bluuebunny/crossref_metadata_2025 huggingface dataset:
A large-eddy simulation study of deep-convection initiation through the collision of two sea-breeze fronts
Authors: Fu Shizuo , Rotunno Richard , Chen Jinghua , Deng Xin , Xue Huiwen
Published: June 2021

Abstract. Deep convection plays important roles in producing severe
weather and regulating the large-scale circulation. However, deep-convection
initiation (DCI), which determines when and where deep convection develops,
has not yet been fully understood. Here, large-eddy simulations are
performed to investigate the detailed processes of DCI, which occurs through
the collision of two sea-breeze fronts developing over a peninsula. In the
simulation with a maximum total heat flux over land of 700 or
500 W m−2, DCI is accomplished through the development of three generations
of convection. The first generation of convection is randomly produced along
the colliding sea-breeze fronts. The second generation of convection only
develops in regions where no strong downdrafts are produced by the first
generation of convection and is also mainly produced through the collision
of the sea-breeze fronts. The third generation of convection mainly develops
from the intersection points of the cold pools produced by the second
generation of convection and is produced through the collision between the
gust fronts and the sea-breeze fronts. Decreasing the maximum total heat
flux from 700 to 500 W m−2 weakens each generation of convection.
Further decreasing the maximum total heat flux to 300 W m−2 leads to
only one generation of shallow convection.
An automated system for trace gas flux measurements from plant foliage and other plant compartments
Authors: Kohl Lukas , Koskinen Markku , Polvinen Tatu , Tenhovirta Salla , Rissanen Kaisa , Patama Marjo , Zanetti Alessandro , Pihlatie Mari
Published: June 2021

Abstract. Plant shoots can act as sources or sinks of trace gases including methane and nitrous oxide. Accurate measurements of these trace gas fluxes require enclosing of shoots in closed non-steady-state chambers. Due to plant physiological activity, this type of enclosure, however, leads to CO2 depletion in the enclosed air volume, condensation of transpired water, and warming of the enclosures exposed to sunlight, all of which may bias the flux measurements. Here, we present ShoTGa-FluMS (SHOot Trace Gas FLUx Measurement System), a novel measurement system designed for continuous and automated measurements of trace gas and volatile organic compound (VOC) fluxes from plant shoots. The system uses transparent shoot enclosures equipped with Peltier cooling elements and automatically replaces fixated CO2 and removes transpired water from the enclosure. The system is designed for measuring trace gas fluxes over extended periods, capturing diurnal and seasonal variations, and linking trace gas exchange to plant physiological functioning and environmental drivers. Initial measurements show daytime CH4 emissions of two pine shoots of 0.056 and
0.089 nmol per gram of foliage dry weight (d.w.) per hour or 7.80 and 13.1 nmolm-2h-1. Simultaneously measured CO2 uptake rates were 9.2 and 7.6 mmolm-2h-1, and transpiration rates were 1.24 and 0.90 molm-2h-1. Concurrent measurement of VOC emissions demonstrated that potential effects of spectral interferences on CH4 flux measurements were at least 10-fold smaller than the measured CH4 fluxes. Overall, this new system solves multiple technical problems that have so far prevented automated plant shoot trace gas flux measurements and holds the potential for providing important new insights into the role of plant foliage in the global CH4 and N2O cycles.
FIXED-WING MICRO UAV OPEN DATA WITH DIGICAM AND RAWINS/GNSS
Authors: Skaloud J. , Cucci D. A. , Joseph Paul K.
Published: June 2021

Abstract. We have recently released in the open domain data originating from a series of flights conducted with a fixed-wing micro UAV carrying high-quality small camera and navigation sensors. This data was previously used in several peer-reviewed publications. However, the data that we describe in the following is part of a larger series that will be released gradually after incorporating user feedback (e.g., on formats, description, etc.) from the first (three) released open data-sets. In the first part of this work we provide a thorough description of the common elements of these data sets, notably the UAV, its sensors, methods of time-stamping and synchronizing data streams, reference geometrical relations among them (system calibration) as well as time-invariant sensor parameters (e.g., lens distortion, non-orthogonality of inertial sensors) together with ground control points that are valid over the whole series. In the second part we describe the individual missions and provide the links to the released data sets.
ROBUST ESTIMATION IN ROBOT VISION AND PHOTOGRAMMETRY: A NEW MODEL AND ITS APPLICATIONS
Authors: Li J. , Zhang Y. , Hu Q.
Published: June 2021

Abstract. Robust estimation (RE) is a fundamental issue in robot vision and photogrammetry, which is the theoretical basis of geometric model estimation with outliers. However, M-estimations solved by iteratively reweighted least squares (IRLS) are only suitable for cases with low outlier rates (< 50%); random sample consensus (RANSAC) can only obtain approximate solutions. In this paper, we propose an accurate and general RE model that unifies various robust costs into a common objective function by introducing a “robustness-control” parameter. It is a superset of typical least-squares, l1-l2, Cauchy, and Geman-McClure estimates. We introduce a parameter-decreasing strategy into the IRLS to optimize our model, called adaptive IRLS. The adaptive IRLS begins with a least-squares estimate for initialization. Then, the “robustness-control” parameter is decreased along with iterations so that the proposed model acts as different robust loss functions and has different degrees of robustness. We also apply the proposed model in several important tasks of robot vision and photogrammetry, such as line fitting, feature matching, image orientation, and point cloud registration (scan matching). Extensive simulated and real experiments show that the proposed model is robust to more than 80% outliers and preserves the advantages of M-estimations (fast and optimal). Our source code will be made publicly available in https://ljy-rs.github.io/web.
DYNAMIC ROUTING FOR NAVIGATION IN CHANGING UNKNOWN MAPS USING DEEP REINFORCEMENT LEARNING
Authors: Han Y. , Yilmaz A.
Published: June 2021

Abstract. In this work, we propose an approach for an autonomous agent that learns to navigate in an unknown map in a real-world environment. Recognizing that the real-world environment is changing overtime such as road-closure happening due to construction work, a key contribution of our paper is adopt the dynamic adaptation characteristic of the reinforcement learning approach and develop a dynamic routing ability for our agent. Our method is based on the Q-learning algorithm and modifies it into a double-critic Qlearning model (DCQN) that only uses visual input without other aids such as GPS. Our treatment of the problem enables the agent to learn the navigation policy while interacting with the environment. We demonstrate that the agent can learn navigating to the destination kilometers away from the starting point in a real world scenario and has the ability to respond to environment changes while learning to adjust the routing plan dynamically by adjusting the old knowledge. The supplementary video can be accessed at the following link: https://www.youtube.com/watch?v=tknsxVuNwkg.
A WEB-BASED PLATFORM FOR MANAGEMENT AND VISUALIZATION OF GEOMETRIC DOCUMENTATION PRODUCTS OF CULTURAL HERITAGE SITES
Authors: Ioannidis C. , Tallis I. , Pastos I. , Boutsi A.-M. , Verykokou S. , Soile S. , Tokmakidis P. , Tokmakidis K.
Published: June 2021

Abstract. The mission of digital Cultural Heritage (CH) repositories goes beyond engagement and dissemination; their research data should be approached and correlated spatially and semantically in the service of conservation and accessibility. Therefore, a flexible system is needed to expand, reuse and repurpose their content, addressing online collaboration, real time progress and ease of use. Towards this direction, the “METEORA” web-based platform is a novel and holistic approach to the organization, management and visualization of the 3D and 2D documentation products of CH sites. It integrates a multi-scale 3D viewer based on the 3DHOP framework, personalized information access and interactive tools for data retrieval and presentation. Emphasis is given in the development of a mid-level interface for creating, updating and maintaining 3DHOP’s functionalities and customizing information based on the user’s scientific specialty and field of interest. The proposed admin control panel updates the state of the current visualization, allows the integration of 3D models and multimedia and automates the creation of clickable points of interest on top of the 3D surfaces. It is based on a MySQL Relational Database Management System (RDBMS) that supports the back-end User Interface (UI) with create, read, update and delete (CRUD) management functions of a typical RESTful service. Moreover, all the control mechanisms as well as the procedures of authentication, authorization and encryption of the credentials of the certified users are configured through the PHP scripting language and JSON files. The resulting platform achieves maximum adaptability in digital archiving of heterogeneous datasets and has increasing efficiency in representing all types of data supported by 3DHOP.
TESSERAE3D: A BENCHMARK FOR TESSERAE SEMANTIC SEGMENTATION IN 3D POINT CLOUDS
Authors: Kharroubi A. , Van Wersch L. , Billen R. , Poux F.
Published: June 2021

Abstract. 3D point cloud of mosaic tesserae is used by heritage researchers, restorers and archaeologists for digital investigations. Information extraction, pattern analysis and semantic assignment are necessary to complement the geometric information. Automated processes that can speed up the task are highly sought after, especially new supervised approaches. However, the availability of labelled data necessary for training supervised learning models is a significant constraint. This paper introduces Tesserae3D, a 3D point cloud benchmark dataset for training and evaluating machine learning models, applied to mosaic tesserae segmentation. It is a publicly available, very high density and coloured dataset, accompanied by a standard multi-class semantic segmentation baseline. It consists of about 502 million points and contains 11 semantic classes covering a wide range of tesserae types. We propose a semantic segmentation baseline building on radiometric and covariance features fed to ensemble learning methods. The results delineate an achievable 89% F1-score and are made available under https://github.com/akharroubi/Tesserae3D, providing a simple interface to improve the score based on feedback from the research community.
AUTOMATIC POINT CLOUD SEGMENTATION FOR THE DETECTION OF ALTERATIONS ON HISTORICAL BUILDINGS THROUGH AN UNSUPERVISED AND CLUSTERING-BASED MACHINE LEARNING APPROACH
Authors: Musicco A. , Galantucci R. A. , Bruno S. , Verdoscia C. , Fatiguso F.
Published: June 2021

Abstract. The article describes an innovative procedure for the three-dimensional analysis of decay morphologies of ancient buildings, through the application of machine learning methods for the automatic segmentation of point clouds. In the field of Cultural Heritage conservation, photogrammetric data can be exploited, for diagnostic and monitoring support, to recognize different typologies of alterations visible on the masonry surface, starting from colour information. Actually, certain stone and plaster surface pathologies (biological patina, biological colonization, chromatic alterations, spots,…) are typically characterized by chromatic variations. To this purpose, colour-based segmentation with hierarchical clustering has been implemented on colour data of point clouds, considered in the HSV colour-space. In addition, geometry-based segmentation of 3D reconstructions has been performed, in order to identify the main architectural elements (walls, vaults), and to associate them to the detected defects. The proposed workflow has been applied to some ancient buildings’ environments, chosen because of their irregularity both in geometrical and colorimetric characteristics.
AUTOMATED AND PERMANENT LONG-RANGE TERRESTRIAL LASER SCANNING IN A HIGH MOUNTAIN ENVIRONMENT: SETUP AND FIRST RESULTS
Authors: Voordendag A. B. , Goger B. , Klug C. , Prinz R. , Rutzinger M. , Kaser G.
Published: June 2021

Abstract. A terrestrial laser scanner (TLS) of the type RIEGL VZ-6000 has been permanently installed and automated at Hintereisferner glacier located in the Ötztal Alps, Austria, to identify snow (re)distribution from surface height changes. A first case study is presented that shows and discusses detected snow distribution at the glacier after a snowfall event, together with concurrent snow erosion and deposition caused by avalanches. The paper shows the potential of a TLS system in a high mountain environment, which is also applicable to other environmental mapping applications. It introduces the setup of the TLS system, its automation procedure, and a first and preliminary uncertainty analysis. TLS data are generally influenced by four uncertainty sources: atmospheric conditions, scanning geometry, mechanical properties, and surface reflectance properties. The first three sources have significant influence on the TLS data at Hintereisferner, whereby the total accuracy of the TLS system is estimated to be in a range of a few decimetres, subject to ongoing more detailed data analysis.
IMAGE TO POINT CLOUD TRANSLATION USING CONDITIONAL GENERATIVE ADVERSARIAL NETWORK FOR AIRBORNE LIDAR DATA
Authors: Shinohara T. , Xiu H. , Matsuoka M.
Published: June 2021

Abstract. This study introduces a novel image to a 3D point-cloud translation method with a conditional generative adversarial network that creates a large-scale 3D point cloud. This can generate supervised point clouds observed via airborne LiDAR from aerial images. The network is composed of an encoder to produce latent features of input images, generator to translate latent features to fake point clouds, and discriminator to classify false or real point clouds. The encoder is a pre-trained ResNet; to overcome the difficulty of generating 3D point clouds in an outdoor scene, we use a FoldingNet with features from ResNet. After a fixed number of iterations, our generator can produce fake point clouds that correspond to the input image. Experimental results show that our network can learn and generate certain point clouds using the data from the 2018 IEEE GRSS Data Fusion Contest.
TOWARDS EFFICIENT INDOOR/OUTDOOR REGISTRATION USING PLANAR POLYGONS
Authors: Djahel R. , Vallet B. , Monasse P.
Published: June 2021

Abstract. The registration of indoor and outdoor scans with a precision reaching the level of geometric noise represents a major challenge for Indoor/Outdoor building modeling. The basic idea of the contribution presented in this paper consists in extracting planar polygons from indoor and outdoor LiDAR scans, and then matching them. In order to cope with the very small overlap between indoor and outdoor scans of the same building, we propose to start by extracting points lying in the buildings’ interior from the outdoor scans as points where the laser ray crosses detected façades. Since, within a building environment, most of the objects are bounded by a planar surface, we propose a new registration algorithm that matches planar polygons by clustering polygons according to their normal direction, then by their offset in the normal direction. We use this clustering to find possible polygon correspondences (hypotheses) and estimate the optimal transformation for each hypothesis. Finally, a quality criteria is computed for each hypothesis in order to select the best one. To demonstrate the accuracy of our algorithm, we tested it on real data with a static indoor acquisition and a dynamic (Mobile Laser Scanning) outdoor acquisition.
SEMI-SUPERVISED SEGMENTATION OF CONCRETE AGGREGATE USING CONSENSUS REGULARISATION AND PRIOR GUIDANCE
Authors: Coenen M. , Schack T. , Beyer D. , Heipke C. , Haist M.
Published: June 2021

Abstract. In order to leverage and profit from unlabelled data, semi-supervised frameworks for semantic segmentation based on consistency training have been proven to be powerful tools to significantly improve the performance of purely supervised segmentation learning. However, the consensus principle behind consistency training has at least one drawback, which we identify in this paper: imbalanced label distributions within the data. To overcome the limitations of standard consistency training, we propose a novel semi-supervised framework for semantic segmentation, introducing additional losses based on prior knowledge. Specifically, we propose a lightweight architecture consisting of a shared encoder and a main decoder, which is trained in a supervised manner. An auxiliary decoder is added as additional branch in order to make use of unlabelled data based on consensus training, and we add additional constraints derived from prior information on the class distribution and on auto-encoder regularisation. Experiments performed on our concrete aggregate dataset presented in this paper demonstrate the effectiveness of the proposed approach, outperforming the segmentation results achieved by purely supervised segmentation and standard consistency training.
MARRYING DEEP LEARNING AND DATA FUSION FOR ACCURATE SEMANTIC LABELING OF SENTINEL-2 IMAGES
Authors: Fonteix G. , Swaine M. , Leras M. , Tarabalka Y. , Tripodi S. , Trastour F. , Giraud A. , Laurore L. , Hyland J.
Published: June 2021

Abstract. The understanding of the Earth through global land monitoring from satellite images paves the way towards many applications including flight simulations, urban management and telecommunications. The twin satellites from the Sentinel-2 mission developed by the European Space Agency (ESA) provide 13 spectral bands with a high observation frequency worldwide. In this paper, we present a novel multi-temporal approach for land-cover classification of Sentinel-2 images whereby a time-series of images is classified using fully convolutional network U-Net models and then coupled by a developed probabilistic algorithm. The proposed pipeline further includes an automatic quality control and correction step whereby an external source can be introduced in order to validate and correct the deep learning classification. The final step consists of adjusting the combined predictions to the cloud-free mosaic built from Sentinel-2 L2A images in order for the classification to more closely match the reference mosaic image.
MONITORING AND ASSESSMENT OF AGRI-URBAN LAND CONVERSION USING MULTI-SENSOR REMOTE SENSING AND GIS TECHNIQUES
Authors: Fargas Jr. D. C. , Narciso G. A. M. , Blanco A. C.
Published: June 2021

Abstract. Continuous agricultural land conversion poses threat to food security but this has not been monitored due to ineffectual policies. One of the Philippine provinces with a high rate of conversion is the rice-producing province of Cavite. To assess the spatiotemporal dynamics of agricultural land conversion in Cavite, this study aims to develop an operational methodology to produce Land Use and Land Cover (LULC) change maps using a multi-sensor remote sensing approach for decision making and planning. LULC maps were generated using Random Forest Classification of Landsat 8 and Sentinel-1 image collections. Spectral indices, combinations of radar polarizations (VV, VH), and their principal components were included to improve its accuracy. Conversion maps were generated by taking the bi-annual difference of LULC maps from 2016 to 2019. Accuracy was assessed using visual inspection with Google Earth Pro. Classification was carried out using single-sensor (optical or radar) and multi-sensor (optical and radar) approach in combination with three feature selection algorithms, namely, Sandri and Zuccolotto (2006), Liaw and Wiener (2015), Kursa and Rudnicki (2010). Multi-sensor and single sensor yielded similarly high overall accuracies (OA = 96%) with the exception of single-sensor radar approach (OA = 53%). Multi-sensor approaches exhibit high accuracies (Cumulative Accuracy = 91%) in detecting agricultural to built-up LULC change up to 5,000 square meters unlike single-sensor optical approach (Cumulative Accuracy = 76%). Among the multi-sensor approaches, the method of Liaw and Wiener (2015) remains to be superior as it only uses eight (8) variables.
ADVERSARIAL DISCRIMINATIVE DOMAIN ADAPTATION FOR DEFORESTATION DETECTION
Authors: Noa J. , Soto P. J. , Costa G. A. O. P. , Wittich D. , Feitosa R. Q. , Rottensteiner F.
Published: June 2021

Abstract. Although very efficient in a number of application fields, deep learning based models are known to demand large amounts of labeled data for training. Particularly for remote sensing applications, responding to that demand is generally expensive and time consuming. Moreover, supervised training methods tend to perform poorly when they are tested with a set of samples that does not match the general characteristics of the training set. Domain adaptation methods can be used to mitigate those problems, especially in applications where labeled data is only available for a particular region or epoch, i.e., for a source domain, but not for a target domain on which the model should be tested. In this work we introduce a domain adaptation approach based on representation matching for the deforestation detection task. The approach follows the Adversarial Discriminative Domain Adaptation (ADDA) framework, and we introduce a margin-based regularization constraint in the learning process that promotes a better convergence of the model parameters during training. The approach is evaluated using three different domains, which represent sites in different forest biomes. The experimental results show that the approach is successful in the adaptation of most of the domain combination scenarios, usually with considerable gains in relation to the baselines.
POSITIVE AND NEGATIVE ROUGHNESS ACCORDING TO LOCAL DIFFERENCES BETWEEN DEM SURFACE AND 3D REFERENCE PLANES
Authors: Parrot J.-F. , Ramírez-Núñez C.
Published: June 2021

Abstract. The irregularities of the earth’s surface are quantified by means of roughness measurements using Digital Elevation Models (DEM’s). This article presents a roughness measurement method that is based on the calculation of the difference of altitude existing between a plane passing through the centre of a moving window and the altitude of the DEM surface inside this window. This method differs from the measure of the standard deviation and best fit plane, in the sense that it considers all difference values, positives or negatives. The measurement is done in a 3 × 3 or a 5 × 5 moving window and contemplates inside this window the plane which passes through the centre of the window and the highest pixel located in the border or perimeter of this window. According to the 3D configuration of the DEM surface inside the moving window, the sum of all the differences is positive or negative, allowing to discriminate the local morphology independently of the global roughness. The roughness variable which distinguishes negative and positive values allows to classify accurately landscape units such as watersheds, riverbeds, volcanic assemblages as well as landforms associated with tectonic structures.
COMPARISON OF CLASSIFICATION ALGORITHMS OF IMAGES FOR THE MAPPING OF THE LAND COVERING IN TASSO FRAGOSO MUNICIPALITY, BRAZIL
Authors: Pereira P. R. M. , Costa F. W. D. , Bolfe E. L. , Macarringe L. , Botelho A. C.
Published: June 2021

Abstract. One of the main applications of satellite images is the characterization of terrestrial coverage, that from the use of classification techniques, allows the monitoring of spatial transformations of the terrestrial surface, this process being directly associated with the potential of classifiers to differentiate the most diverse data present in the images, a fundamental aspect for the use of remote sensing data. This article evaluates the performance of different classification algorithms in the mapping classes of land use and land cover in medium resolution images from the Landsat 8 program, the test area of this test corresponds to the Municipality of Tasso Fragoso (State Maranhão - Brazil), stands out for a typical vegetation cover of the Cerrado Biome, presents similar spectral patterns that induce high difficulty of class differentiation automatically. In this paper, were analyzed the machine learning algorithms C5.0 and Random Forest in comparison to traditional classification algorithms being the Minimum Distance and the Spectral Angle Mapper. The best results were generated by Random Forest with 90% accuracy and Kappa of 0.861, followed by the C5.0 algorithm. Traditional algorithms, on the other hand, presented a lower precision rate with global accuracy, not exceeding 75% of accuracy and Kappa varying between 0.507 and 0.627. The accuracy of the producer showed that all the algorithms, in major or minor tendency presented difficulties in to differentiate the areas, with rates of mistakes varying between 25 and 75%, being the main, the confusion with pastoral areas.
INVESTIGATIONS ON FEATURE SIMILARITY AND THE IMPACT OF TRAINING DATA FOR LAND COVER CLASSIFICATION
Authors: Voelsen M. , Lobo Torres D. , Feitosa R. Q. , Rottensteiner F. , Heipke C.
Published: June 2021

Abstract. Fully convolutional neural networks (FCN) are successfully used for pixel-wise land cover classification - the task of identifying the physical material of the Earth’s surface for every pixel in an image. The acquisition of large training datasets is challenging, especially in remote sensing, but necessary for a FCN to perform well. One way to circumvent manual labelling is the usage of existing databases, which usually contain a certain amount of label noise when combined with another data source. As a first part of this work, we investigate the impact of training data on a FCN. We experiment with different amounts of training data, varying w.r.t. the covered area, the available acquisition dates and the amount of label noise. We conclude that the more data is used for training, the better is the generalization performance of the model, and the FCN is able to mitigate the effect of label noise to a high degree. Another challenge is the imbalanced class distribution in most real-world datasets, which can cause the classifier to focus on the majority classes, leading to poor classification performance for minority classes. To tackle this problem, in this paper, we use the cosine similarity loss to force feature vectors of the same class to be close to each other in feature space. Our experiments show that the cosine loss helps to obtain more similar feature vectors, but the similarity of the cluster centers also increases.
A FULLY AUTOMATED AND FAST APPROACH FOR CANOPY COVER ESTIMATION USING SUPER HIGH-RESOLUTION REMOTE SENSING IMAGERY
Authors: Maimaitijiang M. , Sagan V. , Bhadra S. , Nguyen C. , Mockler T. C. , Shakoor N.
Published: June 2021

Abstract. Canopy cover is a key agronomic variable for understanding plant growth and crop development status. Estimation of canopy cover rapidly and accurately through a fully automated manner is significant with respect to high throughput plant phenotyping. In this work, we propose a simple, robust and fully automated approach, namely a rule-based method, that leverages the unique spectral pattern of green vegetation at visible (VIS) and near-infrared red (NIR) spectra regions to distinguish the green vegetation from background (i.e., soil, plant residue, non-photosynthetic vegetation leaves etc.), and then derive canopy cover. The proposed method was applied to high-resolution hyperspectral and multispectral imagery collected from gantry-based scanner and Unmanned Aerial Vehicle (UAV) platforms to estimate canopy cover. Additionally, machine learning methods, i.e., Support Vector Machine (SVM) and Random Forest (RF) were also employed as bench mark methods. The results show that: the rule-based method demonstrated promising classification accuracies that are comparable to SVM and RF for both hyperspectral and multispectral datasets. Although the rule-based method is more sensitive to mixed pixels and shaded canopy region, which potentially resulted in classification errors and underestimation of canopy cover in some cases; it showed better performance to detect smaller leaves than SVM and RF. Most importantly, the rule-based method substantially outperformed machine learning methods with respect to processing speed, indicating its greater potential for high-throughput plant phenotyping applications.
ESTIMATION OF LAND SURFACE ALBEDO FROM GCOM-C/SGLI SURFACE REFLECTANCE
Authors: Susaki J. , Sato H. , Kuriki A. , Kajiwara K. , Honda Y.
Published: June 2021

Abstract. This paper examines algorithms for estimating terrestrial albedo from the products of the Global Change Observation Mission – Climate (GCOM-C) / Second-generation Global Imager (SGLI), which was launched in December 2017 by the Japan Aerospace Exploration Agency. We selected two algorithms: one based on a bidirectional reflectance distribution function (BRDF) model and one based on multi-regression models. The former determines kernel-driven BRDF model parameters from multiple sets of reflectance and estimates the land surface albedo from those parameters. The latter estimates the land surface albedo from a single set of reflectance with multi-regression models. The multi-regression models are derived for an arbitrary geometry from datasets of simulated albedo and multi-angular reflectance. In experiments using in situ multi-temporal data for barren land, deciduous broadleaf forests, and paddy fields, the albedos estimated by the BRDF-based and multi-regression-based algorithms achieve reasonable root-mean-square errors. However, the latter algorithm requires information about the land cover of the pixel of interest, and the variance of its estimated albedo is sensitive to the observation geometry. We therefore conclude that the BRDF-based algorithm is more robust and can be applied to SGLI operational albedo products for various applications, including climate-change research.
POST-DISASTER ASSESSMENT OF MANGROVE FOREST RECOVERY IN LAWAAN-BALANGIGA, EASTERN SAMAR USING NDVI TIME SERIES ANALYSIS
Authors: Cabello K. E. , Germentil M. Q. , Blanco A. C. , Macatulad E. G. , Salmo III S. G.
Published: June 2021

Abstract. In 2013, Typhoon Haiyan (Yolanda) struck the Eastern Philippines. Mangrove forests in the area were destroyed and were estimated to have at least 86% of damage. Some studies done on the typhoon-stricken mangroves had collected data such as measurements of mangrove trunk, height, roots, and seedlings to investigate the extent of damage and recovery. While these studies were proven to effectively identify mangrove gains and losses, these methods are only applicable in sites that are relatively accessible. This paper highlights the relevance of effective remote monitoring of mangrove forests that are vulnerable to typhoons including post-typhoon recovery. In this study, a Time Series Analysis using Google Earth Engine (GEE) was applied in assessing the damages and recovery of mangroves struck by Super Typhoon Haiyan in Lawaan and Balangiga, Samar (Eastern Philippines). The changes in mangrove extent followed the changes in NDVI; however, there were significant site-specific differences. Based on NDVI values, it was estimated that 83% of the mangrove area was damaged. After three years, regeneration from 2014–2017 was about 144%. Mangroves steadily developed but with a minimal change of 2.83% from 2017–2019. Most villages followed the general recovery trends in Lawaan and Balangiga. However, based on the time series analysis, some villages have minimal recovery than others. It suggests that the recovery of mangroves may be a function of the pre-typhoon mangrove extent and possibly vegetation condition. Even if there were new spaces for mangroves to colonize, some of the sites may not be conducive for plant regrowth.
A CALCULATION METHOD OF ECOSYSTEM SERVICE VALUE BASED ON PRODUCT OF NATIONAL GEOGRAPHIC CONDITIONS MONITORING IN CHINA
Authors: Cheng T. , Zheng X. , Chen H. , Liu J. , Gao X. , Zhang W.
Published: June 2021

Abstract. By 2015, Chinese government had completed the project of China's First National Geographic Conditions Census. In this project, high resolution land cover product all over the country had been generated, and would be updated continuously every year. On the basis of this excellent data source, a big data calculating method of land’s ecosystem service value was proposed, in which many other remote sensing information were used too, such as EVI (Enhanced Vegetation Index), NPP (Net Primary Productivity), vegetation growing season data derived from MODIS product. It analyzed the characters of data type, data time phase, and data structure for all the remote sensing information, also the big data’s engendering background and process. A revised ecosystem service value assessment model was used for calculating. Combining the classification system of terrestrial ecosystem in China and the equivalent value factor per unit ecosystem area, the big data calculating algorithm was designed. Shiyan city, Hubei province, China was selected as the study area for validating the calculating method. The results showed that the total ecosystem service value in Shiyan city in 2015 was 1.97 × 1011 CNY, and the per capita ecosystem service value was 5.69 × 104 CNY. Specially, forest supplied the most ecosystem service value which accounted for 78.54 %, followed by water, grassland, farmland, and desert. The research shows that on the basis of multi-source of remote sensing information mainly the high resolution land cover product obtained in the project of China's First National Geographic Conditions Census, high-precision quantification and spatialization ecosystem service value can be calculated and obtained; multi scale spatial display of the calculating results could be achieved to meet different spatial scaling demands; the big data calculating algorithm has solved the problems of design and computation of structured and unstructured big data computing models; the independent research and development software has solved the problem of software requirements, and the operational efficiency and performance can meet the calculating needs.
ADVANCED SURFACE-REFLECTED RADIANCE CORRECTION FOR AIRBORNE HYPERSPECTRAL IMAGERY IN COASTAL RED TIDE DETECTION
Authors: Min J.-E. , Lee S.-K. , Ryu J.-H.
Published: June 2021

Abstract. Red tides are among the most common coastal hazards, causing serious damage to the coastal environment. Many satellite sensors can detect red tide blooms, but are limited in their detection of the exact area of the bloom and biological abundance in terms of spatial and spectral resolution. The high spatial and spectral resolutions of hyperspectral airborne remote sensing data may help overcome these limitations to analyze red tide blooms more effectively. To identify potential applications of hyperspectral airborne data in red tide detection, an integrated field campaign was performed in September 2016 off the coast of Tongyeong, South Korea. An AisaEAGLE sensor was installed on a Cessna 208B crewed aircraft to obtain hyperspectral images of an 18 km × 18 km coastal area. To assess the atmospheric correction of the hyperspectral data, in situ optical data and water samples were measured on two vessels concurrent with the flight path. Advanced surface-reflected radiance (Lr) correction and basic atmospheric path radiance (La) correction were performed on the hyperspectral images. Of these, Lr correction comprised a large proportion of the atmospheric correction. The atmosphere-corrected remote sensing reflectance data of the hyperspectral images closely matched the in-situ measurements. The data were assessed for red tide events using ratio analysis and the fluorescence line height technique; the ratio analysis more effectively detected regions with suspected red tides.
TOWARDS FINE-GRAINED ROAD MAPS EXTRACTION USING SENTINEL-2 IMAGERY
Authors: Ayala C. , Aranda C. , Galar M.
Published: June 2021

Abstract. Nowadays, it is highly important to keep road maps up-to-date since a great deal of services rely on them. However, to date, these labours have demanded a great deal of human attention due to their complexity. In the last decade, promising attempts have been carried out to fully-automatize the extraction of road networks from remote sensing imagery. Nevertheless, the vast majority of methods rely on aerial imagery (< 1 m), whose costs are not yet affordable for maintaining up-to-date maps. This work proves that it is also possible to accurately detect roads using high resolution satellite imagery (10 m). Accordingly, we have relied on Sentinel-2 imagery considering its freely availability and the higher revisit times compared to aerial imagery. It must be taken into account that the lack of spatial resolution of this sensor drastically increases the difficulty of the road detection task, since the feasibility to detect a road depends on its width, which can reach sub-pixel size in Sentinel-2 imagery. For that purpose, a new deep learning architecture which combines semantic segmentation and super-resolution techniques is proposed. As a result, fine-grained road maps at 2.5 m are generated from Sentinel-2 imagery.
AUGMENTED REALITY ASSET TRACKING USING HOLOLENS
Authors: Fan J. I. , Khoshelham K.
Published: June 2021

Abstract. Asset Tracking is an essential component of building management process. It involves creating and maintaining a database of detailed information of assets such as location, condition, brand, and type. This information can help building owners make informed decisions for cost-effective maintenance of building assets. Existing approaches to perform asset tracking require a manual process of measuring and recording the asset condition and location, which is labour-intensive and costly. The typical approach usually includes a human operator with pen and paper inspecting the site and manually recording the information about the asset. In this paper, we propose an augmented reality asset tracking system using HoloLens to reduce the manual labour involved in this process. The system can automatically detect the asset, record and update its related information by visual inspection. Assets are detected by feeding images captured by the HoloLens built-in camera to a pre-trained object detection network. Using a combination of various sensor readings from the HoloLens, the system can estimate the location of the asset using visual simultaneous localization and mapping (vSLAM). This information is then viewed and verified by the user using the augmented reality user interface. Upon the user confirmation, this information will be uploaded to a database. As a case study, we demonstrate a vending machine tracking system which is able to detect and localise the vending machines in an indoor environment and create a database of vending machine information. The system can detect vending machines with a mean average precision of 94.8% and a localization accuracy of 2.3 meters without pre-screening or user input.
SUITABLE DEM ASSESSMENT STUDY FOR DATA SCARCE CITIES TOWARDS URBAN EXTREME AND NUISANCE FLOOD MAPPING
Authors: Akkimi A. , Dutta S.
Published: June 2021

Abstract. The high-resolution accurate topography data should be used for extreme and nuisance flood inundation modeling and mapping in cities, but not available for many cities, including most developed countries. It is necessary to study and identify an alternate open-source topographic model that satisfies high-resolution topography datasets’ conditions. We analyzed the open-source DEMs visually, elevation histogram statistics, streams and watershed identification, contour statistics, Topographic Wetness Index, and vertical accuracy of other medium-resolution DEMs compared with high-resolution LiDAR data over New York City to determine alternative open-source Digital Elevation Model in the context of urban flood modeling. In high urban sprawl areas, in the context of flood mapping, our findings have shown that the medium resolution DEMs predicted similar to high-resolution DEMs with the same linear errors around RMSE 25–35ft and LE90 30–40ft. Overall, the ALOS AW3D performed better than other open-source DEMs. Even though SRTM predicted well, it inducted smoothness in DEM where more buildings were located. It noted that ALOS PALSAR DEM is not suitable for any urban studies. ASTER DEM has also shown good agreement with LiDAR and observed elevations, but it induced by noise while processing. Finally, it can be suggested that the ALOS AW3D can be used as an alternative source for urban flood modeling which represented footprints of buildings even though it performed average in vertical accuracy.
EMBODIED DIGITAL TWINS FOR ENVIRONMENTAL APPLICATIONS
Authors: Klippel A. , Sajjadi P. , Zhao J. , Wallgrün J. O. , Huang J. , Bagher M. M.
Published: June 2021

Abstract. The synergies of advances in environmental sensing and modeling and the mainstreaming of immersive technologies lay the foundation for a theoretical grounding of embodied digital twins. Embodied digital twins draw on an established understanding of the importance of place for environmental sciences as well as a paradigmatic shift in the cognitive sciences toward embodied cognition. Nonetheless, the excitement of realizing embodied experiences through immersive technologies such as augmented and virtual reality stands in stark contrast to a lack of consistent terminology and empirical research. In this vision paper, we are proposing to draw more deeply on the theoretical basis of embodied cognition and to establish research frameworks for advancing embodied digital twins. We discuss several examples for turning environmentally sensed and modeled information into high-fidelity immersive experiences and provide a discussion and outlook on critical topics to address.
MAPPING OF 3D EYE-TRACKING IN URBAN OUTDOOR ENVIRONMENTS
Authors: Kollert A. , Rutzinger M. , Bremer M. , Kaufmann K. , Bork-Hüffer T.
Published: June 2021

Abstract. New geospatial technologies and ubiquitous sensing allow new insights into people’s spatial practices and experiences of public spaces. These tools offer new data streams for analysis and interpretation of social phenomena. Mobile augmented reality tools such as smartphones and wearables merge the experience of entangled online and offline spaces in citizen’s daily life. This paper demonstrates a concept that combines eye-tracking tools with innovative mapping in order to enhance the interpretability of real outdoor environmental experiences. Through videogrammetry, a participants’ head posture can be reconstructed. Subsequently the fixations measured through eye-tracking are projected onto a 3D point cloud of the surrounding environment. The presented methodological approach is implemented in the interdisciplinary project DigitAS – The Digital, Affects and Space – which investigates the perception of public places as spaces of recreation, security or fear. The project’s Mixed Methods approach combined qualitative, mobile, in-situ and reconstructive methods with eye-tracking in an outdoor setting. Potentials of the geospatial mapping concept for social science research is discussed.
INTEGRATION BETWEEN SURFACE AND SUBSURFACE SPATIAL OBJECTS FOR DEVELOPING OMAN 3D SDI BASED ON THE CITYGML STANDARD
Authors: Al Kalbani K. , Abdul Rahman A.
Published: June 2021

Abstract. The paper investigates the capability to integrate the surface and subsurface 3D spatial objects data structure within the 3D spatial data infrastructure (3D SDI) based on the CityGML standards. In fact, a number of countries around the world have started applying the 3D city models for their planning and infrastructure management. While others are still working toward 3D SDI by using CityGML standards. Moreover, most of these initiatives focus on the surface spatial objects with less interest to model subsurface spatial objects. However, dealing with 3D SDI requires both surface and subsurface spatial objects with clear consideration on the issues and challenges in terms of the data structure. On the other hand, the study has used geospatial tools and databases such as FME, PostgreSQL-PostGIS, and 3D City Database to generate the 3D model and to test the capability for integrating the surface and subsurface 3D spatial objects data structure within the 3D SDI. This paper concludes by describing a framework that aims to integrate surface and subsurface 3D geospatial objects data structure in Oman SDI. The authors believe that there are possible solutions based on CityGML standards for surface and subsurface 3D spatial objects. Moreover, solving the issues in data structure can establish a better vision and open new avenues for the 3D SDI.
A review of drought indices: predominance of drivers over impacts and the importance of local context
Authors: Kchouk Sarra , Melsen Lieke A. , Walker David W. , van Oel Pieter R.
Published: June 2021

Abstract. Drought monitoring and Early Warning Systems (DEWS) are seen as helpful tools to tackle drought at an early stage and reduce the possibility of harm or loss. They usually include indices attributed to meteorological, agricultural and/or hydrological drought: physically based drought drivers. These indices are used to determine the onset, end and severity of a drought event. Drought impacts are less monitored or even not included in DEWS. Therefore, the likelihood of experiencing drought impacts is often simply linearly linked to drivers of drought. The aim of this study is to evaluate the validity of the assumed direct linkage between drivers of drought and drought impact. We reviewed scientific literature on both drivers and impacts of drought. We conducted a bibliometric analysis based on 5000+ scientific studies in which selected drought indices (drivers) and drought impacts were mentioned in relation to a geographic area. Our review shows that there is a tendency in scientific literature to focus on drivers of drought, with the preferred use of meteorological and remotely sensed drought indices. Studies reporting drought impacts are more localised, with relatively many studies focusing on Sub-Saharan Africa and Australasia for impacts with regard to food security and water security, respectively. Our review further suggests that drought-impacts studies are dependent on both the physical and human processes occurring in the geographic area, i.e. the local context. With the aim of increasing the relevance and utility of the information provided by DEWS, we argue in favour of additional consideration of drought impact indices oriented towards sustainable development and human welfare.