bluuebunny/crossref_metadata_2025 huggingface dataset:
LIDAR-BASED MULTI-SENSOR MODULE DEVELOPMENT AND ASSESSMENT OF APPLICABILITY FOR DAMAGE INVESTIGATION OF INDOOR DISASTER
Authors: Shin D. Y. , Cho D. H. , Hong J. W. , Han S. H. , Cho S. B. , Lim E. T. , Jung Y. H. , Kim S. S.
Published: May 2022

Abstract. As the risk of disaster scenes increases, the number of cases of acquiring disaster scenes information using unmanned robots is increasing. Because unmanned robots can be remotely controlled, sensors such as LiDAR and optical cameras are installed so that investigators can safely observe disaster scenes and acquire information. In particular, the information that can be acquired is different depending on the sensor characteristics, and a sensor module suitable for the purpose is being developed. Accordingly, the National Disaster Management research Institute(NDMI) also developed a investigation robot capable of acquiring information on disaster sites independently. Based on 3D point cloud data, we developed a multi-sensor module and SLAM algorithm customized to the investigation robot to collect quantitative information on the damage situation. To test the performance of the independently developed multi-sensor module, SLAM mapping was performed in a disaster building reproduced like a disaster scene, and various SLAM algorithms and distance comparison were performed. As a result, PackSLAM developed in this study showed the lowest error. In the future, to increase applicability at disaster sites, more precise experiments will be conducted by conducted by establishing a rough terrain environment.
TECHNOLOGY ON HIGH-ACCURACY DEM EXTRACTION FROM AIRBORNE INTERFEROMETRIC SAR
Authors: Kang Q. , Zhang J. , Huang G. , Yang S. , Zhang Z.
Published: May 2022

Abstract. Remote sensing image mapping (aviation, aerospace, UAV) is the main form of current mapping. Optical image mapping has been a certain research foundation and played an important role in the mapping task. However, cloud, ice, snow, rain, and haze conditions seriously affect the interpretation and processing of optical images, making it difficult to meet the needs of rapid emergency response. Synthetic Aperture Radar (SAR) is currently the only method that can achieve all-day, all-weather rapid imaging under various weather conditions such as day, night, cloud, snow, rain, etc. With unique advantages unmatched by traditional optical remote sensing technology, it is an important technical guarantee for improving emergency response capabilities and promoting remote sensing and geographic information industries, and an earth observation technology that has been invested and vigorously developed internationally. Digital Elevation Model (DEM) plays an increasingly important role in the application of terrain information acquisition such as rapid acquisition of sudden disasters and surface deformation information acquisition. Rapid acquisition of large area high-accuracy DEM is one of the important research contents of SAR mapping. Based on interferometric SAR data combined with sensor orbit data, external DEM and other auxiliary data, DEM is finally generated through complex image registration, interference image generation, removal of flat ground effects, interference phase filtering, phase unwrapping, interference calibration and geocoding. The registration accuracy of multiple images greatly affects the accuracy of DEM. This paper proposes an improved registration method based on the conventional rough registration, pixel-level registration and sub-pixel registration. When the motion compensation of the sensor platform is unknown during imaging, it can meet the requirements of high-accuracy and large-scale rapid registration of dual-antenna polarization interferometric SAR. The experimental data is X-band airborne SAR data in Weinan region of Shaanxi Province, and DEM products with the elevation accuracy better than 0.5m is generated, which verifies the effectiveness, accuracy and fast processing ability of the method.
QUALITY ASSESSMENT AND ANALYSIS FOR THE ONE STATUS MAP (CORE AREA) OF HOUSING THEMATIC DATA
Authors: Yin S. , Li H. , Chen C. , Zhou J. , Yao B.
Published: May 2022

Abstract. A systematic solution to the one status map (core area) of Beijing - housing thematic data (hereinafter referred to as "the one status map (core area) of housing thematic data") aims to provide housing thematic data with full coverage, high precision, dynamic update and attribute integration, and has the characteristics of "accurate data, fast update, complete attributes and multiple classifications". This paper makes full use of relevant standards and specifications, combined with internal specifications related to data processing and quality evaluation. Based on the analysis of the characteristics and technical requirements of the data, this paper puts forward a quality evaluation system suitable for the data, including quality elements, quality measurement and quality evaluation, so as to provide technical guarantee for ensuring the quality of achievements.
COMPARISON OF CONVOLUTIONAL NEURAL NETWORKS FOR CLOUDY OPTICAL IMAGES RECONSTRUCTION FROM SINGLE OR MULTITEMPORAL JOINT SAR AND OPTICAL IMAGES
Authors: Cresson R. , Narçon N. , Gaetano R. , Dupuis A. , Tanguy Y. , May S. , Commandré B.
Published: May 2022

Abstract. With the increasing availability of optical and synthetic aperture radar (SAR) images thanks to the Sentinel constellation, and the explosion of deep learning, new methods have emerged in recent years to tackle the reconstruction of optical images that are impacted by clouds. In this paper, we focus on the evaluation of convolutional neural networks that use jointly SAR and optical images to retrieve the missing contents in one single polluted optical image. We propose a simple framework that ease the creation of datasets for the training of deep nets targeting optical image reconstruction, and for the validation of machine learning based or deterministic approaches. These methods are quite different in terms of input images constraints, and comparing them is a problematic task not addressed in the literature. We show how space partitioning data structures help to query samples in terms of cloud coverage, relative acquisition date, pixel validity and relative proximity between SAR and optical images. We generate several datasets to compare the reconstructed images from networks that use a single pair of SAR and optical image, versus networks that use multiple pairs, and a traditional deterministic approach performing interpolation in temporal domain.
DO WE STILL NEED IMAGENET PRE-TRAINING IN REMOTE SENSING SCENE CLASSIFICATION?
Authors: Risojević V. , Stojnić V.
Published: May 2022

Abstract. Due to the scarcity of labeled data, using supervised models pre-trained on ImageNet is a de facto standard in remote sensing scene classification. Recently, the availability of larger high resolution remote sensing (HRRS) image datasets and progress in self-supervised learning have brought up the questions of whether supervised ImageNet pre-training is still necessary for remote sensing scene classification and would supervised pre-training on HRRS image datasets or self-supervised pre-training on ImageNet achieve better results on target remote sensing scene classification tasks. To answer these questions, in this paper we both train models from scratch and fine-tune supervised and self-supervised ImageNet models on several HRRS image datasets. We also evaluate the transferability of learned representations to HRRS scene classification tasks and show that self-supervised pre-training outperforms the supervised one, while the performance of HRRS pre-training is similar to self-supervised pre-training or slightly lower. Finally, we propose using an ImageNet pre-trained model combined with a second round of pre-training using in-domain HRRS images, i.e. domain-adaptive pre-training. The experimental results show that domain-adaptive pre-training results in models that achieve state-of-the-art results on HRRS scene classification benchmarks. The source code and pre-trained models are available at https://github.com/risojevicv/RSSC-transfer.
DEEP LEARNING TRAINING WITH UNBALANCE SAMPLE DISTRIBUTION FOR REMOTE SENSING IMAGE SEGMENTATION
Authors: Xu Y. , Hu X. , Gong J. , Huang X. , Li J.
Published: May 2022

Abstract. The intelligent interpretation of remote sensing images based on deep learning has become a hot spot with the increasing satellite images acquired due to the rapid development of aerospace technology. Sufficient and reasonable distributed samples are essential for the accuracy of deep learning. The spatial distribution of natural features is inhomogeneous in the real world. When people create sample dataset, they often collect within a certain local range, which may bring problems of unbalanced distribution of samples, including the unbalance between training dataset and validation dataset, and the unbalance among different sample categories. This long-tail distribution of samples (i.e., a few classes account for most of the data, while most classes are under-represented) can lead to bias in the training model and make it difficult to ensure accuracy.In this paper we tried to solved the above-mentioned problem in landcover classification with high spatial and spectral resolution (HSSR) remote sensing images. We first adopted an iterative stratification method for multi-label data classification to ensure that both training dataset and validation dataset contain reasonable proportion of landcover classes. Then we proposed a weighted loss algorithm to further strengthen the learning ability of the model for rare categories. Experiments on a large volume HSSR dataset shows that with our methods the accuracy of landcover classification increased by 2%.
A NOVEL SAMPLE LABELLING CRITERION FOR PIN DEFECT DETECTION IN UAV IMAGE
Authors: Ying K. , Wang J.
Published: May 2022

Abstract. With the rapid development of UAV technology, defect detection based on UAV images has expanded from power components such as insulators and dampers to bolts and pins. Different from the defect detection of insulators or dampers, there are two main difficulties in pin defect detection: (1) It is very small for bolts and pins compared to the entire image, usually less than 1%, and there are not enough features for detection; (2) Only bolts on link fittings need to be fixed with pins, while bolts in other parts do not need, so it is difficult to judge whether there are pin defects on bolts only based on the absence of pins on the bolts. Aiming at the above problems, cascade object detection method is adopted for pin defect detection in this paper, and improves the detection accuracy by gradually narrowing ROI (region of interest). The main contribution of this paper is to formulate a novel sample labelling criterion for cascade pin defect detection method. Building a sample set according to this criterion can not only greatly reduce the workload, but also improve the object detection accuracy. In this paper, YOLOv4 is used to validate the proposed method. The result shows that compared with the existing sample set building methods, the proposed sample labelling criterion improves the accuracy from 85.2% to 92% and recall from 85.7% to 94.2%.
THE STUDY ON THE SEMI-EMPIRICAL SOIL MODELS FOR IMPROVING THE GNSS-R SM RETRIEVAL
Authors: Jia Y. , Chen H. , Wu K. , Zou J. , Liu Q.
Published: May 2022

Abstract. Soil moisture (SM) is one of the important physical properties of soil and plays an important role in agricultural production. Global Navigation Satellite System Reflectometry (GNSS-R) SM retrieval is based on the concept of receiving GPS signals reflected by the ground using a passive receiver. Although many research has been reported showing the capability of the GNSS-R technique on SM application. Due to the diversity of soil types and the complexity of soil composition, the study of soil permittivity is still attracting many interesting. This paper presented the investigations on characteristic and performances for different semi-empirical soil models, to reveal and verify the relationship between dielectric constant and SM under different realistic soil compositions. This study is helpful to improve the accuracy for the GNSS-R SM retrieval and can be used to other SM related studies.
MULTI-TEMPORAL DATA AUGMENTATION FOR HIGH FREQUENCY SATELLITE IMAGERY: A CASE STUDY IN SENTINEL-1 AND SENTINEL-2 BUILDING AND ROAD SEGMENTATION
Authors: Ayala C. , Aranda C. , Galar M.
Published: May 2022

Abstract. Semantic segmentation of remote sensing images has many practical applications such as urban planning or disaster assessment. Deep learning-based approaches have shown their usefulness in automatically segmenting large remote sensing images, helping to automatize these tasks. However, deep learning models require large amounts of labeled data to generalize well to unseen scenarios. The generation of global-scale remote sensing datasets with high intraclass variability presents a major challenge. For this reason, data augmentation techniques have been widely applied to artificially increase the size of the datasets. Among them, photometric data augmentation techniques such as random brightness, contrast, saturation, and hue have been traditionally applied aiming at improving the generalization against color spectrum variations, but they can have a negative effect on the model due to their synthetic nature. To solve this issue, sensors with high revisit times such as Sentinel-1 and Sentinel-2 can be exploited to realistically augment the dataset. Accordingly, this paper sets out a novel realistic multi-temporal color data augmentation technique. The proposed methodology has been evaluated in the building and road semantic segmentation tasks, considering a dataset composed of 38 Spanish cities. As a result, the experimental study shows the usefulness of the proposed multi-temporal data augmentation technique, which can be further improved with traditional photometric transformations.
INFRASTRUCTURE MONITORING USING THE INTERFEROMETRIC SYNTHETIC APERTURE RADAR (INSAR) TECHNIQUE
Authors: Gao Q. , Crosetto M. , Monserrat O. , Palama R. , Barra A.
Published: May 2022

Abstract. This paper focuses on the application of spaceborne radar interferometry as a tool for assisting the monitoring different types of infrastructures, including railways and highways. The Persistent Scatterer Interferometry (PSI) technique, the most advanced class of differential interferometric Synthetic Aperture Radar techniques (DInSAR), is used to generate the deformation maps, including the time series and the displacement velocity for each measured persistent scatterers. The dataset considered in this work consists of 261 SAR IW-SLC images acquired by the Sentinel-1 A/B satellites, between January 2016 and September 2021, over the metropolitan area of Barcelona. The infrastructures, especially railways and highways, not only cross the cities, but also connect them crossing non-urban areas. One main technical issue of monitoring infrastructures is the low density of persistent scatterers (PS) in rural areas. To improve the density, the processing chain combines the interferograms selection based on the coherence threshold. Also, for a better point selection, the Equivalent Spatial Coherence (Omega Factor) is tested.
SURFACE SUBSIDENCE MONITORING IN GANZHOU AREA BASED ON SBAS-INSAR
Authors: Li X. , Zhou L. , Ma J. , Zhu Z. , Li X. , Huang L.
Published: May 2022

Abstract. In this paper, we first obtained the temporal-spatial changes of surface subsidence in Ganzhou area based on SBAS-InSAR technology using 24 scenes Sentine-1A images covering the Ganzhou area from 2018 to 2020. Then we studied the surface subsidence characteristics in the study area. Finally, the relationship between surface subsidence and factors such as precipitation, human engineering activities, groundwater extraction, and sediment accumulation were analyzed in Ganzhou area. The results showed that : 80% of the sedimentation rate was −5.4–3.0 mm/a in the study area; serious subsidence areas were mainly located in Ganxian District, which annual average subsidence rate reached −26.4mm/a, and the time series of surface subsidence was accompanied by certain seasonal changes; the seasonal changes in surface subsidence were related to rainfall and groundwater extraction, in addition, the surface subsidence was obviously affected by human engineering activities and river sediment accumulation.
INSAR DEFORMATION TIME SERIES CLASSIFICATION USING A CONVOLUTIONAL NEURAL NETWORK
Authors: Mirmazloumi S. M. , Gambin Á. F. , Wassie Y. , Barra A. , Palamà R. , Crosetto M. , Monserrat O. , Crippa B.
Published: May 2022

Abstract. Temporal analysis of deformations Time Series (TS) provides detailed information of various natural and humanmade displacements. Interferometric Synthetic Aperture Radar (InSAR) generates millimetre-scale products, indicating the chronicle behaviour of detected targets via TS products. Deep Learning (DL) can handle a massive load of InSAR TS to categorize significant movements from non-moving targets. To this end, we employed a supervised Convolutional Neural Network (CNN) model to distinguish five deformations trends, including Stable, Linear, Quadratic, Bilinear, and Phase Unwrapping Error (PUE). Considering several arguments in a CNN model, we trained numerous combinations to explore the most accurate combination from 5000 samples extracted from a Persistent Scatterer Interferometry (PSI) technique and Sentinel-1 images over the Granada region, Spain. The model overall accuracy exceeds 92%. Deformations of three cases of landslides were also detected over the same area, including the Cortijo de Lorenzo, El Arrecife, and Rules Viaduct areas.
ASSESSING THE EFFECT OF ALOS-2 DATA UTILIZATION ON THE ACCURACY OF ESTIMATION VERTICAL AND HORIZONTAL DEFORMATION COMPONENTS IN THE AREA OF HARD COAL MINING EXPLOITATION IN POLAND BY USING DIFFERENTIAL SYNTHETIC APERTURE RADAR INTERFEROMETRY
Authors: Pawluszek-Filipiak K. , Wielgocka N. , Tondaś D. , Borkowski A.
Published: May 2022

Abstract. Mining-induced deformations are characterized by high deformation rates. These deformations consist of both vertical and horizontal components, which should not be neglected during the monitoring of such areas. Unfortunately, Differential Satellite Synthetic Aperture Radar Interferometry (DInSAR) can only measure deformations in the line of sight (LOS) direction towards the satellite. Combining data from ascending and descending geometry allows reconstruction of the vertical and east-west deformation components. Theoretically, at least information from one ascending and one descending geometry is enough to reconstruct the vertical and east-west deformation components; however, for the area of interest three various Sentinel-1 datasets (one ascending and two descending) are available and have been used in the study. Furthermore, considering the availability of other SAR missions with slightly different geometries, the objective of this study was to evaluate the effect of ALOS-2 use on the accuracy of estimating the east-west and vertical cumulative deformation component estimated for a time span of approximately one year. Unfortunately, we have access to only five ALOS-2 scenes within the area of interest. However, the comparison of the decomposed results with GNSS measurements shows that the application of additional ALOS-2 data, even five scenes, allows the root mean square error (RMSE) to be decreased for the vertical and horizontal deformation component from 0.038m to 0.032m and from 0.031m to 0.018m, respectively. This means that the application of ALOS-2 data has a positive value on the accuracy of decomposed vertical and horizontal deformation components.
EVALUATION OF PIXEL SELECTION METHODS FOR TRAFFIC INFRASTRUCTURE MONITORING USING SENTINEL-1 INSAR
Authors: Piter A. , Haghighi M. H. , Motagh M.
Published: May 2022

Abstract. The Synthetic Aperture Radar (SAR) satellite Sentinel-1 provides excellent traffic infrastructure monitoring capabilities due to its short revisit time, wide-scale coverage and free of charge data policy. However, pixels from Sentinel-1 have a medium spatial resolution so that traffic infrastructure is only covered by a few pixels. Moreover, Interferometric Synthetic Aperture Radar (InSAR) yields deformation time series for coherent pixels only, thus limiting the number of pixels reporting the ground motion at traffic infrastructure. Although various InSAR time series methods have been successfully applied for traffic infrastructure monitoring, the selection of appropriate methods achieving a high pixel density has not yet been evaluated. In this study, we test whether we can improve the monitoring capabilities by combining different InSAR time series methods. On the one hand, we applied widely used Stanford Method for Persistent Scatterers (StaMPS) as a baseline method to retrieve the deformation time series. On the other hand, we enhanced the phase quality in a pre-processing step using the Phase-Linking (PL) approach and afterwards estimated the deformation time series also with StaMPS based on pixels selected by PL. We compared and evaluated the achieved pixel densities from both methods at main roads, highways and railways in a study area in Germany. We found that the InSAR time series methods selected complementary sets of pixels at all traffic infrastructure types. Moreover, the results indicate a great potential for railway monitoring using Sentinel-1 InSAR due to both high pixel density and homogeneous spatial distribution of pixels. Interestingly, coherent scatterers selected by StaMPS were observed to coincide with large traffic signs at highways which show a double-bounce scattering in the amplitude images. This study shows the benefits of combining PL and StaMPS for increased pixel density at traffic infrastructure and confirms Sentinel-1 data as a suitable data source for traffic infrastructure monitoring.
EVALUATION OF ZY1-02D HYPERSPECTRAL SATELLITE SURFACE REFLECTANCE PRODUCTS
Authors: Liu S. , Guo L. , Xue B. , Wang X. , Zhang H. , Zhang H.
Published: May 2022

Abstract. ZY-1 02D satellite is an important satellite in China space infrastructure planning. The Land Satellite Remote Sensing Application Center of the Ministry of Natural Resources of the People’s Republic of China (LASAC) undertook the construction of the satellite hyperspectral data processing system and produced surface reflectance products. Reflectance is the basis of hyperspectral remote sensing data application, which is directly related to the efficiency and quality of hyperspectral application. At present, there is no comprehensive and in-depth study on the accuracy evaluation of the atmospheric correction reflectance of ZY1 - 02D satellite hyperspectral data. In view of this problem, Accuracy Verification of LASAC products using accuracy, precision, uncertainty, spectral curve correlation and spectral angle chord four indicators based on surface reflectance produced by AERONET ground measured data. The results show that the average values of accuracy, precision, uncertainty of LASAC products are 3.2 %, 2.02 % and 4.13 %, respectively. The spectral curve correlation and spectral chord angle are 0.998 and 0.979, respectively. The research result can provide technical reference for the processing and application of ZY1 - 02D hyperspectral image surface reflectance products.
EVALUATION OF FEATURE SELECTION METHODS FOR VEGETATION MAPPING USING MULTITEMPORAL SENTINEL IMAGERY
Authors: Dobrinić D. , Gašparović M. , Medak D.
Published: May 2022

Abstract. With the recent advances in remote sensing technologies for Earth observation (EO), many different remote sensors (e.g., optical, radar) collect data with distinctive properties. EO data have been employed to monitor croplands and forested areas, oceans and seas, urban settlements, and natural hazards. The spectral, spatial, and temporal resolutions of remote sensors have been continuously improving, making geospatial monitoring more accurate and comprehensive than ever before. To tackle this issue, various variable selection methods (e.g., filter, wrapper, and embedded methods) are nowadays used to reduce data complexity, and hence improve classification accuracy. Therefore, the goal of this research was twofold. Firstly, to assess the performance of the random forest (RF) classifier in a large heterogeneous landscape with diverse land-cover categories using multi-seasonal Sentinel imagery (i.e., Sentinel-1; S1 and Sentinel-2; S2) and ancillary data. Secondly, to compare RF variable selection methods to identify a subset of predictor variables that will be included in a final, simpler model. Using mean decrease accuracy (MDA) as a feature selection (FS) method, an original dataset was reduced from 114 to 34 input features, and its classification performance outperformed all-feature (114 features) and band-only (36 features) model with an OA of 90.91%. The most pertinent input features for vegetation mapping were S2 spectral bands (14 features), followed by the spectral indices derived from S2, texture features, and S1 bands. This research improved vegetation mapping by integrating radar and optical imagery, especially after applying FS methods which removed redundant and noisy features from the original dataset. Future research should address additional feature selection methods (i.e., filter, wrapper, or the embedded) for vegetation mapping, combined with advanced deep learning methods.
MULTI-SPECTRAL EDGE DETECTION FOR ENHANCED EXTRACTION AND CLASSIFICATION OF HOMOGENEOUS REGIONS IN REMOTELY SENSED IMAGES
Authors: Braitbart M. , Almog O. , Shoshany M.
Published: May 2022

Abstract. Mediterranean environments are characterized by high spatial and temporal heterogeneity due to their climatological, lithological, soil and vegetation geo-diversity and their high population density which cause growing land-use transformations at the rural-urban fringe. Remote sensing mapping and monitoring land cover in these environments under such conditions is a challenging task. Instead of the common per pixel approach we suggest combining application of an object-oriented classification based on image objects separation through edge detection with unsupervised classification. The main elements of our methodology are: (1) separating image areas into vegetation/ non-vegetation regions utilizing NDVI threshold; (2) calculation of the spatial variance at different bands; (3) image objects extraction through enhancement of the differences between edge pixels and regions of homogeneity; (4) per-object classification for the homogenous areas; (5) overlaying large unclassified image areas by the results of ISODATA (Iterative Self-Organizing Data Analysis) unsupervised classification. Our methodology was applied on multi-spectral images acquired by the VENμS remote sensing system. The study area consists of a typical rural area in semi-arid climate regions undergoing increasing urbanization. Six test areas were selected representing different spatial combinations of natural/ planted forests, agriculture and built-up land-use/ land cover types. While bare fields were poorly classified, areas of low vegetation cover were classified with producer/user accuracies below 60%, built-up areas and roads, cultivated areas, shrublands and bata (dwarf-shrubs) and rocky areas gained good producer/ user classification accuracies.
CRITICAL REFLECTION ON QUANTITATIVE ASSESSMENT OF IMAGE FUSION QUALITY
Authors: Xu S. , Ehlers M.
Published: May 2022

Abstract. Image fusion technique has been extended its development from multi-sensor fusion, multi-model fusion to multi-focus fusion. More and more advanced techniques such as deep learning have been integrated into the development of image fusion algorithms. However, as an important aspect, fusion quality assessment has been received less attention. This paper intends to reflect on the commonly used indices for quantitative assessment and investigate how they can represent the fusion quality regarding spectral preservation and spatial improvement. We found that image dissimilarities are unavoidable due to the spectral coverage of different image sensors. Image fusion should integrate these dissimilarities when they are representing spatial improvement. Such integration will naturally change the pixel values. However, as the quality indices for the assessment of spectral preservation are measuring image dissimilarities, the integration of spatial information will lead to a low fusion quality assessment. For the evaluation of spatial improvement, the quality indices only work if the spatial details have been lost; however, in the case of spatial details gain, these indices do not reflect them as spatial improvements. Moreover, this paper raises attention to image processing procedures involved in image fusion, including image geo-registration, image clipping and image resampling, which will change image statistics and thereby influence the quality assessment when statistical indices are used.
KEY RESEARCH AND APPLICATION OF THE REAL-TIME SERVICE OF REMOTE SENSING BIG DATA
Authors: Zheng X. , Cheng T. , Li Z. , Chen C. , Zhang W. , Liu J. , Xing X. , Huang W. , Zha Z. , Zhang H. , Tang D. , Wang C. , Li H. , Ding L. , Wang Z. , Gao X.
Published: May 2022

Abstract. With the rapid development of earth observation technology, the remote sensing data gradually becomes the significant data resources for spatiotemporal data analysis. However, to realize the efficient management and quick real-time service of the multi-source, multi-scale and multi-spectral is the key problem for the data management units. This paper brings up a novel technology system and resolution for online and real-time service of the massive remote sensing images by innovatively developing a data management model based on an extensible discrete global grid, inventing the grid-based search and dispatch method for remote sensing big data, firstly building a parameter-driven dynamic map service mode for multi -source, multi-scale and multi-spectral remote sensing big data. This method has greatly improved the efficiency for data management units, such as the Chinese government, corporations etc. and has produced a lot of social and economic benefits.
USE OF TREE HEIGHT FOR MANGROVE SPECIES CLASSIFICATION
Authors: Roy L. , Marchand C. , Landes T. , Macher H. , Grussenmeyer P.
Published: May 2022

Abstract. Mangrove forests play an important role in the balance of biodiversity. However, they are threatened by agriculture, aquaculture, urbanization and global warming. That’s why it is imperative to monitor this ecosystem and understand how it evolves in the face of these threats in order to better preserve it. The traditional methods are invasive and time consuming. Besides, it is often difficult to get into mangroves because of the particular structure of some species, so measurements cannot be taken in those areas. That’s why it is very interesting to use aerial data provided by unmanned aerial vehicles (UAVs) photos or airborne laser scanning systems (ALS). Moreover, some representative elements of mangroves are only a few tens of centimeters high. This is the case of pneumatophores. Traditional measurements would be much too long. In this case, it is interesting to use terrestrial laser scanning systems (TLS) to make measurements and to follow them. A research project began in 2021 to try to understand how urban mangroves develop in semi-arid regions, using remote sensing techniques (photogrammetry, airborne and terrestrial laser scanning). The purpose of this paper is first to present the project and the issues of monitoring mangrove forests. Then, it proposes a state of the art of the methodologies used to record mangrove. Finally, it presents the different acquisitions made as well as the first results of species classification based on photogrammetric point cloud processing. The assessment based on ground truth shows already promising results.
AN ANALYSIS OF THE INFLUENCE OF THE NUMBER OF OBSERVATIONS IN A RANDOM FOREST TIME SERIES CLASSIFICATION TO MAP THE FOREST AND DEFORESTATION IN THE BRAZILIAN AMAZON
Authors: Vieira L. S. , Queiroz G. R. , Shiguemori E. H.
Published: May 2022

Abstract. Remote sensing has been an essential tool in combating deforestation. However, the ever-rising deforestation rates require new remote sensing techniques. This paper presents a study to determine the effects on the accuracy of the data analysis of varying the number of satellite observations, using a Random Forest classification algorithm. We carried out experiments on the Landsat-8 data cube with 22 images and developed an automatic sampling system based on PRODES to generate the labeled time series. We split the time series dataset to build data subsets with different number of observations. The results showed that a fewer number of observations negatively effects the accuracy of the RF algorithm when analyzing deforested areas, but not forest areas. The RF classifiers were compared using a random test data set, where all classifiers presented an Overall Accuracy (OA), Balanced Accuracy (BA), and f1-score (F1) above 97%. In the first evaluation, the variation in the number of observations appears to cause little influence on the classification accuracy. The analysis used the reference map to contrast the RF classifier’s results. The results showed that the best results in OA occurred with fewer observations. The best performance of 96% happened with four observations. We evaluated the performance of the classes, deforestation, and forest individually. The results showed that a fewer number of observations had negative effects on the accuracy of the RF algorithm when analyzing deforested areas, but not forest areas. Finally, we evaluated the visual quality of the land cover maps produced.
TOWARD A LOW-COST, MULTISPECTRAL, HIGH ACCURACY MAPPING SYSTEM FOR VINEYARD INSPECTION
Authors: Beniaouf S. , Mabillard R. , Gressin A.
Published: May 2022

Abstract. Confronted with the climate change challenge and the territorial constraints, agriculture has to modernize itself. The use of georeferenced data and remote-sensing imagery is a major step in this direction. This precision mapping of crops requires powerful and accurate acquisition systems, while remaining financially attractive. The development of multispectral sensors and low-cost GNSS makes it possible to consider systems that will be able to map at the plant scale. However, these positioning systems do not yet guarantee a precise overlap of data acquired at different times. Thus, we propose in this paper a method to register terrestrial image data, acquired on vineyard plots. Our method seeks to avoid image registration problems, such as illumination changes, by detecting the vine stocks, reconstructing them in 3D, and registering them individually. The 3D detection method is based on an image-based object detection method (Faster R-CNN) and a structure-from-motion reconstruction of object-masked images. The results that we obtained on a vineyard plot, allowed us to validate the method, with a precision of less than 10 cm, making it possible to map the vine by stock.
TEMPORAL AND SPATIAL ANALYSIS OF CLASSIFICATION TREE FOR IMPERVIOUS SURFACE MAPPING FROM SENTINEL-2 MSI DATA
Authors: Gao J. , Chen Y. , Guo Y. , Yang S.
Published: May 2022

Abstract. For studies of urban development, it is an important method for obtaining the distribution of impervious surface (IS) areas and their dynamic change from remote sensing data. The dilemma of the same spectrum for different features and different spectrums for the same features, posed by the complexity of the IS objects, is the fundamental obstacle encountered in the extraction of urban IS areas. In this study, an automatic extraction method for urban IS areas is proposed and analyzed, based on classification and regression tree (CART) and ensemble learning strategies. The Sentinel-2 MSI data of 30 cities in China from 2018 to 2020 were selected for IS extraction experiments. We perform temporal and spatial modeling of the splitting threshold offset in the classification model to explore the effect of time and space on IS extraction. The obtained offset models show that the temporal variation is not significant, while the spatial offsets have more obvious linear relationships.
DETECTING AND EVALUATING DISTURBANCE IN TEMPERATE RAINFOREST WITH SENTINEL-2, MACHINE LEARNING AND FOREST PARAMETERS
Authors: Kutchartt E. , Hernández J. , Corvalán P. , Promis Á. , Pirotti F.
Published: May 2022

Abstract. Earth observation via remote sensing imagery provides a fast way to define alteration levels. In this work 12 stands of Araucaria-Nothofagus forests were selected in southern Chile, which represented four alteration levels: (i) None (ii) Low (iii) Medium and (iv) High. The stands were surveyed measuring 379 field plots and Google Earth Engine was used to collect a composite of Sentinel-2 images over a one-year range, from June 2019 to June 2020. The following approaches were tested: (i) aggregating the normalized difference vegetation index (NDVI) of each image and selecting the 95th and 99th percentile values of NDVI for each pixel; (ii) creating a composite imagery with best pixels over one year timeline using NDVI as weighting factor and NDVI value band itself (NDVI) – this is similar to the 99th percentile in the previous point, but with maximum values of NDVI; (iii) aggregating the composite as in the previous approach, but using the full spectral information of Sentinel-2 and then random forest machine learning for classification over alteration areas with k-fold validation with k=5. Results show that the 95th and 99th percentile of NDVI values from approach (i) do not discriminate the four classes correctly. The maximum NDVI from approach can distinguish all four classes. It must be noted through that statistical significance does not necessarily imply a strong practical significance; medium and high alterations have very similar NDVI distributions. Random forest results provided an F-score for each class higher than 80% except for the “medium alteration” class.
EXPLORING THE HARMONIZED LANDSAT SENTINEL (HLS) DATACUBE TO MAP AN AGRICULTURAL LANDSCAPE IN THE BRAZILIAN SAVANNA
Authors: Parreiras T. C. , Bolfe E. L. , Sano E. S. , Victoria D. C. , Sanches I. D. , Vicente L. E.
Published: May 2022

Abstract. Brazil has established itself as one of the world leaders in food production. Different types of remote sensing mapping techniques have been undertaken to support rural planning in the country. However, due to the complex dynamics of Brazilian agriculture, especially in the Cerrado biome (tropical savanna), there is a need for more feasible crop discrimination and monitoring initiatives, which require a consistent time series of remote sensing data at medium meter and potentially up to 3 day Landsat 8 and Sentinel-2 satellite time series, minimizing the cloud cover limitations for rainfed agricultural monitoring. This paper aims to explore the potential of the Harmonized Landsat 8 Sentinel-2 (HLS) data cube to map agricultural landscapes in the Brazilian Cerrado. The HLS multispectral bands from 27 scenes with less than 10% cloud cover, from October 2020 to September 2021, encompassing one entire crop growing season, were processed by the Random Forest algorithm to produce a map with four land use/cover classes (annual crops, sugarcane, renovated sugarcane fields, cultivated pastures, and native Cerrado). We performed accuracy assessment through 10-fold cross-validation and confusion matrix analyses. The results showed a high level of overall accuracy and Kappa coefficient, both with 99%, as well as high user's and producer's accuracies of at least 99%. The HLS dataset has been continuously improved, showing very promising results for rainfed agricultural mapping and monitoring.
IMPROVING THE BINARY CLASSIFICATION OF PEAT LOCALITIES FROM MULTI-SOURCE REMOTELY-SENSED DATA USING CNN
Authors: Pittman R. , Hu B.
Published: May 2022

Abstract. Neural networks were explored to achieve a binary classification for determining land corresponding to peat for a study area in the boreal forest of northern Ontario, Canada. Environmental covariates were employed as predictors and obtained from multiple sources, which included multispectral imagery, LiDAR, SAR, and aeromagnetic data. A dense neural network (DNN), as well as a convolutional neural network (CNN), were each implemented. Logistic regression, support vector machine (SVM) and random forest (RF) approaches were also modelled. Neighboring pixels surrounding the soil sampling sites were incorporated as input into the CNN, that permitted training on additional information that was not exploited by other methods. Preliminary results indicate that a CNN can attain improved accuracies for peat classification, when compared against other approaches.
Assessment of Refractive Status of 5-15 Years Old Children Attending Government Schools of Rural Agra
Authors: Yadav Himanshu Kumar , Sen Snigdha , Gupta Preeti , Agrawal Renu , Singh Niranjan
Published: May 2022

Introduction: In children uncorrected refractive errors have a profound effect on educational and psychosocial development hence it is necessary to estimate the prevalence both at the community and at the school level to aid planning and implementation of refractive error services in children. Objective: To determine the refractive status of 5 to 15 years old children attending government schools of rural areas of district Agra, Uttar Pradesh (UP), India. Method: Study conducted on 902 students of age group 5-15 years of randomly selected government schools of Bichpuri Block of district Agra. Children underwent visual acuity assessment and torch light examination, height and weight measurement. Children with VA ≤6/9 were further examined and cycloplegic retinoscopy, fundus examination, slit lamp examination and post mydriatic refraction was done. On the basis of values of cycloplegic refraction and post mydriatic refraction, refractive error was classified as myopia, hypermetropia and astigmatism. Statistical Analysis was done by applying Chi square test. Result: Out of 902 children, 125 children (13.86 %) were having refractive error of which 76 were myopic (8.43%), 39 were astigmatic (4.32%) and 10 were hypermetropic (1.11%). There was an increase in the overall prevalence of refractive error with advancing age. There was no significant association of refractive error with gender and nutritional status. Conclusion: Vision screening of school children is very useful for early detection and correction of refractive errors. Screening of the refractive errors in school should be carried out periodically and regularly.
Self-Medication Practices and Health Seeking Behaviour among Residents of Selected Villages in A Block of West Bengal: A Mixed-Methods Study
Authors: Shukla Vineeta , Ray Kuntala , Dutta Sinjita , Basu Mausumi
Published: May 2022

Introduction: Self-medication can lead to problems such as adverse effects and antibiotic resistance. This study was conducted to estimate the proportion of self-medication practice, to elicit the reasons for practicing self-medication, to find the factors associated with self-medication practice and to assess the health seeking behaviour among a rural community of West Bengal. Method: A study with mixed-methods approach was conducted among 212 households from four selected villages in a block of West Bengal. Focussed Group Discussions were held with respondents who were practising self-medication. Data were analysed using SPSS 25.0 and Atlas ti 7.0. Multivariable logistic regression was performed to find the factors associated with self-medication practice. Results: The proportion of self-medication practice was found to be 41%. Allopathy was most commonly preferred system of Medicine (78.8%). Statistically significant factors associated with self-medication practice were education upto Middle school (aOR 3.59) and Secondary level (aOR 10.71), Class III socio-economic status (aOR 5.03) and presence of acute illness (aOR 28.92). Conclusion: Proportion of self-medication practice among rural population was high. This needs to be addressed and health education should be provided to them.
Socio-cultural characteristics of people and the shape of transit-oriented development (TOD) in Indonesia: A mobility culture perspective
Authors: Hasibuan Hayati Sari , Permana Chrisna T
Published: May 2022

Recent literature argues that many transit-oriented development (TOD) projects have failed because their approaches focus on “one-size-fits-all” technical provisions and/or pay little attention to local socio-cultural suitability. Through a sociological institutionalism lens, this article examines how the local socio-cultural characteristics of people reshape mobility culture in transit areas and lead to the potential emergence of locally based TOD concepts. Our discussions are guided by the socio-cultural mobility analysis framework, an extended version of the original mobility culture theory. This analytical framework divides mobility culture into land use, housing, and transport dimensions. Five TOD potential areas located in the periphery of Greater Jakarta, Indonesia, are presented as a research window in which data and information are collected through a mixture of primary surveys and documentary reviews. This article reveals that housing preference emerges as the most important aspect of reshaping mobility culture in transit areas in Indonesia.
¿Vercingetórix en la serie de L. Hostilio Saserna (RRC 448)?
Authors: Amela Valverde Luis
Published: May 2022

Es habitual comprobar que en diversos estudios e investigaciones y casas de subastas de monedas se aluda a la representación del jefe arverno Vercingétorix enun denario romano acuñado por L. Hostilio Saserna. En este trabajo se estudia esta serie monetal y su relación con la conquista de las Galias efectuada por C. Julio César.
La real provisión en época moderna (Perú, siglos XVI-XVII): caracterización de un tipo diplomático
Authors: Ramírez Barrios Julio Alberto
Published: May 2022

La Real Provisión fue uno de los principales tipos diplomáticos expedidos por las cancille-rías regias, vinculada a la persona del monarca por elementos tan distintivos como la intitulación completa, solemne y mayestática, y el sello con sus armas reales. Esta vinculación hizo que la Real Provisión alcanzara una gran significación en Indias como un instrumento de poder utilizado por las autoridades representativas del monarca para hacer cumplir sus decisiones y dotarlas de mayor autoridad. El objetivo de este estudio es avanzar en el conocimiento de esta tipología diplomática a partir del análisis de las Reales Provisiones expedidas en Perú (siglos XVI y XVII) por las autoridades capacitadas para su expedición (Real Audiencia, Tribunal de Cuentas y virreyes). Para ello, en primer lugar, se reflexionará sobre el concepto “tipo diplomático” y sus elementos definitorios, a fin de calificar con precisión los documentos objeto de estudio. Una vez establecido qué entendemos por Real Provisión, se darán unas notas sobre su origen en la Castilla bajomedieval y de su proceso de elaboración en el distrito de la Audiencia de Lima. Por último, se abordará el análisis de las formas materiales y documentales que caracterizaron a este tipo diplomático.
Analysis of the competitiveness situation at the international and regional levels
Authors: MIKAYELYAN TIGRAN
Published: May 2022

International competitiveness is a key precondition for ensuring the economic
development of countries in the modern world. An in-depth analysis of the economic
situation in competitive countries provides an opportunity to assess and understand the
factors and conditions that have created a "healthy" economic environment in those
countries, ensured a high standard of living and quality of life. On the other hand, a
comparative analysis of Global Competitiveness Index of Armenia with regional countries
is of key importance. The paper addresses the important issues of increasing the
competitiveness of the Armenian economy in the current situation.