Constraint Model for the Satellite Image Mosaic Selection Problem

peer reviewedSatellite imagery solutions are widely used to study and monitor different regions of the Earth. However, a single satellite image can cover only a limited area. In cases where a larger area of interest is studied, several images must be stitched together to create a single larger image, called a mosaic, that can cover the area. Today, with the increasing number of satellite images available for commercial use, selecting the images to build the mosaic is challenging, especially when the user wants to optimize one or more parameters, such as the total cost and the cloud coverage percentage in the mosaic. More precisely, for this problem the input is an area of interest, several satellite images intersecting the area, a list of requirements relative to the image and the mosaic, such as cloud coverage percentage, image resolution, and a list of objectives to optimize. We contribute to the constraint and mixed integer lineal programming formulation of this new problem, which we call the satellite image mosaic selection problem, which is a multi-objective extension of the polygon cover problem. We propose a dataset of realistic and challenging instances, where the images were captured by the satellite constellations SPOT, Pléiades and Pléiades Neo. We evaluate and compare the two proposed models and show their efficiency for large instances, up to 200 images

Introduction to the Satellite Image Mosaic Combination Problem

peer reviewedGovernments and military forces are no longer solely occupying the space industry market, which continues to grow rapidly. According to a recent European Union Space Program Agency report, the Global Satellite Navigation and Earth Observation (EO) market reached revenues of around 200 billion euros in 2022 and is expected to reach 500 billion euros by 2031. As access to space has become cheaper, more private companies have entered the space business. Some companies even use space data without owning any space assets, thanks to services such as satellite-as-a-service. Thanks to advances in satellite design and high-resolution remote sensors, the EO sector has experienced significant growth in recent years. In 2021, the number of satellites dedicated to EO was more effective than the number of launches from 2012-2016. In 2020, more than 100 terabytes of satellite images were generated per day. This research focuses on the combinatorial optimization problem of selecting a set of satellite images that form a mosaic covering the interested area. The goal is to recommend a collection of images that meet the user's criteria by optimizing specific parameters, for example, the total cost of the images or the image resolution. The main contribution of this abstract is the presentation and modeling of the problem, which we call the Satellite Image Mosaic Combination Problem (SIMCOP). At higher levels of abstraction, some similarities can be found between SIMCOP and the Cloud Brokering Problem, especially for the bundled version. Another problem that can be somehow related to SIMCOP is the Internet Shopping Optimization Problem in various variations, where a customer plans to buy products from online stores.9. Industry, innovation and infrastructur

Satellite Image Mosaic Combination Problem

peer reviewed9. Industry, innovation and infrastructur

Evaluation of a quality improvement intervention to reduce anastomotic leak following right colectomy (EAGLE): pragmatic, batched stepped-wedge, cluster-randomized trial in 64 countries

Background Anastomotic leak affects 8 per cent of patients after right colectomy with a 10-fold increased risk of postoperative death. The EAGLE study aimed to develop and test whether an international, standardized quality improvement intervention could reduce anastomotic leaks. Methods The internationally intended protocol, iteratively co-developed by a multistage Delphi process, comprised an online educational module introducing risk stratification, an intraoperative checklist, and harmonized surgical techniques. Clusters (hospital teams) were randomized to one of three arms with varied sequences of intervention/data collection by a derived stepped-wedge batch design (at least 18 hospital teams per batch). Patients were blinded to the study allocation. Low- and middle-income country enrolment was encouraged. The primary outcome (assessed by intention to treat) was anastomotic leak rate, and subgroup analyses by module completion (at least 80 per cent of surgeons, high engagement; less than 50 per cent, low engagement) were preplanned. Results A total 355 hospital teams registered, with 332 from 64 countries (39.2 per cent low and middle income) included in the final analysis. The online modules were completed by half of the surgeons (2143 of 4411). The primary analysis included 3039 of the 3268 patients recruited (206 patients had no anastomosis and 23 were lost to follow-up), with anastomotic leaks arising before and after the intervention in 10.1 and 9.6 per cent respectively (adjusted OR 0.87, 95 per cent c.i. 0.59 to 1.30; P = 0.498). The proportion of surgeons completing the educational modules was an influence: the leak rate decreased from 12.2 per cent (61 of 500) before intervention to 5.1 per cent (24 of 473) after intervention in high-engagement centres (adjusted OR 0.36, 0.20 to 0.64; P < 0.001), but this was not observed in low-engagement hospitals (8.3 per cent (59 of 714) and 13.8 per cent (61 of 443) respectively; adjusted OR 2.09, 1.31 to 3.31). Conclusion Completion of globally available digital training by engaged teams can alter anastomotic leak rates. Registration number: NCT04270721 (http://www.clinicaltrials.gov)