Satellite Image Analysis for Disaster and Crisis-Management Support

By S. Voigt, T. Kemper, T. Riedlinger, R. Kiefl, K. Scholte, H. Mehl. IEEE Transactions on Geoscience and Remote Sensing, 2007

390 citations recorded by the source corpus.

Subfields: Aerospace Engineering, Global and Planetary Change, Ocean Engineering

View Paper: https://www.semanticscholar.org/paper/20c18a6895130ba6617ce6a59b8a0eb8173981e7

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Citations in the corpus

A review of the status of satellite remote sensing and image processing techniques for mapping natural hazards and disasters (2009, methodology): “landslide mapping (Singhroy, 1995; Singhroy et al., 1998; Ostir et al., 2003; Nichol and Wong, 2005a; Kumar et al., 2006; Voigt et al., 2007).”
Earthquake Damage Assessment of Buildings Using VHR Optical and SAR Imagery (2010, background): “in the actual scene, generating a low match and classifying the building as ωd. This also occurs for the building ω [ 3 ] that is”
A comprehensive review of earthquake-induced building damage detection with remote sensing techniques (2013, background): “Fortunately, remote sensing techniques, both spaceborne and airborne, can make a very effective contribution (Voigt et al., 2007).”
Tackling Climate Change with Machine Learning (2019, background): “Further, this imagery can assist damage assessment, by comparing scenes immediately pre- and post-disaster [637, 638].”
Evaluation of Different Machine Learning Methods and Deep-Learning Convolutional Neural Networks for Landslide Detection (2019, background): “…occur in the vicinity of human habitations or infrastructures, e.g., power lines, roads, bridges and settlement areas, which means there is often time pressure to detect and delineate landslides affecting certain areas to carry out tasks such as timely support planning and crisis responses [19,20].”
A neural network approach using multi-scale textural metrics from very high-resolution panchromatic imagery for urban land-use classification (2009, background): “In fact, in destructive disasters, such as an earthquake, a prompt and accurate overview of the damage of the human settlements is very important to be able to manage the rescue efforts and, subsequently, to organize restoration activities ( Voigt et al., 2007; Chini et al., 2008b).”
A cloud detection algorithm for satellite imagery based on deep learning (2019, background): “…particular, automated classification is crucial in many use cases, such as harvest yield estimation (Prasad et al., 2006; Ferencz et al., 2004), change detection (Verbesselt et al., 2010; Sakamoto et al., 2005), and disaster management (Voigt et al., 2007; Tralli et al., 2005; Joyce et al., 2009).”
Sentinel-1-based flood mapping: a fully automated processing chain (2016, background): “information source for an effective disaster management, helping humanitarian relief organizations and decision-makers to obtain spatially explicit information about inundated areas in a time- and cost-efficient manner (Voigt et al. 2007).”
Segment Optimization and Data-Driven Thresholding for Knowledge-Based Landslide Detection by Object-Based Image Analysis (2011, background): “Satellite remote sensing technology has proven to be the best tool for generating such landslide inventories, particularly with the availability of high-resolution images [3]–[6].”