Integrating pre-processing pipelines in ODC based framework

Using on-demand processing pipelines to generate virtual geospatial products is beneficial to optimizing resource management and decreasing processing requirements and data storage space. Additionally, pre-processed products improve data quality for data-driven analytical algorithms, such as machine learning or deep learning models. This paper proposes a method to integrate virtual products based on integrating open-source processing pipelines. In order to validate and evaluate the functioning of this approach, we have integrated it into a geo-imagery management framework based on Open Data Cube (ODC). To validate the methodology, we have performed three experiments developing on-demand processing pipelines using multi-sensor remote sensing data, for instance, Sentinel-1 and Sentinel-2. These pipelines are integrated using open-source processing frameworks.Comment: 4 pages, 5 figures, IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposiu

Le chêne chevelu en structure.

Quercus cerris est une des espèces les plus importantes d'Italie. Il est difficile à scier et coller. Malgré ce défaut, un débouché qui paraîtrait bien valoriser ses propriétés est l'utilisation en structure. Après un classement selon des critères visuels, les pièces du test ont ensuite été testées avec trois méthodes non destructives, telles que ultrason, les oscillations libres et la mesure des modules d'élasticité apparent

Geo-imagery management and statistical processing in a regional context using Open Data Cube

We propose a methodology to manage and process remote sensing and geo-imagery data for non-expert users. The proposed system provides automated data ingestion and manipulation capability for analytical data-driven purposes. In this paper, we describe the technological basis of the proposed method in addition to describing the tool architecture, the inherent data flow, and its operation in a specific use case to provide statistical summaries of Sentinel-2 regions of interest corresponding to the cultivation of polygonal areas located in the Basque Country (ES).Comment: 4 pages, 4 figures, Published in 2021 IEEE International Geoscience and Remote Sensing Symposium IGARS

Polarimetric SAR Image Segmentation with B-Splines and a New Statistical Model

We present an approach for polarimetric Synthetic Aperture Radar (SAR) image region boundary detection based on the use of B-Spline active contours and a new model for polarimetric SAR data: the GHP distribution. In order to detect the boundary of a region, initial B-Spline curves are specified, either automatically or manually, and the proposed algorithm uses a deformable contours technique to find the boundary. In doing this, the parameters of the polarimetric GHP model for the data are estimated, in order to find the transition points between the region being segmented and the surrounding area. This is a local algorithm since it works only on the region to be segmented. Results of its performance are presented

Cloud Based N-Dimensional Weather Forecast Visualization Tool with Image Analysis Capabilities

We have designed and implemented a framework that permits remote access to weather forecasts

Query by Example in Earth-Observation Image Archive Using Data Compression-Based Approach

This paper presents an implementation of query by example in Earth Observation image archive using data compression-based approach. Data compression approach allows to exploit the compression properties of the objects and to estimate the shared information between them, this concept is extended to image retrieval for finding similar objects in the image archive. Our implementation is based on LZW algorithm for compressing the image content and extracting features of the images. The fast compression distance (FCD) is defined as a similarity metric in order to retrieve the most similar images. This tool is satisfactory implemented and tested using optical and SAR images

Storehouse: a Reinforcement Learning Environment for Optimizing Warehouse Management

Warehouse Management Systems have been evolving and improving thanks to new Data Intelligence techniques. However, many current optimizations have been applied to specific cases or are in great need of manual interaction. Here is where Reinforcement Learning techniques come into play, providing automatization and adaptability to current optimization policies. In this paper, we present Storehouse, a customizable environment that generalizes the definition of warehouse simulations for Reinforcement Learning. We also validate this environment against state-of-the-art reinforcement learning algorithms and compare these results to human and random policies

Visual analytics for built-up area understanding from metric resolution Earth observation data

Large scale archives can benefit the application of visual analytics methodologies aimed at characterizing their contents by the effective inclusion of the human analyst in the interpretation loop. Exploiting the knowledge of users that are not remote sensing experts requires the design of easy to use applications. Applied analytical reasoning by visual representations involves methodological aspects dealing with both the design of multiple interactive visualizations as well as data representation and transformation considerations. We present examples of such methodological aspects aiming at the understanding and characterization of metric resolution datasets acquired on urban environments