bluuebunny/crossref_metadata_2025 huggingface dataset:
EXPERIÊNCIAS MOBILIZADAS EM UM CURSO DE FORMAÇÃO CONTINUADA ACERCA DO ENSINO E DA APRENDIZAGEM DE LÍNGUAS ADICIONAIS PARA CRIANÇAS
Authors: Dallagnol Riscieli , Seganfredo Santos Leandra Ines
Published: June 2021

Este artigo tem por objetivo socializar o processo de formação continuada vivenciado em um curso de pós-graduação Lato Sensu denominado Ensino e aprendizagem de línguas adicionais para crianças. O estudo filia-se a área da Linguística Aplicada, é de natureza qualitativa de cunho interpretativista (BAUER e GASKELL, 2002; BORTONI-RICARDO, 2008), e tem como bases teóricas Scaramucci, Costa e Rocha (2008), Santos (2005; 2009; 2011), Schlatter e Garcez (2012), Leffa (2008; 2012), dentre outros. Para a coleta de dados foram utilizadas entrevistas semiestruturadas e observação participante das aulas com registro em diário de campo. Os sujeitos colaboradores são docentes e discentes do curso. Identificamos que os maiores desafios estipulados pelos sujeitos colaboradores é exercitar a reflexão entre a relação teoria X prática, além disso, exercitar a prática de fazer as leituras necessárias do aporte teórico das disciplinas. Os resultados mostram que as contribuições da formação continuada, conforme os dizeres dos sujeitos, se produz no sentido de ajudar a refletir, reconstruir novos saberes, compreender teorias sobre o ensino-aprendizagem de língua adicional para crianças e subsidia entender as especificidades dessa área.
Produzindo seres vivos: uma biofábrica brasileira
Authors: Turco Claudia Santos , Paiva Eduardo Nazareth
Published: June 2021

Seres humanos se utilizam dos demais seres vivos como objetos, sejam estes animais, plantas ou micro-organismos. Nas últimas décadas, as características desta relação têm se alterado, o que fez surgir os conceitos de bio-objeto e de biofábrica. Amparado nestes conceitos, o presente trabalho tem como objetivo apresentar o processo produtivo de um bio-objeto no Rio de Janeiro, Brasil: os mosquitos Aedes aegypti com a bactéria Wolbachia. A análise foi feita a partir de dados bibliográficos e de uma visita de campo às instalações da biofábrica em estudo.
Technical note: Sea salt interference with black carbon quantification in snow samples using the single particle soot photometer
Authors: Zanatta Marco , Herber Andreas , Jurányi Zsófia , Eppers Oliver , Schneider Johannes , Schwarz Joshua P.
Published: June 2021

Abstract. After aerosol deposition from the atmosphere, black carbon (BC) takes part
in the snow albedo feedback contributing to the modification of the Arctic
radiative budget. With the initial goal of quantifying the concentration of
BC in the Arctic snow and subsequent climatic impacts, snow samples were
collected during the research vessel (R/V) Polarstern expedition of PASCAL (Physical Feedbacks of Arctic Boundary Layer, Sea Ice,
Cloud and Aerosol; Polarstern cruise 106) in the sea-ice-covered Fram Strait in early summer 2017. The refractory BC (rBC) content was then measured in the laboratory of the Alfred Wegener Institute with the single particle soot photometer (SP2). Based on the strong observational correlations between both rBC concentration and rBC diameter with snow salinity, we hypothesize
a salt-induced matrix effect interfering with the SP2 analysis. This paper
evaluates the impact of sea salt, based on the measurement of electrical
conductivity (κ) in water samples, on rBC measurements made with a
SP2 nebulizer technique. Under realistic salinity conditions, laboratory
experiments indicated a dramatic six-fold reduction in observed rBC
concentration with increasing salinity. In the salinity conditions tested in the present work (salt concentration below 0.4 g L−1) the impact of salt on the nebulization of water droplets might be negligible. However, the SP2 mass detection efficiency systematically decreased with increasing
salinity, with the smaller rBC particles being preferentially undetected.
The high concentration of suspended salt particles and the formation of
thick salt coatings on rBC cores caused problems in the SP2 analog-to-digital conversion of the signal and incandescence quenching,
respectively. Changes to the signal acquisition parameters and the laser power of the SP2 improved the mass detection efficiency, which, nonetheless, stayed below unity. The present work provides evidence that a high concentration of sea salt undermines the quantification of rBC in snow performed with the SP2 nebulizer system described here. This interference has not been previously reported and might affect the future such analysis of rBC particles in snow collected, especially over sea ice or coastal regions strongly affected by sea salt deposition.
The fate of upwelled nitrate off Peru shaped by submesoscale filaments and fronts
Authors: Hauschildt Jaard , Thomsen Soeren , Echevin Vincent , Oschlies Andreas , José Yonss Saranga , Krahmann Gerd , Bristow Laura A. , Lavik Gaute
Published: June 2021

Abstract. Filaments and fronts play a crucial role for a net offshore and downward nutrient transport in Eastern Boundary Upwelling Systems (EBUSs) and thereby reduce regional primary production. Most studies on this topic are based on either observations or model simulations, but only seldom are both approaches are combined quantitatively to assess the importance of filaments for primary production and nutrient transport.
Here we combine targeted interdisciplinary shipboard observations of a cold filament off Peru with submesoscale-permitting (1/45∘) coupled physical (Coastal and Regional Ocean Community model, CROCO) and biogeochemical (Pelagic Interaction Scheme for Carbon and Ecosystem Studies, PISCES) model simulations to (i) evaluate the model simulations in detail, including the timescales of biogeochemical modification of the newly upwelled water, and (ii) quantify the net effect of submesoscale fronts and filaments on primary production in the Peruvian upwelling system. The observed filament contains relatively cold, fresh, and nutrient-rich waters originating in the coastal upwelling. Enhanced nitrate concentrations and offshore velocities of up to
0.5 m s−1 within the filament suggest an offshore transport of nutrients. Surface chlorophyll in the filament is a factor of 4 lower than at the upwelling front, while surface primary production is a factor of 2 higher. The simulation exhibits filaments that are similar in horizontal and vertical scale compared to the observed filament. Nitrate concentrations and primary production within filaments in the model are comparable to observations as well, justifying further analysis of nitrate uptake and subduction using the model. Virtual Lagrangian floats were released in the subsurface waters along the shelf and biogeochemical variables tracked along the trajectories of floats upwelled near the coast. In the submesoscale-permitting (1/45∘) simulation, 43 % of upwelled floats and 40 % of upwelled nitrate are subducted within 20 d after upwelling, which corresponds to an increase in nitrate subduction compared to a mesoscale-resolving (1/9∘) simulation by 14 %. Taking model biases into account, we give a best estimate for subduction of upwelled nitrate off Peru between 30 %– 40 %. Our results suggest that submesoscale processes further reduce primary production by amplifying the downward and offshore export of nutrients found in previous mesoscale studies, which are thus likely to underestimate the reduction in primary production due to eddy fluxes. Moreover, this downward and offshore transport could also enhance the export of fresh organic matter below the euphotic zone and thereby potentially stimulate microbial activity in regions of the upper offshore oxygen minimum zone.
Revised alkenone δ13C based CO2 estimates during the Plio-Pleistocene
Authors: Seki Osamu , Bendle James
Published: June 2021

Abstract. Here we revisit reported alkenone δ13C (δ13CC37) based CO2 records during the Plio-Pleistocene and apply a refined approach to better constrain the dynamic range of CO2 during the time. Specifically, we consider ways to correct for regional differences in physical oceanographic factors. As a result of our relatively simple approach we find that offsets of ~150 ppm between reported δ13CC37 CO2 records from different sites can be significantly reduced. This confirms that better constraints on environmental variables, including physical oceanographic controls on depth and season of production are key aspects for improving δ13CC37 based CO2 estimates. The revised δ13CC37 CO2 datasets suggest that Plio-Pleistocene CO2 levels are 180–350 ppm, which is consistent with the most of reported CO2 reconstructions, though their upper end of Pliocene CO2 levels are lower than that of some CO2 estimates.
Machine learning deciphers CO2 sequestration and subsurface flowpaths from stream chemistry
Authors: Shaughnessy Andrew R. , Gu Xin , Wen Tao , Brantley Susan L.
Published: June 2021

Abstract. Endmember mixing analysis (EMMA) is often used by hydrogeochemists
to interpret the sources of stream solutes, but variations in stream
concentrations and discharges remain difficult to explain. We discovered
that machine learning can be used to highlight patterns in stream chemistry
that reveal information about sources of solutes and subsurface groundwater
flowpaths. The investigation has implications, in turn, for the balance of
CO2 in the atmosphere. For example, CO2-driven weathering of
silicate minerals removes carbon from the atmosphere over ∼106-year timescales. Weathering of another common mineral, pyrite, releases sulfuric
acid that in turn causes dissolution of carbonates. In that process,
however, CO2 is released instead of sequestered from the atmosphere. Thus, understanding long-term global CO2 sequestration by weathering
requires quantification of CO2- versus H2SO4-driven
reactions. Most researchers estimate such weathering fluxes from stream
chemistry, but interpreting the reactant minerals and acids dissolved in streams has been fraught with difficulty. We apply a machine-learning
technique to EMMA in three watersheds to determine the extent of mineral
dissolution by each acid, without pre-defining the endmembers. The results
show that the watersheds continuously or intermittently sequester CO2, but the extent of CO2 drawdown is diminished in areas heavily affected
by acid rain. Prior to applying the new algorithm, CO2 drawdown was
overestimated. The new technique, which elucidates the importance of
different subsurface flowpaths and long-timescale changes in the watersheds,
should have utility as a new EMMA for investigating water resources
worldwide.
DEEP LEARNING FOR AUTOMATIC BUILDING DAMAGE ASSESSMENT: APPLICATION IN POST-DISASTER SCENARIOS USING UAV DATA
Authors: Calantropio A. , Chiabrando F. , Codastefano M. , Bourke E.
Published: June 2021

Abstract. During the last few years, the technical and scientific advances in the Geomatics research field have led to the validation of new mapping and surveying strategies, without neglecting already consolidated practices. The use of remote sensing data for damage assessment in post-disaster scenarios underlined, in several contexts and situations, the importance of the Geomatics applied techniques for disaster management operations, and nowadays their reliability and suitability in environmental emergencies is globally recognized. In this paper, the authors present their experiences in the framework of the 2016 earthquake in Central Italy and the 2019 Cyclone Idai in Mozambique. Thanks to the use of image-based survey techniques as the main acquisition methods (UAV photogrammetry), damage assessment analysis has been carried out to assess and map the damages that occurred in Pescara del Tronto village, using DEEP (Digital Engine for Emergency Photo-analysis) a deep learning tool for automatic building footprint segmentation and building damage classification, functional to the rapid production of cartography to be used in emergency response operations. The performed analyses have been presented, and the strengths and weaknesses of the employed methods and techniques have been outlined. In conclusion and based on the authors' experience, some operational suggestions and best practices are provided and future research perspectives within the same research topic are introduced.
DRONE-BASED CONTAINER CRANE INSPECTION: CONCEPT, CHALLENGES AND PRELIMINARY RESULTS
Authors: Maboudi M. , Alamouri A. , De Arriba López V. , Bajauri M. S. , Berger C. , Gerke M.
Published: June 2021

Abstract. Container crane inspection is a very important task to maintain their uninterrupted operation. Nevertheless, this is a costly and time-consuming activity if performed manually. Recently, image-based detection of surface damages or changes using drones has gained increasing interest in industry; especially when objects of interest have a complex structure like container cranes. One main aim of this paper is a single-epoch image analysis which will also serve later for multi-epoch processing. It provides reliable information about current defects that may lead to big damages if not inspected by experts. Naïve Bayes classifier is employed to classify the images in different classes of which critical defects and especially rust is important. The preliminary results show that the precision on the target class reached about 99%. However, 87% percent recall in this class is not enough and it should be improved for this application.Having a large dataset requires an efficient data management system to provide users and decision makers with the information needed. In addition, in order to foster full automation, the aforementioned image analysis component should have a direct connection to the database and thus is able to query image and semantic information. We therefore introduce the second aim of our research, that is a concept for database design. Here, not only the raw data and the final results are integrated but also the intermediate results. At the same time, the database concept is connected to an integrated client interface that allows retrieving data of interest in a virtual globe.
CONVOLUTIONAL NEURAL NETWORKS FOR DETECTING BRIDGE CROSSING EVENTS WITH GROUND-BASED INTERFEROMETRIC RADAR DATA
Authors: Arnold M. , Hoyer M. , Keller S.
Published: June 2021

Abstract. This study focuses on detecting vehicle crossings (events) with ground-based interferometric radar (GBR) time series data recorded at bridges in the course of critical infrastructure monitoring. To address the challenging event detection and time series classification task, we rely on a deep learning (DL) architecture. The GBR-displacement data originates from real-world measurements at two German bridges under normal traffic conditions. As preprocessing, we only apply a low-pass filter. We develop and evaluate a one-dimensional convolutional neural network (CNN) to achieve a solely data-driven event detection. As a baseline machine learning approach, we use a Random Forest (RF) with a selected feature-based input. Both models’ performance is evaluated on two datasets by focusing on identifying events and pure bridge oscillations. Generally, the event classification results are promising, and the CNN outperforms the RF with an overall accuracy of 94.7% on the test subset. By relying on an entirely unknown second dataset, we focus on the models’ performances regarding the distinction between events and decays. On this dataset, the CNN meets this challenge successfully, while the feature-based RF classifies the majority of non-event decays as events. To sum up, the presented results reveal the potential of a data-driven DL approach concerning the detection of bridge crossing events in GBR-based displacement time series data. Based on such an event detection, a prospective assessment of bridge conditions seems feasible as an extension to previous structural health monitoring approaches.
DEEPLIO: DEEP LIDAR INERTIAL SENSOR FUSION FOR ODOMETRY ESTIMATION
Authors: Javanmard-Gh. A. , Iwaszczuk D. , Roth S.
Published: June 2021

Abstract. Having a good estimate of the position and orientation of a mobile agent is essential for many application domains such as robotics, autonomous driving, and virtual and augmented reality. In particular, when using LiDAR and IMU sensors as the inputs, most existing methods still use classical filter-based fusion methods to achieve this task. In this work, we propose DeepLIO, a modular, end-to-end learning-based fusion framework for odometry estimation using LiDAR and IMU sensors. For this task, our network learns an appropriate fusion function by considering different modalities of its input latent feature vectors. We also formulate a loss function, where we combine both global and local pose information over an input sequence to improve the accuracy of the network predictions. Furthermore, we design three sub-networks with different modules and architectures derived from DeepLIO to analyze the effect of each sensory input on the task of odometry estimation. Experiments on the benchmark dataset demonstrate that DeepLIO outperforms existing learning-based and model-based methods regarding orientation estimation and shows a marginal position accuracy difference.
TERRESTRIAL MOBILE MAPPING BASED ON A MICROWAVE RADAR SENSOR. APPLICATION TO THE LOCALIZATION OF MOBILE ROBOTS
Authors: Rouveure R. , Faure P. , Monod M.-O.
Published: June 2021

Abstract. Mobile robotics applications in outdoor environments now use intensively Global Positioning System (GPS). For localization or navigation operations, GPS has become an essential tool due to its ease of use, its precision, and its worldwide accessibility. The increase of autonomy level in mobile robotics implies a robust centimeter-level positioning, but the presence of natural (trees, mountains) or man-made obstacles (buildings) can degrade or prevent GPS signals reception. We present in this paper a solution for robots localization based on PELICAN microwave radar. PELICAN radar provides each second a panoramic image of the surrounding environment. These images are concatenated through a Simultaneous Localization And Mapping (SLAM) algorithm in order to build global maps of the traveled environments. The proposed solution computes the position and orientation of the robot through a real-time 3D matching between the current radar image and a pre-existing radar map constructed during an exploratory phase.
ROADSIDE TREE EXTRACTION AND DIAMETER ESTIMATION WITH MMS LIDAR BY USING POINT-CLOUD IMAGE
Authors: Takahashi G. , Masuda H.
Published: June 2021

Abstract. Efficient management of roadside trees for local governments is important. Mobile Mapping System (MMS) equipped with a high-density LiDAR scanner has the possibility to be applied to estimate DBH of roadside trees using point clouds. In this study, we propose a method for detecting roadside trees and estimating their DBHs automatically from MMS point clouds. In our method, point clouds captured using the MMS are mapped on a 2D image plane, and they are converted into a wireframe model by connecting adjacent points. Then, geometric features are calculated for each point in the wireframe model. Tree points are detected using a machine learning technique. The DBH of each tree is calculated using vertically aligned circles extracted from the wireframe model. Our method allows robustly calculating the DBH even if there is a hump at breast height. We evaluated our method using actual MMS data measured in an urban area in Tokyo. Our method achieved a high extraction performance of 100 percent of precision and 95.1 percent of recall for 102 roadside trees. The average accuracy of the DBH was 2.0 cm. These results indicate that our method is useful for the efficient management of roadside trees.
BIDIRECTIONAL MULTI-SCALE ATTENTION NETWORKS FOR SEMANTIC SEGMENTATION OF OBLIQUE UAV IMAGERY
Authors: Lyu Y. , Vosselman G. , Xia G.-S. , Yang M. Y.
Published: June 2021

Abstract. Semantic segmentation for aerial platforms has been one of the fundamental scene understanding task for the earth observation. Most of the semantic segmentation research focused on scenes captured in nadir view, in which objects have relatively smaller scale variation compared with scenes captured in oblique view. The huge scale variation of objects in oblique images limits the performance of deep neural networks (DNN) that process images in a single scale fashion. In order to tackle the scale variation issue, in this paper, we propose the novel bidirectional multi-scale attention networks, which fuse features from multiple scales bidirectionally for more adaptive and effective feature extraction. The experiments are conducted on the UAVid2020 dataset and have shown the effectiveness of our method. Our model achieved the state-of-the-art (SOTA) result with a mean intersection over union (mIoU) score of 70.80%.
WHICH 3D DATA REPRESENTATION DOES THE CROWD LIKE BEST? CROWD-BASED ACTIVE LEARNING FOR COUPLED SEMANTIC SEGMENTATION OF POINT CLOUDS AND TEXTURED MESHES
Authors: Kölle M. , Laupheimer D. , Walter V. , Haala N. , Soergel U.
Published: June 2021

Abstract. Semantic interpretation of multi-modal datasets is of great importance in many domains of geospatial data analysis. However, when training models for automated semantic segmentation, labeled training data is required and in case of multi-modality for each representation form of the scene. To completely avoid the time-consuming and cost-intensive involvement of an expert in the annotation procedure, we propose an Active Learning (AL) pipeline where a Random Forest classifier selects a subset of points sufficient for training and where necessary labels are received from the crowd. In this AL loop, we aim on coupled semantic segmentation of an Airborne Laser Scanning (ALS) point cloud and the corresponding 3D textured mesh generated from LiDAR data and imagery in a hybrid manner. Within this work we pursue two main objectives: i) We evaluate the performance of the AL pipeline applied to an ultra-high resolution ALS point cloud and a derived textured mesh (both benchmark datasets are available at https://ifpwww.ifp.uni-stuttgart.de/benchmark/hessigheim/default.aspx). ii) We investigate the capabilities of the crowd regarding interpretation of 3D geodata and observed that the crowd performs about 3 percentage points better when labeling meshes compared to point clouds. We additionally demonstrate that labels received solely by the crowd can power a machine learning system only differing in Overall Accuracy by less than 2 percentage points for the point cloud and less than 3 percentage points for the mesh, compared to using the completely labeled training pool. For deriving this sparse training set, we ask the crowd to label 0.25 % of available training points, resulting in costs of 190 $.
MULTI-SENSOR APPROACH TO LEAF AREA INDEX ESTIMATION USING STATISTICAL MACHINE LEARNING MODELS: A CASE ON MANGROVE FORESTS
Authors: Martinez K. P. , Burgos D. F. M. , Blanco A. C. , Salmo III S. G.
Published: June 2021

Abstract. Leaf Area Index (LAI) is a quantity that characterizes canopy foliage content. As leaf surfaces are the primary sites of energy, mass exchange, and fundamental production of terrestrial ecosystem, many important processes are directly proportional to LAI. With this, LAI can be considered as an important parameter of plant growth. Multispectral optical images have been widely utilized for mangrove-related studies, such as LAI estimation. In Sentinel-2, for example, LAI can be estimated using a biophysical processor in SNAP or using various machine learning algorithms. However, multispectral optical images have disadvantages due to its weather-dependence and limited canopy penetration. In this study, a multi-sensor approach was implemented by using free multi-spectral optical images (Sentinel-2 ) and synthetic aperture radar (SAR) images (Sentinel-1) to perform Leaf Area Index (LAI) estimation. The use of SAR images can compensate for the above-mentioned disadvantages and it then can pave the way for regular mapping and assessment of LAI, despite any weather conditions and cloud cover. In this study, generation of LAI models that explores linear, non-linear and decision trees modelling algorithms to incorporate Sentinel-1 derivatives and Sentinel-2 LAI were executed. The Random Forest model have exhibited the most robust model having the lowest RMSE of 0.2845. This result poses a concrete relationship of a biophysical entity derived from optical parameters to RADAR derivatives to which opens the opportunity of integrating both systems to compensate each disadvantages and produce a more efficient quantification of LAI.
WATER QUALITY RETRIEVAL AND ALGAL BLOOM DETECTION USING HIGH-RESOLUTION CUBESAT IMAGERY
Authors: Niroumand-Jadidi M. , Bovolo F.
Published: June 2021

Abstract. Recent advancements in developing small satellites known as CubeSats provide an increasingly viable means of characterizing the dynamics of inland and nearshore waters with an unprecedented combination of high revisits (< 1 day) with a high spatial resolution (meter-scale). Estimation of water quality parameters can benefit from the very high spatiotemporal resolution of CubeSat imagery for monitoring subtle variations and identification of hazardous events like algal blooms. In this study, we present the first study on retrieving lake chlorophyll-a (Chl-a) concentration and detecting algal blooms using imagery acquired by the PlanetScope constellation which is currently the most prominent source of CubeSat data. Moreover, the concentration of total suspended matter (TSM) is retrieved that is an indicator of turbidity. The retrievals are based upon inverting the radiative transfer model. The low spectral resolution (four bands) of PlanetScope imagery poses challenges for such a physics-based inversion due to spectral ambiguities in optically-complex waters like inland waters. To deal with this issue, the number of variable parameters is minimized through inverse modeling. Given the significance of having high-quality water-leaving reflectance for physics-based models, a variable parameter (gdd) is considered to compensate for the atmospheric and sun-glint artifacts. The results compared to the in-situ data indicate high potentials of PlanetScope imagery in retrieving water quality parameters and detection of algal blooms in our case study (Lake Trasimeno, Italy).
EVALUATING THE IMPACT OF LASRC AND SEN2COR ATMOSPHERIC CORRECTION ALGORITHMS ON LANDSAT-8/OLI AND SENTINEL-2/MSI DATA OVER AERONET STATIONS IN BRAZILIAN TERRITORY
Authors: Marujo R. F. B. , Fronza J. G. , Soares A. R. , Queiroz G. R. , Ferreira K. R.
Published: June 2021

Abstract. Accurate and consistent Surface Reflectance estimation from optical remote sensor observations is directly dependant on the used atmospheric correction processor and the differences caused by it may have implications on further processes, e.g. classification. Brazil is a continental scale country with different biomes. Recently, new initiatives, as the Brazil Data Cube Project, are emerging and using free and open data policy data, more specifically medium spatial resolution sensor images, to create image data cubes and classify the Brazilian territory crops. For this reason, the purpose of this study is to verify, on Landsat-8 and Sentinel-2 images for the Brazilian territory, the suitability of the atmospheric correction processors maintained by their image providers, LaSRC from USGS and Sen2cor from ESA, respectively. To achieve this, we tested the surface reflectance products from Landsat-8 processed through LaSRC and Sentinel-2 processed through LaSRC and Sen2cor comparing to a reference dataset computed by ARCSI and AERONET. The obtained results point that Landsat-8/OLI images atmospherically corrected using the LaSRC corrector are consistent to the surface reflectance reference and other atmospheric correction processors studies, while for Sentinel-2/MSI images, Sen2cor performed best. Although corrections over Sentinel-2/MSI data weren’t as consistent as in Landsat-8/OLI corrections, in comparison to the surface reflectance references, most of the spectral bands achieved acceptable APU results.
TOWARDS DETECTING FLOATING OBJECTS ON A GLOBAL SCALE WITH LEARNED SPATIAL FEATURES USING SENTINEL 2
Authors: Mifdal J. , Longépé N. , Rußwurm M.
Published: June 2021

Abstract. Marine litter is a growing problem that has been attracting attention and raising concerns over the last years. Significant quantities of plastic can be found in the oceans due to the unfiltered discharge of waste into rivers, poor waste management, or lost fishing nets. The floating elements drift on the surface of water bodies and can be aggregated by processes, such as river plumes, windrows, oceanic fronts, or currents. In this paper, we focus on detecting big patches of floating objects that can contain plastic as well as other materials with optical Sentinel 2 data. In contrast to previous work that focuses on pixel-wise spectral responses of some bands, we employ a deep learning predictor that learns the spatial characteristics of floating objects. Along with this work, we provide a hand-labeled Sentinel 2 dataset of floating objects on the sea surface and other water bodies such as lakes together with pre-trained deep learning models. Our experiments demonstrate that harnessing the spatial patterns learned with a CNN is advantageous over pixel-wise classifications that use hand-crafted features. We further provide an analysis of the categories of floating objects that we captured while labeling the dataset and analyze the feature importance for the CNN predictions. Finally, we outline the limitations of trained CNN on several systematic failure cases that we would like to address in future work by increasing the diversity in the dataset and tackling the domain shift between regions and satellite acquisitions. The dataset introduced in this work is the first to provide public large-scale data for floating litter detection and we hope it will give more insights into developing techniques for floating litter detection and classification. Source code and data are available at https://github.com/ESA-PhiLab/floatingobjects.
JUNGLE-NET: USING EXPLAINABLE MACHINE LEARNING TO GAIN NEW INSIGHTS INTO THE APPEARANCE OF WILDERNESS IN SATELLITE IMAGERY
Authors: Stomberg T. , Weber I. , Schmitt M. , Roscher R.
Published: June 2021

Abstract. Explainable machine learning has recently gained attention due to its contribution to understanding how a model works and why certain decisions are made. A so far less targeted goal, especially in remote sensing, is the derivation of new knowledge and scientific insights from observational data. In our paper, we propose an explainable machine learning approach to address the challenge that certain land cover classes such as wilderness are not well-defined in satellite imagery and can only be used with vague labels for mapping. Our approach consists of a combined U-Net and ResNet-18 that can perform scene classification while providing at the same time interpretable information with which we can derive new insights about classes. We show that our methodology allows us to deepen our understanding of what makes nature wild by automatically identifying simple concepts such as wasteland that semantically describes wilderness. It further quantifies a class’s sensitivity with respect to a concept and uses it as an indicator for how well a concept describes the class.
DETECTION OF METHANE PLUMES IN HYPERSPECTRAL IMAGES FROM SENTINEL-5P BY COUPLING ANOMALY DETECTION AND PATTERN RECOGNITION
Authors: Ouerghi E. , Ehret T. , de Franchis C. , Facciolo G. , Lauvaux T. , Meinhardt E. , Morel J.-M.
Published: June 2021

Abstract. Reducing methane emissions is essential to tackle climate change. Here, we address the problem of detecting large methane leaks using hyperspectral data from the Sentinel-5P satellite. For that we exploit the fine spectral sampling of Sentinel-5P data to detect methane absorption features visible in the shortwave infrared wavelength range (SWIR). Our method involves three separate steps: i) background subtraction, ii) detection of local maxima in the negative logarithmic spectrum of each pixel and iii) anomaly detection in the background-free image. In the first step, we remove the impact of the albedo using albedo maps and the impact of the atmosphere by using a principal component analysis (PCA) over a time series of past observations. In the second step, we count for each pixel the number of local maxima that correspond to a subset of local maxima in the methane absorption spectrum. This counting method allows us to set up a statistical a contrario test that controls the false alarm rate of our detections. In the last step we use an anomaly detector to isolate potential methane plumes and we intersect those potential plumes with what have been detected in the second step. This process strongly reduces the number of false alarms. We validate our method by comparing the detected plumes against a dataset of plumes manually annotated on the Sentinel-5P L2 methane product.
COMBINING TERRESTRIAL SCANNED DATASETS WITH UAV POINT CLOUDS FOR MINING OPERATIONS
Authors: Fotheringham M. , Paudyal D. R.
Published: June 2021

Abstract. Surveyors of open cut mining operations employ multiple data acquisition techniques such as the use of Unmanned Aerial Vehicles (UAV), Terrestrial Laser Scanning (TLS) and GNSS positioning for creating 3D surface models. Surveyors, mine planners and geologists are increasingly combining point cloud datasets to achieve more detailed surface models for the use of material reconciliation and volume calculations. Terrestrial Laser Scanning and UAV photogrammetry have enabled large, accurate and time effective data collection and increased computing capacity enables geospatial professionals to create 3D virtual surfaces, through merging UAV point clouds and TLS data combing with GNSS positioning. This research paper investigates the effects of combining data sets for creating 3D surface models from independent spatial data collection methods such as UAV, TLS and GNSS and assess their accuracy for the purpose of volume calculations in mining operation. 3D surface models provide important information for mining operations, planning of resources, material volumes calculation and financial calculations. A case study of two rehabilitation mine sites in Northern Victoria, Australia was selected for this study. Field data were collected using Terrestrial Laser Scanner and UAV. After each dataset was processed and filtered, the data were merged to create surface models. The accuracy of the combined model was assessed comparing height (Z) values using a fishnet point grid of the surfaces. Volumes between surfaces were calculated, and a cost applied to the results based on the current bulk cubic meter (BCM) haulage rates. The outputs from this study will provide scientific contributions to civil and mining industries where the computation of stockpile values is required.
JOINT ESTIMATION OF ROAD ROUGHNESS FROM CROWD-SOURCED BICYCLE ACCELERATION MEASUREMENTS
Authors: Wage O. , Sester M.
Published: June 2021

Abstract. In contrast to cars, route choices for cycling are barely influenced by the respective traffic situation, but to a large extent by the routes’ comfort. Especially in urban settings with several alternatives, segments with many or long stops at traffic lights and badly maintained roads are avoided due to a low comfort and cyclists vary from the shortest route. This fact is only indirectly considered in common navigation applications.This work aims to integrate surface roughness measurements collected from diverse bicycles to a joint scale via a least-squares adjustment. Data was collected using smartphones, which were mounted to bike hand bars and measured positions and vertical accelerations on user’s trips. As this way sensed roughness also depends on the bike setting and type, the resulting values would be different for different users. Thus, this paper presents a novel approach to harmonize observations from differing sensitive setups. The basic concept idea of bundle block adjustment is adapted to calibrate a basic scale model and in parallel adjust the observations of surface roughness to a common scale.This way a crowd-sourced roughness map can be generated. Such a map can be used to enrich bike focused routing services and thus encourage cycling in daily live. In addition, it can also be used to derive hints for infrastructure servicing.
A TWO-LEVEL APPROACH FOR THE CROWD-BASED COLLECTION OF VEHICLES FROM 3D POINT CLOUDS
Authors: Walter V. , Kölle M. , Collmar D. , Zhang Y.
Published: June 2021

Abstract. In this article, we present a two-level approach for the crowd-based collection of vehicles from 3D point clouds. In the first level, the crowdworkers are asked to identify the coarse positions of vehicles in 2D rasterized shadings that were derived from the 3D point cloud. In order to increase the quality of the results, we utilize the wisdom of the crowd principle which says that averaging multiple estimates of a group of individuals provides an outcome that is often better than most of the underlying estimates or even better than the best estimate. For this, each crowd job is duplicated 10 times and the multiple results are integrated with a DBSCAN cluster algorithm. In the second level, we use the integrated results as pre-information for extracting small subsets of the 3D point cloud that are then presented to crowdworkers for approximating the included vehicle by means of a Minimum Bounding Box (MBB). Again, the crowd jobs are duplicated 10 times and an average bounding box is calculated from the individual bounding boxes. We will discuss the quality of the results of both steps and show that the wisdom of the crowd significantly improves the completeness as well as the geometric quality. With a tenfold acquisition, we have achieve a completeness of 93.3 percent and a geometric deviation of less than 1 m for 95 percent of the collected vehicles.
KNOWLEDGE AND SKILLS RELATED TO ACTIVE OPTICAL SENSORS IN THE BODY OF KNOWLEDGE FOR EARTH OBSERVATION AND GEOINFORMATION (EO4GEO BOK)
Authors: Dubois C. , Jutzi B. , Olijslagers M. , Pathe C. , Schmullius C. , Stelmaszczuk-Górska M. A. , Vandenbroucke D. , Weinmann M.
Published: June 2021

Abstract. The field of Earth Observation (EO) and Geoinformation (GI) is gaining more and more importance due to the increasing number of data and data processing algorithms to respond even more accurately to a variety of challenges in many application areas. In order to follow recent activities and align the exponential evolution of datasets and recent processing trends with market and academic training requirements, the EO*GI sector needs an updated and new definition of knowledge and skills. To this goal, a specific body of knowledge for Earth Observation and Geoinformation (EO4GEO BoK) is currently being implemented with the aim of providing interconnected concepts and job-oriented skills. One novelty of the BoK is to include topics related to Earth Observation, in particular to introduce the different sensors used in this field. Active optical sensors are at the crossroad between Earth Observation and close-range photogrammetry and have not been described in any other existing BoK. This paper introduces therefore the part of the EO4GEO BoK that is dedicated to active optical sensors. Such systems are used in various job-oriented applications such as archeology, mobile mapping, indoor or outdoor, or for surveying purposes, just to name a few. The structure of this part of the BoK is explained and specific descriptions and relationships between the identified concepts are given. Finally, the skills acquired by completing these BoK concepts are presented and discussed in terms of how they can be used in a professional context or in the definition of job-oriented learning paths.
DIGITAL PRESERVATION OF ANCIENT MAYA CAVE ARCHITECTURE: RECENT FIELD EFFORTS IN QUINTANA ROO, MEXICO
Authors: Rissolo D. , Lo E. , Hess M. R. , Meyer D. E. , Amador F. E.
Published: June 2021

Abstract. The presence of ancient Maya shrines in caves serves as unequivocal evidence for the ritual appropriation of these subterranean spaces and their significance with respect to Maya religious practice. Detailed study of the miniature masonry temples and altar features in the caves of Quintana Roo, Mexico reveals a strong stylistic and likely functional correspondence between these structures and their terrestrial counterparts at Postclassic sites. The Proyecto Arquitectura Subterranea de Quintana Roo (coordinated by the Center of Interdisciplinary Science for Art, Architecture, and Archaeology, or CISA3, at the University of California, San Diego and in collaboration with the Instituto Nacional de Antropologia e Historia in Mexico) is conducting a survey and program of digital documentation of both the pristine and impacted cave shrines of the region. Once an area is developed and populated, and access is opened to caves containing ancient architectural features, they are soon vandalized – often resulting in the complete obliteration of these rare miniature buildings and their diagnostic architectural elements. This emergent situation necessitates the use of rapid reality-capture tools; however, the physical challenges of working in caves requires researchers of adapt increasingly common architectural documentation methodologies to more adverse field conditions.
Towards a compound-event-oriented climate model evaluation: a decomposition of the underlying biases in multivariate fire and heat stress hazards
Authors: Villalobos-Herrera Roberto , Bevacqua Emanuele , Ribeiro Andreia F. S. , Auld Graeme , Crocetti Laura , Mircheva Bilyana , Ha Minh , Zscheischler Jakob , De Michele Carlo
Published: June 2021

Abstract. Climate models' outputs are affected by biases that need to be detected and adjusted to model climate impacts. Many climate hazards and climate-related impacts are associated with the interaction between multiple drivers, i.e. by compound events. So far climate model biases are typically assessed based on the hazard of interest, and it is unclear how much a potential bias in the dependence of the hazard drivers contributes to the overall bias and how the biases in the drivers interact. Here, based on copula theory, we develop a multivariate bias-assessment framework, which allows for disentangling the biases in hazard indicators in terms of the underlying univariate drivers and their statistical dependence. Based on this framework, we dissect biases in fire and heat stress hazards in a suite of global climate models by considering two simplified hazard
indicators: the wet-bulb globe temperature (WBGT) and the Chandler burning index (CBI). Both indices solely rely on temperature and relative humidity. The spatial pattern of the hazard indicators is well represented by climate models. However, substantial biases exist in the representation of extreme conditions, especially in the CBI (spatial average of absolute bias: 21 ∘C) due to the biases driven by relative humidity (20 ∘C). Biases in WBGT (1.1 ∘C) are small compared to the biases driven by temperature (1.9 ∘C) and relative humidity (1.4 ∘C), as the two biases compensate for each other. In many regions, also biases related to the statistical dependence (0.85 ∘C) are important for WBGT, which indicates that well-designed physically based multivariate bias adjustment procedures should be considered for hazards and impacts that depend on multiple drivers. The proposed compound-event-oriented evaluation of climate model biases is easily applicable to other hazard types. Furthermore, it can contribute to improved present and future risk assessments through increasing our understanding of the biases' sources in the simulation of climate impacts.
Van İli Merkez İlçede Küçükbaş Hayvancılık Faaliyetleri ve Genel Sorunlar: I. İşletmelerin Yapısal Özellikleri
Authors: YILDIZ Aşkın , AYGÜN Turgut
Published: June 2021

Bu araştırma, Van ili merkez ilçede küçükbaş hayvan yetiştiriciliği yapan işletmelerin yapısal özellikleri ve genel sorunlarının belirlenmesi amacıyla yapılmıştır. Araştırma materyalini Merkez ilçeye bağlı köylerde bulunan 168 küçükbaş hayvancılık işletmesinde yapılan anketler oluşturmuştur. Küçükbaş hayvancılık işletmelerinde ortalama koç, anaç koyun, kuzu, teke, anaç keçi ve oğlak sayıları sırasıyla 2.87, 81.27, 61.97, 0.89, 13.27 ve 11.27 baş olarak bulunmuştur. Yetiştiricilerin ortalama yaşı 48.75 ve ailedeki birey sayısı ise 9.68 kişi olarak saptanmıştır. Küçükbaş hayvan işletmelerinin ortalama sulu ve kıraç arazi varlığı sırasıyla 51.66 da ve 75.17 da olarak belirlenmiştir. Yetiştiriciler eğitim durumları bakımından ele alındığında; %69’u ilkokul, %3.6’sı lise mezunundan, %10.7’si ise okuma yazma bilmeyenlerden oluşmaktadır. Hayvan barınaklarının %100’ü kapalı ağıl şeklindedir. Barınak yapı malzemesi olarak %51.8’i kerpiç ve %33.3’ü ise brikettir. Koyun ve keçi yetiştiriciliğinin en önemli sorunları olarak; çoban bulma zorluğu, veterinerlik hizmetlerinin yetersizliği, genç nüfusun bu istihdam koluna talebinin azlığı ve büyük şehirlere göçün hızla artmasıdır. Bu sorunların giderilmesi, küçükbaş hayvancılık işletme sahiplerinin hayvancılığa olan ilgisini artıracağı ve ildeki küçükbaş hayvan yetiştiriciliğine bilgi kaynağı olacağı düşünülmektedir.
Van İli Merkez İlçede Küçükbaş Hayvancılık Faaliyetleri ve Genel Sorunlar: II. İşletmelerde Yetiştirme İşleri
Authors: YILDIZ Aşkın , AYGÜN Turgut
Published: June 2021

Bu araştırma, Van ili Merkez ilçede küçükbaş hayvan yetiştiriciliği yapan işletmelerin yetiştiricilik faaliyetleri ve genel sorunlarının belirlenmesi amacıyla yapılmıştır. Araştırma materyalini Merkez ilçeye bağlı köylerde bulunan 168 küçükbaş hayvan işletmesinden alınan anketler oluşturmuştur. Küçük aile işletmesi şeklinde etkinliğini sürdüren işletmeler bir araya gelerek ortak sürü (% 84.5) oluşturmaktadırlar. Çoban kullanımı bakımından aile işletmesinde aileden biri çobanlık yaparken, ortak sürüde sürü mevcuduna göre ücretli olarak geçici veya sürekli çoban tutulmaktadır. Yapılan anket sonucuna göre, küçükbaş hayvan yetiştiriciliği yapan işletme başına düşen ortalama süt verimi 38.89 kg olarak belirlenmiştir. Yetiştiricinin kamudan ücretsiz veterinerlik hizmeti, uzun vadeli kredi, damızlık hayvan temini ve ürünlerde fiyat istikrarı gibi beklentileri bulunmaktadır. Koyun ve keçi yetiştiriciliğinin en önemli sorunları olarak; çoban bulma zorluğu, veterinerlik hizmetlerinin yetersizliği, genç nüfusun bu istihdam koluna talebinin azlığı ve büyük şehirlere göçün hızla artmasıdır. Kamu ve yetiştiriciler biraraya gelmek suretiyle Van İlinde küçükbaş hayvan yetiştiriciliği, potansiyeli de göz önüne alınarak yeniden temel istihdam unsuru haline getirilebilir.
Evaluation of Herbicidal Potential of Eucalyptus spp for Biological Control of Dactyloctenium aegyptium L.
Authors: Ehtaiwesh Amal
Published: June 2021

Pots culture experiment was conducted to evaluates the herbicidal potential of Eucalyptus spp. (aceae) on growth of Dactyloctenium aegyptium L. (Poaceae). Leaves extract with concentrations of 10, 20 and 40% of Eucalyptus plant were applied as soil and foliar application to seeds and seedlings of D. aegyptium species.  The result indicated that the application of Eucalyptus leaves extracts caused significant reduction in germination traits, root and shoot length, seedling fresh and dry weights. Also, the result revealed that the Inhibitory effect of Eucalyptus extract on D. aegyptium L was more noticeable in plant growth traits than on germination traits. Results show that among the different concentration of Eucalyptus leave extracts used in this study, 40% was the most toxic and caused the significant effect on germination and growth traits of Dactyloctenium aegyptium. Application of 40% of Eucalyptus leave extract decreased germination percentage by 30%, mean daily germination by 45%, shoot length by 38%. root length by 54%, number of branches by 61%, number of leavesby59%, seedling fresh weights by 23%, seedling dry weights by 62% and seedling vigor index by 60%. The results of this study concluded that the use of Eucalyptus plants have the potential to be developed further as a bio-herbicide system to control weed such as Dactyloctenium aegyptium L. However, more researches are needed to evaluate the negative impact of Eucalyptus on crop growth.