronic Publication
Fraunhofer IAIS ()
Artificial intelligence (AI); Machine learning {ML); Convolutional neural network (CNN); Faster R-CNN; Remote sensing (RS); Dwelling detection; Object detection; Pattern recognition

Abstract
The management of humanitarian operations in highly intense situations like migration movements happening at borders often lack current and suflicient 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 estimated for forecasting purposes. To count the dwellings, object detection machine learning methods can be used. Inthis paper, a dwelling detection workflow using a Faster R-CNN i s described To train the Faster R-CNN, a fast training data annotation workflow was developed. The Faster R-CNN outputs an estimate of people living in a camp and a con.fidence factor, giving a global evaluation metric about the quality of the analysis of the image andby that of the calculation itself. This workflow yields results that can be used in humanitarian operations. So our related proposal is to get satellite images fast, evaluate them with our method, and have better numbers für contingency and capacity planning. By this, stress für all people involved in a humanitarian (crisis) situation can be reduced.

: http://publica.fraunhofer.de/documents/N-595900.html