Very High Resolution (VHR) Satellite Imagery: Processing and Applications

Recently, growing interest in the use of remote sensing imagery has appeared to provide synoptic maps of water quality parameters in coastal and inner water ecosystems;, monitoring of complex land ecosystems for biodiversity conservation; precision agriculture for the management of soils, crops, and pests; urban planning; disaster monitoring, etc. However, for these maps to achieve their full potential, it is important to engage in periodic monitoring and analysis of multi-temporal changes. In this context, very high resolution (VHR) satellite-based optical, infrared, and radar imaging instruments provide reliable information to implement spatially-based conservation actions. Moreover, they enable observations of parameters of our environment at greater broader spatial and finer temporal scales than those allowed through field observation alone. In this sense, recent very high resolution satellite technologies and image processing algorithms present the opportunity to develop quantitative techniques that have the potential to improve upon traditional techniques in terms of cost, mapping fidelity, and objectivity. Typical applications include multi-temporal classification, recognition and tracking of specific patterns, multisensor data fusion, analysis of land/marine ecosystem processes and environment monitoring, etc. This book aims to collect new developments, methodologies, and applications of very high resolution satellite data for remote sensing. The works selected provide to the research community the most recent advances on all aspects of VHR satellite remote sensing

Seabed mapping in coastal shallow waters using high resolution multispectral and hyperspectral imagery

Coastal ecosystems experience multiple anthropogenic and climate change pressures. To monitor the variability of the benthic habitats in shallow waters, the implementation of effective strategies is required to support coastal planning. In this context, high-resolution remote sensing data can be of fundamental importance to generate precise seabed maps in coastal shallow water areas. In this work, satellite and airborne multispectral and hyperspectral imagery were used to map benthic habitats in a complex ecosystem. In it, submerged green aquatic vegetation meadows have low density, are located at depths up to 20 m, and the sea surface is regularly affected by persistent local winds. A robust mapping methodology has been identified after a comprehensive analysis of different corrections, feature extraction, and classification approaches. In particular, atmospheric, sunglint, and water column corrections were tested. In addition, to increase the mapping accuracy, we assessed the use of derived information from rotation transforms, texture parameters, and abundance maps produced by linear unmixing algorithms. Finally, maximum likelihood (ML), spectral angle mapper (SAM), and support vector machine (SVM) classification algorithms were considered at the pixel and object levels. In summary, a complete processing methodology was implemented, and results demonstrate the better performance of SVM but the higher robustness of ML to the nature of information and the number of bands considered. Hyperspectral data increases the overall accuracy with respect to the multispectral bands (4.7% for ML and 9.5% for SVM) but the inclusion of additional features, in general, did not significantly improve the seabed map quality.Peer ReviewedPostprint (published version

Long-lasting floods buffer the thermal regime of the Pampas

This work was funded by grants from the National Research Council of Argentina (CONICET), the International Research Development Centre [IDRC-Canada, Project 106601-001], ANPCyT [PRH 27 [PICT 2013-2973; PICT 2014-2790], and the Inter-American Institute for Global Change Research [IAI, CRN II 2031], which is supported by the US National Science Foundation[Grant number 448 GEO-0452325]. We thank Dr. Horacio Zagarese from INTECH for the lagoon temperature dataset provided. We thank the anonymous reviewers for their careful reading of our manuscript and their many insightful comments and suggestions.Peer reviewedPostprin

SEG-ESRGAN: A multi-task network for super-resolution and semantic segmentation of remote sensing images

The production of highly accurate land cover maps is one of the primary challenges in remote sensing, which depends on the spatial resolution of the input images. Sometimes, high-resolution imagery is not available or is too expensive to cover large areas or to perform multitemporal analysis. In this context, we propose a multi-task network to take advantage of the freely available Sentinel-2 imagery to produce a super-resolution image, with a scaling factor of 5, and the corresponding high-resolution land cover map. Our proposal, named SEG-ESRGAN, consists of two branches: the super-resolution branch, that produces Sentinel-2 multispectral images at 2 m resolution, and an encoder–decoder architecture for the semantic segmentation branch, that generates the enhanced land cover map. From the super-resolution branch, several skip connections are retrieved and concatenated with features from the different stages of the encoder part of the segmentation branch, promoting the flow of meaningful information to boost the accuracy in the segmentation task. Our model is trained with a multi-loss approach using a novel dataset to train and test the super-resolution stage, which is developed from Sentinel-2 and WorldView-2 image pairs. In addition, we generated a dataset with ground-truth labels for the segmentation task. To assess the super-resolution improvement, the PSNR, SSIM, ERGAS, and SAM metrics were considered, while to measure the classification performance, we used the IoU, confusion matrix and the F1-score. Experimental results demonstrate that the SEG-ESRGAN model outperforms different full segmentation and dual network models (U-Net, DeepLabV3+, HRNet and Dual_DeepLab), allowing the generation of high-resolution land cover maps in challenging scenarios using Sentinel-2 10 m bands.This work was funded by the Spanish Agencia Estatal de Investigación (AEI) under projects ARTEMISAT-2 (CTM 2016-77733-R), PID2020-117142GB-I00 and PID2020-116907RB-I00 (MCIN/AEI call 10.13039/501100011033). L.S. would like to acknowledge the BECAL (Becas Carlos Antonio López) scholarship for the financial support.Peer ReviewedPostprint (published version

Comparative study of upsampling methods for super-resolution in remote sensing

Many remote sensing applications require high spatial resolution images, but the elevated cost of these images makes some studies unfeasible. Single-image super-resolution algorithms can improve the spatial resolution of a lowresolution image by recovering feature details learned from pairs of low-high resolution images. In this work, several configurations of ESRGAN, a state-of-the-art algorithm for image super-resolution, are tested. We make a comparison between several scenarios, with different modes of upsampling and channels involved. The best results are obtained training a model with RGB-IR channels and using progressive upsampling.This work has been partially supported by the ARTEMISAT-2 (CTM2016-77733-R) and MALEGRA TEC2016-75976-R projects, funded by the Spanish AEI, FEDER funds,and by the Spanish Ministerio de Economía y Competitividad, respectively. L.S.R. would like to acknowledge the BECAL (Becas Carlos Antonio López) scholarship for the financial support.Peer ReviewedPostprint (author's final draft

Single-image super-resolution of sentinel-2 low resolution bands with residual dense convolutional neural networks

Sentinel-2 satellites have become one of the main resources for Earth observation images because they are free of charge, have a great spatial coverage and high temporal revisit. Sentinel-2 senses the same location providing different spatial resolutions as well as generating a multi-spectral image with 13 bands of 10, 20, and 60 m/pixel. In this work, we propose a single-image super-resolution model based on convolutional neural networks that enhances the low-resolution bands (20 m and 60 m) to reach the maximal resolution sensed (10 m) at the same time, whereas other approaches provide two independent models for each group of LR bands. Our proposed model, named Sen2-RDSR, is made up of Residual in Residual blocks that produce two final outputs at maximal resolution, one for 20 m/pixel bands and the other for 60 m/pixel bands. The training is done in two stages, first focusing on 20 m bands and then on the 60 m bands. Experimental results using six quality metrics (RMSE, SRE, SAM, PSNR, SSIM, ERGAS) show that our model has superior performance compared to other state-of-the-art approaches, and it is very effective and suitable as a preliminary step for land and coastal applications, as studies involving pixel-based classification for Land-Use-Land-Cover or the generation of vegetation indices.This work was funded by the Spanish Agencia Estatal de Investigación (AEI) under projects ARTEMISAT-2 (CTM2016-77733-R) and PID2020-117142GB-I00 of the call MCIN/AEI/10.13039/501100011033).Peer ReviewedPostprint (published version

A dual network for super-resolution and semantic segmentation of sentinel-2 imagery

There is a growing interest in the development of automated data processing workflows that provide reliable, high spatial resolution land cover maps. However, high-resolution remote sensing images are not always affordable. Taking into account the free availability of Sentinel-2 satellite data, in this work we propose a deep learning model to generate high-resolution segmentation maps from low-resolution inputs in a multi-task approach. Our proposal is a dual-network model with two branches: the Single Image Super-Resolution branch, that reconstructs a high-resolution version of the input image, and the Semantic Segmentation Super-Resolution branch, that predicts a high-resolution segmentation map with a scaling factor of 2. We performed several experiments to find the best architecture, training and testing on a subset of the S2GLC 2017 dataset. We based our model on the DeepLabV3+ architecture, enhancing the model and achieving an improvement of 5% on IoU and almost 10% on the recall score. Furthermore, our qualitative results demonstrate the effectiveness and usefulness of the proposed approach.This work has been supported by the Spanish Research Agency (AEI) under project PID2020-117142GB-I00 of the call MCIN/AEI/10.13039/501100011033. L.S. would like to acknowledge the BECAL (Becas Carlos Antonio López) scholarship for the financial support.Peer ReviewedPostprint (published version

Treatment for acute uncomplicated diverticulitis without antibiotherapy: systematic review and meta-analysis of randomized clinical trials

Background: Use of antibiotics in selected cases of acute uncomplicated diverticulitis (AUD) has recently been questioned. Objective: The aim of this study is to examine the safety and efficacy of treatment regimens without antibiotics compared with that of traditional treatments with antibiotics in selected patients with AUD. Data sources: PubMed, Medline, Embase, Web of Science, and the Cochrane Library Methods: A systematic review was performed according to PRISMA and AMSTAR guidelines by searching through Medline, Embase, Web of Science, and the Cochrane Library for randomized clinical trials (RCTs) published before December 2022. The outcomes assessed were the rates of readmission, change in strategy, emergency surgery, worsening, and persistent diverticulitis. Study selection: RCTs on treating AUD without antibiotics published in English before December 2022 were included. Intervention: Treatments without antibiotics were compared with treatments with antibiotics. Main outcome measures: The outcomes assessed were the rates of readmission, change in strategy, emergency surgery, worsening, and persistent diverticulitis. Results: The search yielded 1163 studies. Four RCTs with 1809 patients were included in the review. Among these patients, 50.1% were treated conservatively without antibiotics. The meta-analysis showed no significant differences between nonantibiotic and antibiotic treatment groups with respect to rates of readmission [odds ratio (OR) = 1.39; 95% CI: 0.93-2.06; P = 0.11; I-2 = 0%], change in strategy (OR = 1.03; 95% CI: 0.52-2,02; P = 0.94; I-2 = 44%), emergency surgery (OR = 0.43; 95% CI: 0.12-1.53; P = 0.19; I-2 = 0%), worsening (OR = 0.91; 95% CI: 0.48-1.73; P = 0.78; I-2 = 0%), and persistent diverticulitis (OR = 1.54; 95% CI: 0.63-3.26; P = 0.26; I-2 = 0%). Limitations: Heterogeneity and a limited number of RCTs. Conclusions: Treatment for AUD without antibiotic therapy is safe and effective in selected patients. Further RTCs should confirm the present findings

Intramural child burials in Iron Age Navarra: How ancient DNA can contribute to household archaeology

The transition from the Late Bronze to the Iron Age on the Iberian Peninsula saw a shift in mortuary customs from mainly inhumation to cremation of the deceased. The poor preservation characteristic of cremated skeletal remains has hindered molecular analyses (isotope analyses, ancient DNA) of the Iberian Final Bronze and Iron Age communities of Iberia. Incidentally, a limited number of young children, often newborns, were exempt from the predominant cremation ritual, in favour of intramural inhumations inside buildings at certain settlements. The discourse surrounding the mean- ing and interpretation of this particular burial rite has developed over a long time in Iberian archaeology but has always been hampered by the limited anthropological, archaeological, and molecular data from these intramural inhumations. Here, we study the genomes of 37 intramurally buried children found in three Early Iron Age settlements, dated between c. 800–450 BC. Population genetic analyses on the newly reported individuals extend our understanding of ancient Iberia by revealing previously unsampled genetic diversity as well as showing a lesser influence of Mediterranean ancestry than on previously published Iron Age individuals from northern Spain. We also provide insights into the sex and biological relatedness of the children, and in so doing, elucidate differ- ent aspects of the intramural burial ritual and building use in settlements. More broadly, the genetic data from these individuals fill an important gap in the archaeogenetic record of northern Spain and offer a unique opportunity to study the genetic makeup and population changes from the Bronze Age to Antiquity.This project has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement number 851511). It has also been supported by the research project »Convergence and interaction between complex Bronze Age societies« from the Academia program of the Institució Catalana de Recerca i Estudis Avançats (ICREA) of the Catalan Government and the Spanish Ministry for Science and Innovation (PID2020-112909GB-100)

Religious freedom before, during and after Covid-19 between Europe and the Member States

Interventi tenuti il 26 novembre 2021 in occasione del Seminario di studio sul tema “Religious freedom before, during and after covid-19 between Europe and the Member States” organizzato dall’Eulerit Academic Forum dell’Università degli Studi di Trieste col supporto del Modulo Jean Monnet su “The European impact on the regulation Law&Religion in Italy and Beyond