BEWATS: BEACH WASTE TRACKING SYSTEM USING SATELLITE, UAV’s and MARINE DYNAMICS MODELS. 

2021 ◽  
Author(s):  
Omjyoti Dutta ◽  
Beatriz Revilla-Romero ◽  
Adrian Sanz-Díaz ◽  
Fernando Martin-Rodriguez ◽  
Orentino Mojon-Ojea ◽  
...  
Keyword(s):  
Industrial Development ◽  
Tracking System ◽  
Synthetic Data ◽  
Earth Observation ◽  
Machine Learning Techniques ◽  
Marine Litter ◽  
North West ◽  
Area Of Interest ◽  
Dynamics Models ◽  
Sentinel 2

<p>Marine litter is a growing problem that advances parallel to economic and industrial development and seriously affects ecosystems. One of the most abundant pollutants are plastics. The BEWATS project focuses on innovative tools for remote marine litter control and management through satellite and UAV’s. The areas of study are currently at the Vigo coast in Galicia (North-West of Spain). In this area, there are many high natural value beaches including Nature Reserve and part of a National Park. These beaches are receiving an increasing amount of marine litter, mainly plastic, helped by strong currents in the area. Every few months, these beaches are clean and the collected litter information tracked. In this context, the BEWATS project concentrates on tracking the possible path through which marine litter reaches the area of interest. In this presentation, we will discuss how this is achieved by data fusion from UAV imagery, marine dynamics model simulations and Earth-observation satellite data (Sentinel-2). To detect possible marine litter, we have developed a novel synthetic data-based approach to marine litter detection using Sentinel-2 images and machine learning techniques. Within this approach, one can classify and quantify according to pixel-level litter fraction present. We have validated our approach with existing open-sourced available datasets.  </p><p>The BEWATS project is led by Vigo University, which provides UAV’s imagery, and the Spanish Research Council (CSIC) provides marine dynamics models for tracking waste routes and delineation of waste concentration zones. In this context, GMV provides Earth observation based solution of detecting marine litter. BEWATS is founded by the Biodiversity Foundation of the Spanish Ministry for the Ecological Transition and the Demographic Challenge.</p>

Simulating Marine Litter observations from space to support Operations Research

2021 ◽  
Author(s):  
Stephen Emsley ◽  
Manuel Arias ◽  
Théodora Papadopoulou ◽  
François-Régis Martin-Lauzer
Keyword(s):  
Spatial Resolution ◽  
Operations Research ◽  
Open Space ◽  
Wind Waves ◽  
Synthetic Data ◽  
Atmospheric Conditions ◽  
Marine Litter ◽  
Model Components ◽  
River Mouths ◽  
Sentinel 2

<p>An breadboard for end-to-end (E2E) Marine Litter Optical Performance Simulations (ML-OPSI) is being designed in the frame of the ESA Open Space Innovation Platform (OSIP) Campaign to support Earth Observation (EO) scientists with the design of computational experiments for Operations Research. The ML-OPSI breadboard will estimate Marine Litter signal at Top-Of-Atmosphere (TOA) from a set of Bottom-Of-Atmosphere (BOA) scenarios representing the various case studies by the community (e.g., windrows, frontal areas, river mouths, sub-tropical gyres), coming from synthetic data (computer-simulated) or from real observations. It is a modular, pluggable and extensible framework, promoting re-use and be adapted for different missions, sensors and scenarios.</p><p>The breadboard consists of (a) the OPSI components for the simulation i.e. the process of using a model to study the characteristics of the system by manipulating variables and by studying the properties of the model allowing an evaluation to optimise performance and make predictions about the real system; and (b) the Marine Litter model components for the detection of marine litter. It shall consider the changes caused in the water reflectance and properties due to marine litter, exploiting gathered information of plastic polymers, different viewing geometries, and atmospheric conditions as naturally occurring. The modules of the breadboard include a Scenario Builder Module (SB) with maximum spatial resolution and best modelling as possible of the relevant physical properties, which for spectral sensors could include high spatial resolution and high spectral density/resolution BOA radiance simulations in the optical to SWIR bands; a Radiative Transfer Module (RTM) transforming water-leaving to TOA reflectance for varying atmospheric conditions and observational geometries; a Scene Generator Module (SGM) which could use Sentinel-2, Landsat, or PRISMA data as reference or any other instrument as pertinent; a Performance Assessment Module (PAM) for ML detection that takes into account the variability of the atmosphere, the sunlight & skylight at BOA, the sea-surface roughness with trains of wind waves & swells, sea-spray (whitecaps), air bubbles in the mixed layer, marine litter dynamics as well as instrumental noise to assess marine litter detection feasibility.</p><p>Marine Litter scenarios of reference shall be built based on in-situ campaigns, to reflect the true littering conditions at each case, both in spatial distribution and composition. The breadboard shall be validated over artificial targets at sea in field campaigns as relevant. This might include spectral measurements from ASD, on-field radiometers, and cameras on UAVs, concomitant with Copernicus Sentinel-2 acquisitions. Combined, they can be used to estimate atmospheric contribution and assess performance of the testes processing chain.</p><p>This activity collaborates on the ““Remote Sensing of Marine Litter and Debris” IOCCG taskforce.</p>

scholarly journals Extracting multilayer networks from Sentinel-2 satellite image time series

2020 ◽  
Vol 8 (S1) ◽  
pp. S26-S42 ◽  
Author(s):  
Roberto Interdonato ◽  
Raffaele Gaetano ◽  
Danny Lo Seen ◽  
Mathieu Roche ◽  
Giuseppe Scarpa
Keyword(s):  
Remote Sensing ◽  
Time Series ◽  
Satellite Image ◽  
European Space Agency ◽  
Image Data ◽  
Earth Observation ◽  
Supervised Machine Learning ◽  
Machine Learning Techniques ◽  
Case Scenario ◽  
Sentinel 2

AbstractNowadays, modern Earth Observation systems continuously generate huge amounts of data. A notable example is the Sentinel-2 Earth Observation mission, developed by the European Space Agency as part of the Copernicus Programme, which supplies images from the whole planet at high spatial resolution (up to 10 m) with unprecedented revisit time (every 5 days at the equator). In this data-rich scenario, the remote sensing community is showing a growing interest toward modern supervised machine learning techniques (e.g., deep learning) to perform information extraction, often underestimating the need for reference data that this framework implies. Conversely, few attention is being devoted to the use of network analysis techniques, which can provide a set of powerful tools for unsupervised information discovery, subject to the definition of a suitable strategy to build a network-like representation of image data. The aim of this work is to provide clues on how Satellite Image Time Series can be profitably represented using complex network models, by proposing a methodology to build a multilayer network from such data. This is the first work to explore the possibility to exploit this model in the remote sensing domain. An example of community detection over the provided network in a real-case scenario for the mapping of complex land use systems is also presented, to assess the potential of this approach.

The Sen2Cube.at national semantic Earth observation data cube for Austria 

2021 ◽  
Author(s):  
Martin Sudmanns ◽  
Hannah Augustin ◽  
Lucas van der Meer ◽  
Andrea Baraldi ◽  
Dirk Tiede
Keyword(s):  
Inference Engine ◽  
Earth Observation ◽  
Data Cube ◽  
Observation Data ◽  
Semantic Enrichment ◽  
Area Of Interest ◽  
Information Layer ◽  
Semantic Models ◽  
Earth Observation Data ◽  
Sentinel 2

<div> <p>The Sen2Cube.at is a Sentinel-2 semantic Earth observation (EO) data and information cube that combines an EO data cube with an AI-based inference engine by integrating a computer-vision approach to infer new information. Our approach uses semantic enrichment of optical images and makes the data and information directly available and accessible for further use within an EO data cube. The architecture is based on an expert system, in which domain-knowledge can be encoded in semantic models (knowledgebase) and applied to the Sentinel-2 data as well as semantically enriched, data-derived information (factbase).  </p> </div><div> <p>The initial semantic enrichment in the Sen2Cube.at system is general-purpose, user- and application-independent, derived directly from optical EO images as an initial step towards a scene classification map. These information layers are automatically generated from Sentinel-2 images with the SIAM software (Satellite Image Automated Mapper). SIAM is a knowledge-based and physical-model-based decision tree that produces a set of information layers in a fully automated process that is applicable worldwide and does not require any samples. A graphical inference engine allows application-specific Web-based semantic querying based on the generic information layer as a replicable and explainable approach to produce information. The graphical inference engine is a new Browser-based graphical user interface (GUI) developed in-house with a semantic querying language. Users formulate semantic models in a graphical way and can execute them on any area-of-interest and time interval, which will be evaluated by the core of the inference engine attached to the data cube. This also enables non-expert users to formulate analyses without requiring programming skills.  </p> </div><div> <p>While the methodology is software-independent, the prototype is based on the Open Data Cube and additional in-house developed components in the Python programming language. Scaling is possible depending on the available infrastructure resources due to the system’s Docker-based container architecture. Through its fully automated semantic enrichment, innovative graphical querying language in the GUI for semantic querying and analysis as well as the implementation as a scalable infrastructure, this approach is suited for big data analysis of Earth observation data. It was successfully scaled to a national data cube for Austria, containing all available Sentinel-2 images from the platforms A and B. </p> </div>

scholarly journals A Comparison of Optimized Sentinel-2 Super-Resolution Methods Using Wald’s Protocol and Bayesian Optimization

2021 ◽  
Vol 13 (11) ◽  
pp. 2192
Author(s):  
Sveinn E. Armannsson ◽  
Magnus O. Ulfarsson ◽  
Jakob Sigurdsson ◽  
Han V. Nguyen ◽  
Johannes R. Sveinsson
Keyword(s):  
Synthetic Data ◽  
Super Resolution ◽  
Earth Observation ◽  
Bayesian Optimization ◽  
Multispectral Images ◽  
Regression Kriging ◽  
Tuning Parameters ◽  
Quantitative Results ◽  
Reduced Scale ◽  
Sentinel 2

In the context of earth observation and remote sensing, super-resolution aims to enhance the resolution of a captured image by upscaling and enhancing its details. In recent years, numerous methods for super-resolution of Sentinel-2 (S2) multispectral images have been suggested. Most of those methods depend on various tuning parameters that affect how effective they are. This paper’s aim is twofold. Firstly, we propose to use Bayesian optimization at a reduced scale to select tuning parameters. Secondly, we choose tuning parameters for eight S2 super-resolution methods and compare them using real and synthetic data. While all the methods give good quantitative results, Area-To-Point Regression Kriging (ATPRK), Sentinel-2 Sharpening (S2Sharp), and Sentinel-2 Symmetric Skip Connection convolutional neural network (S2 SSC) perform markedly better on several datasets than the other methods tested in this paper.

scholarly journals Using Synthetic Data to Improve and Evaluate the Tracking Performance of Construction Workers on Site

2020 ◽  
Vol 10 (14) ◽  
pp. 4948
Author(s):  
Marcel Neuhausen ◽  
Patrick Herbers ◽  
Markus König
Keyword(s):  
Real World ◽  
Safety Management ◽  
Tracking System ◽  
Synthetic Data ◽  
Tracking Systems ◽  
Construction Sites ◽  
Virtual Construction ◽  
Percentage Points ◽  
Video Frames ◽  
Graphics Software

Vision-based tracking systems enable the optimization of the productivity and safety management on construction sites by monitoring the workers’ movements. However, training and evaluation of such a system requires a vast amount of data. Sufficient datasets rarely exist for this purpose. We investigate the use of synthetic data to overcome this issue. Using 3D computer graphics software, we model virtual construction site scenarios. These are rendered for the use as a synthetic dataset which augments a self-recorded real world dataset. Our approach is verified by means of a tracking system. For this, we train a YOLOv3 detector identifying pedestrian workers. Kalman filtering is applied to the detections to track them over consecutive video frames. First, the detector’s performance is examined when using synthetic data of various environmental conditions for training. Second, we compare the evaluation results of our tracking system on real world and synthetic scenarios. With an increase of about 7.5 percentage points in mean average precision, our findings show that a synthetic extension is beneficial for otherwise small datasets. The similarity of synthetic and real world results allow for the conclusion that 3D scenes are an alternative to evaluate vision-based tracking systems on hazardous scenes without exposing workers to risks.

scholarly journals A Sentinel-2 Dataset for Uganda

Data ◽  
2021 ◽  
Vol 6 (4) ◽  
pp. 35
Author(s):  
Jonas Ardö
Keyword(s):  
Environmental Monitoring ◽  
Input Data ◽  
Vegetation Indices ◽  
Earth Observation ◽  
Observation Data ◽  
Output Data ◽  
Radiometric Correction ◽  
Clear Sky ◽  
Earth Observation Data ◽  
Sentinel 2

Earth observation data provide useful information for the monitoring and management of vegetation- and land-related resources. The Framework for Operational Radiometric Correction for Environmental monitoring (FORCE) was used to download, process and composite Sentinel-2 data from 2018–2020 for Uganda. Over 16,500 Sentinel-2 data granules were downloaded and processed from top of the atmosphere reflectance to bottom of the atmosphere reflectance and higher-level products, totalling > 9 TB of input data. The output data include the number of clear sky observations per year, the best available pixel composite per year and vegetation indices (mean of EVI and NDVI) per quarter. The study intention was to provide analysis-ready data for all of Uganda from Sentinel-2 at 10 m spatial resolution, allowing users to bypass some basic processing and, hence, facilitate environmental monitoring.

scholarly journals 'Greenfields' and 'Brownfields': automotive industrial development in the UK and in Portugal

Finisterra ◽  
2012 ◽  
Vol 31 (62) ◽  
Author(s):  
Andrew Pike ◽  
Mário Vale
Keyword(s):  
Industrial Policy ◽  
Automobile Industry ◽  
Industrial Development ◽  
Development Strategies ◽  
North West ◽  
Demonstration Effect ◽  
North East ◽  
The North ◽  
The Uk ◽  
The Impact

The industrial policy in the UK and in Portugal, as in most EU countries, seeks to attract new investment capacity, to create jobs and to promote the impact of the so-called "demonstration efect" of "greenfield" development strategies pursued in the new plants of inward investors on existing or "brownfield" plants. This industrial policy focus is particularly evident in the automobile industry.This paper compares the industrial policy oriented towards the automobile industry in the UK and in Portugal. Two recent "greenfield" investments are analised: Nissan in the North-East region (UK) and Ford/VW in the Setúbal Peninsula (Portugal), as well as three "brownfield" plants: Ford Halewood and GM Vauxhall Ellesmere Port in the North-West region (UK) and Renault in Setúbal (Portugal). The first part starts with a discussion of industrial policy in the automobile sector, the role of "greenfield" development strategies and the "demonstration effect" on "brownfield" plants. Then, the limits of new inward investment are pointed out, basically their problems and restrictions. Afterwards, the structural barriers to the "demonstration effect" within "brownfield" plants are outlined and some possabilities for alternative "brownfield" development strategies are presented.

scholarly journals Posture similarity index: a method to compare hand postures in synergy space

PeerJ ◽  
2018 ◽  
Vol 6 ◽  
pp. e6078 ◽  
Author(s):  
Nayan Bhatt ◽  
Varadhan SKM
Keyword(s):  
Experimental Data ◽  
Tracking System ◽  
Synthetic Data ◽  
Similarity Index ◽  
Principal Component ◽  
Synthetic Dataset ◽  
Human Hand ◽  
Task Requirements ◽  
Kinematic Synergies ◽  
The Central Nervous System

Background The human hand can perform a range of manipulation tasks, from holding a pen to holding a hammer. The central nervous system (CNS) uses different strategies in different manipulation tasks based on task requirements. Attempts to compare postures of the hand have been made for use in robotics and animation industries. In this study, we developed an index called the posture similarity index to quantify the similarity between two human hand postures. Methods Twelve right-handed volunteers performed 70 postures, and lifted and held 30 objects (total of 100 different postures, each performed five times). A 16-sensor electromagnetic tracking system captured the kinematics of individual finger phalanges (segments). We modeled the hand as a 21-DoF system and computed the corresponding joint angles. We used principal component analysis to extract kinematic synergies from this 21-DoF data. We developed a posture similarity index (PSI), that represents the similarity between posture in the synergy (Principal component) space. First, we tested the performance of this index using a synthetic dataset. After confirming that it performs well with the synthetic dataset, we used it to analyze the experimental data. Further, we used PSI to identify postures that are “representative” in the sense that they have a greater overlap (in synergy space) with a large number of postures. Results Our results confirmed that PSI is a relatively accurate index of similarity in synergy space both with synthetic data and real experimental data. Also, more special postures than common postures were found among “representative” postures. Conclusion We developed an index for comparing posture similarity in synergy space and demonstrated its utility by using synthetic dataset and experimental dataset. Besides, we found that “special” postures are actually “special” in the sense that there are more of them in the “representative” postures as identified by our posture similarity index.

On the use of drones to detect and map marine macro-litter on the North Atlantic Portuguese beach-dune systems: the experiences of UAS4Litter project

2021 ◽  
Author(s):  
Umberto Andriolo ◽  
Gil Gonçalves ◽  
Filipa Bessa ◽  
Paula Sobral ◽  
Luis Pinto ◽  
...  
Keyword(s):  
North Atlantic ◽  
Marine Pollution ◽  
Dynamic Modelling ◽  
Coastal Dunes ◽  
Marine Debris ◽  
Machine Learning Techniques ◽  
Unmanned Aerial Systems ◽  
Marine Litter ◽  
Portuguese Coast ◽  
Dune Systems

<p>Unmanned Aerial Systems (UAS, aka drones) are being used to map marine macro-litter on the coast. Within the UAS4Litter project, the application of UAS has been applied on three sandy beach-dune systems on the wave-dominated North Atlantic Portuguese coast. Several technical solutions have been tested in terms of drone mapping performance, manual image screening and marine litter map analysis. The conceptualization and implementation of a multidisciplinary framework allowed to improve and making more efficient the mapping of marine litter items with UAS on coastal environment. </p><p>The location of major marine litter loads within the monitored areas were found associated to beach slope and water level dynamics on the beach profiles. Moreover, the abundance of marine pollution was related to the geographical location and level of urbanization of the study sites. The testing of machine learning techniques underlined that automated technique returned reliable abundance map of marine litter, while manual image screening was required for a detailed categorization of the items. </p><p>As marine litter pollution on coastal dunes has received limited scientific attention when compared with sandy shores, a novel non-intrusive UAS-based marine litter survey have been also applied to quantify the level of contamination on coastal dunes. The results showed the influence of the different dune plant communities in trapping distinct type of marine litter, and the role played by wind and overwash events in defining the items pathways through the dune blowouts. </p><p>The experiences on the Portuguese coast show that UAS allows an integrated approach for marine litter mapping, beach morphodynamic and nearshore hydrodynamic, setting the ground for marine litter dynamic modelling on the shore. Besides, UAS can give a new impulse to coastal dune litter monitoring, where the long residence time of marine debris threat the bio-ecological equilibrium of these ecosystems.</p>

Evaluation of the modern geoecological state of the fjords of the eastern Barents sea

2021 ◽  
pp. 899-943
Author(s):  
V.A. Shakhverdov ◽  
◽  
D.V. Ryabchuk ◽  
M.A. Spiridonov ◽  
V.A. Zhamoida ◽  
...  
Keyword(s):  
Bottom Sediments ◽  
Industrial Development ◽  
Barents Sea ◽  
Chemical Elements ◽  
Environmental Damage ◽  
North West ◽  
Geochemical Zoning ◽  
The North ◽  
The Barents Sea ◽  
Main Components

A brief analysis of the history of environmental geological study of the Barents Sea is given. It shows that at the beginning of industrial development the geological environment was characterized by a low level of disturbance and pollution. On example of the Kola Bay, an assessment of the current environmental geological conditions of the fjords in the eastern part of the Barents Sea is given. Seismic-acoustic studies confirm the predominantly tectonic origin of the bay and the hazardous spread of gravitational rocks movement within the coastal slopes. The background geochemical characteristics of recent bottom sediments are quantified. It is shown that geochemical zoning of the bottom of the bay is a consequence of both natural and anthropogenic processes. According to the content of Cu, Zn, As, Cd, Pb, Hg and hexane-soluble petroleum products (PP) in the bottom sediments, the characteristics of various areas were obtained. It is shown that the distribution of PP and several other pollutants in the main components of aquatic and coastal geosystems is a leading element of the environmental monitoring system, quantitative assessment of anthropogenic impact and accumulated environmental damage. Active economic activity within the southern leg of the Kola Bay, as well as the naval bases, significantly affects the distribution of chemical elements. The data concerning distribution of chemical elements forms in bottom sediments are given that suggest a high probability of secondary pollution of the bottom water when the physicochemical conditions of sedimentation processes change. A comparative analysis showed that bottom sediments of the Kola Bay are characterized by the highest concentration of chemical elements in the North-West Region of the Russian Federation.

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