The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume XL-3/W3
https://doi.org/10.5194/isprsarchives-XL-3-W3-371-2015
https://doi.org/10.5194/isprsarchives-XL-3-W3-371-2015
19 Aug 2015
 | 19 Aug 2015

UAV DATA PROCESSING FOR RAPID MAPPING ACTIVITIES

W. Tampubolon and W. Reinhardt

Keywords: rapid disaster mapping, accuracy of classification

Abstract. During disaster and emergency situations, geospatial data plays an important role to serve as a framework for decision support system. As one component of basic geospatial data, large scale topographical maps are mandatory in order to enable geospatial analysis within quite a number of societal challenges.

The increasing role of geo-information in disaster management nowadays consequently needs to include geospatial aspects on its analysis. Therefore different geospatial datasets can be combined in order to produce reliable geospatial analysis especially in the context of disaster preparedness and emergency response. A very well-known issue in this context is the fast delivery of geospatial relevant data which is expressed by the term “Rapid Mapping”.

Unmanned Aerial Vehicle (UAV) is the rising geospatial data platform nowadays that can be attractive for modelling and monitoring the disaster area with a low cost and timely acquisition in such critical period of time. Disaster-related object extraction is of special interest for many applications.

In this paper, UAV-borne data has been used for supporting rapid mapping activities in combination with high resolution airborne Interferometric Synthetic Aperture Radar (IFSAR) data. A real disaster instance from 2013 in conjunction with Mount Sinabung eruption, Northern Sumatra, Indonesia, is used as the benchmark test for the rapid mapping activities presented in this paper. On this context, the reliable IFSAR dataset from airborne data acquisition in 2011 has been used as a comparable dataset for accuracy investigation and assessment purpose in 3 D reconstructions. After all, this paper presents a proper geo-referencing and feature extraction method of UAV data to support rapid mapping activities.