pyFAI is an open-source software package designed to perform azimuthal integration and, correspondingly, two-dimensional regrouping on area-detector frames for small-and wide-angle X-ray scattering experiments. It is written in Python (with binary submodules for improved performance), a language widely accepted and used by the scientific community today, which enables users to easily incorporate the pyFAI library into their processing pipeline. This article focuses on recent work, especially the ease of calibration, its accuracy and the execution speed for integration.
Recently, a vast amount of satellite data has become available, going beyond standard optical (EO) data to other forms such as synthetic aperture radars (SAR). While more robust, SAR data are often more difficult to interpret, can be of lower resolution, and require intense pre-processing compared to EO data. On the other hand, while more interpretable, EO data often fail under unfavourable lighting, weather, or cloud-cover conditions. To leverage the advantages of both domains, we present a novel autoencoder-based architecture that is able to both (i) fuse multi-spectral optical and radar data in a common shared-space, and (ii) perform image segmentation for building footprint detection under the assumption that one of the data modalities is missing-resembling a situation often encountered under real-world settings. To do so, a novel randomized skip-connection architecture that utilizes autoencoder weight-sharing is designed. We compare the proposed method to baseline approaches relying on network fine-tuning, and established architectures such as UNet. Qualitative and quantitative results show the merits of the proposed method, that outperforms all compared techniques for the task-at-hand.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations鈥揷itations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.