Wildfires become more frequent in the context of global warming and severe drought in several parts of the globe. Earth observation data can be used to provide information in such cases, but sometimes, when using optical satellite imagery, the evaluation of the effects produced by ongoing large scale forest fires, can be impeded by smoke. It can reduce the accuracy of the information required by disaster management authorities when allocating resources. To improve both the usability of optical remote sensing data and the quality of the obtained information we compare multiple feature extraction, classification, and visual enhancement methods and algorithms for land cover mapping of smoke covered Sentinel-2 data. The demonstration is performed for the 2019 forest fires in Australia.
Throughout the years, various Earth Observation (EO) satellites have generated huge amounts of data. The extraction of latent information in the data repositories is not a trivial task. New methodologies and tools, being capable of handling the size, complexity and variety of data, are required. Data scientists require support for the data manipulation, labeling and information extraction processes. This paper presents our Earth Observation Image Librarian (EOLib), a modular software framework which offers innovative image data mining capabilities for TerraSAR-X and EO image data, in general. The main goal of EOLib is to reduce the time needed to bring information to end-users from Payload Ground Segments (PGS). EOLib is composed of several modules which offer functionalities such as data ingestion, feature extraction from SAR (Synthetic Aperture Radar) data, meta-data extraction, semantic definition of the image content through machine learning and data mining methods, advanced querying of the image archives based on content, meta-data and semantic categories, as well as 3-D visualization of the processed images. EOLib is operated by DLR's (German Aerospace Center's) Multi-Mission Payload Ground Segment of its Remote Sensing Data Center at Oberpfaffenhofen, Germany.
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