Abstract. The Theia Snow collection routinely provides high-resolution maps of the snow-covered area from Sentinel-2 and Landsat-8 observations. The collection covers selected areas worldwide, including the main mountain regions in western Europe (e.g. Alps, Pyrenees) and the High Atlas in Morocco. Each product of the Theia Snow collection contains four classes: snow, no snow, cloud and no data. We present the algorithm to generate the snow products and provide an evaluation of the accuracy of Sentinel-2 snow products using in situ snow depth measurements, higher-resolution snow maps and visual control. The results suggest that the snow is accurately detected in the Theia snow collection and that the snow detection is more accurate than the Sen2Cor outputs (ESA level 2 product). An issue that should be addressed in a future release is the occurrence of false snow detection in some large clouds. The snow maps are currently produced and freely distributed on average 5 d after the image acquisition as raster and vector files via the Theia portal (https://doi.org/10.24400/329360/F7Q52MNK).

Orfeo ToolBox is an open-source project for state-of-the-art remote sensing, including a fast image viewer, applications callable from command-line, Python or QGIS, and a powerful C++ API. This article is an introduction to the Orfeo ToolBox's flagship features from the point of view of the two communities it brings together: remote sensing and software engineering.

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Processing large very high-resolution remote sensing images on resource-constrained devices is a challenging task because of the large size of these data sets. For applications such as environmental monitoring or natural resources management, complex algorithms have to be used to extract information from the images. The memory required to store the images and the data structures of such algorithms may be very high (hundreds of gigabytes) and therefore leads to unfeasibility on commonly available computers. Segmentation algorithms constitute an essential step for the extraction of objects of interest in a scene and will be the topic of the investigation in this paper. The objective of the present work is to adapt image segmentation algorithms for large amounts of data. To overcome the memory issue, large images are usually divided into smaller image tiles, which are processed independently. Region-merging algorithms do not cope well with image tiling since artifacts are present on the tile edges in the final result due to the incoherencies of the regions across the tiles. In this paper, we propose a scalable tile-based framework for region-merging algorithms to segment large images, while ensuring identical results, with respect to processing the whole image at once. We introduce the original concept of the stability margin for a tile. It allows ensuring identical results to those obtained if the whole image had been segmented without tiling. Finally, we discuss the benefits of this framework and demonstrate the scalability of this approach by applying it to real large images.

A large-scale feature selection wrapper is discussed for the classification of high dimensional remote sensing. An efficient implementation is proposed based on intrinsic properties of Gaussian mixtures models and block matrix. The criterion function is split into two parts : one that is updated to test each feature and one that needs to be updated only once per feature selection. This split saved a lot of computation for each test. The algorithm is implemented in C++ and integrated into the Orfeo Toolbox. It has been compared to other classification algorithms on two high dimension remote sensing images. Results show that the approach provides good classification accuracies with low computation time.