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Camille Parisel

Publications and source records attributed to Camille Parisel.

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The On-Sky Performance of the LSST Camera CCD Array

The focal plane of the LSST Camera contains 189 individual science CCDs, arranged into 21 raft tower modules, along with 4 wavefront and 8 guider CCDs located in 4 additional corner RTMs. Altogether, the LSST Camera CCDs compose the largest focal plane ever constructed. The LSST Camera is the primary instrument of Rubin Observatory, which will begin the Legacy Survey of Space and Time in 2026. In this paper, we describe the on-sky performance of the LSST Camera CCDs, from receipt at NSF/DOE Vera C. Rubin Observatory in May 2024 to on-sky observations during the first year of operations. We discuss the process to establish functionality of several CCDs which were affected by an electrical short and faulty analog-digital converter, optimizations of readout timing in response to changes in the survey strategy, and implementation of enhanced focal plane safety measures through an active clearing mechanism on the CCDs. Finally, we discuss sensor features observed on-sky, and global performance during the first year of operations. The operations to date of the LSST Camera CCDs have demonstrated the capability of performing a wide, fast, and deep optical imaging survey of the entire southern sky at the Rubin Observatory.

astro-ph.IM

Very High Resolution Land Cover Mapping of Urban Areas at Global Scale with Convolutional Neural Networks

This paper describes a methodology to produce a 7-classes land cover map of urban areas from very high resolution images and limited noisy labeled data. The objective is to make a segmentation map of a large area (a french department) with the following classes: asphalt, bare soil, building, grassland, mineral material (permeable artificialized areas), forest and water from 20cm aerial images and Digital Height Model. We created a training dataset on a few areas of interest aggregating databases, semi-automatic classification, and manual annotation to get a complete ground truth in each class. A comparative study of different encoder-decoder architectures (U-Net, U-Net with Resnet encoders, Deeplab v3+) is presented with different loss functions. The final product is a highly valuable land cover map computed from model predictions stitched together, binarized, and refined before vectorization.

eess.IV