Stefan Voigt

TL;DR: An automatic near-real time (NRT) flood detection approach is presented, which combines histogram thresholding and segmentation based classification, specifically oriented to the analysis of single-polarized very high resolution Synthetic Aperture Radar (SAR) satellite data.

TL;DR: This paper describes successful rapid satellite mapping campaigns supporting disaster relief and demonstrates how this technology can be used for civilian crisis-management purposes and reports on rapid-mapping experiences gained during various disaster-response applications.

TL;DR: In this paper, the authors introduce the problem of coal fires referring to two coalfields in north-central China and investigate the environmental impacts of the fires, such as atmospheric influences, land subsidence, landscape degradation, and the danger for water resources and human health.

TL;DR: It is shown that satellite-based emergency mapping is most intensively deployed in Asia and Europe and follows well the geographic, physical, and temporal distributions of global natural disasters.

TL;DR: The experiments that were performed on a bitemporal TerraSAR-X StripMap data set from South West England during and after a large-scale flooding in 2007 confirm the effectiveness of the proposed change detection method and show an increased classification accuracy of the hybrid MRF model in comparison to the sole application of the HMAP estimation.