bluuebunny/crossref_metadata_2025 huggingface dataset:
AUTOMATIC TRAINING DATA GENERATION IN DEEP LEARNING-AIDED SEMANTIC SEGMENTATION OF HERITAGE BUILDINGS
Authors: Murtiyoso A. , Matrone F. , Martini M. , Lingua A. , Grussenmeyer P. , Pierdicca R.
Published: May 2022

Abstract. In the geomatics domain the use of deep learning, a subset of machine learning, is becoming more and more widespread. In this context, the 3D semantic segmentation of heritage point clouds presents an interesting and promising approach for modelling automation, in light of the heterogeneous nature of historical building styles and features. However, this heterogeneity also presents an obstacle in terms of generating the training data for use in deep learning, hitherto performed largely manually. The current generally low availability of labelled data also presents a motivation to aid the process of training data generation. In this paper, we propose the use of approaches based on geometric rules to automate to a certain degree this task. One object class will be discussed in this paper, namely the pillars class. Results show that the approach managed to extract pillars with satisfactory quality (98.5% of correctly detected pillars with the proposed algorithm). Tests were also performed to use the outputs in a deep learning segmentation setting, with a favourable outcome in terms of reducing the overall labelling time (−66.5%). Certain particularities were nevertheless observed, which also influence the result of the deep learning segmentation.
DEEP LEARNING FOR SEMANTIC SEGMENTATION OF CORAL IMAGES IN UNDERWATER PHOTOGRAMMETRY
Authors: Zhang H. , Gruen A. , Li M.
Published: May 2022

Abstract. Regular monitoring activities are important for assessing the influence of unfavourable factors on corals and tracking subsequent recovery or decline. Deep learning-based underwater photogrammetry provides a comprehensive solution for automatic large-scale and precise monitoring. It can quickly acquire a large range of underwater coral reef images, and extract information from these coral images through advanced image processing technology and deep learning methods. This procedure has three major components: (a) Generation of 3D models, (b) understanding of relevant corals in the images, and (c) tracking of those models over time and spatial change analysis. This paper focusses on issue (b), it applies five state-of-the-art neural networks to the semantic segmentation of coral images, compares their performance, and proposes a new coral semantic segmentation method. Finally, in order to quantitatively evaluate the performance of neural networks for semantic segmentation in these experiments, this paper uses mean class-wise Intersection over Union (mIoU), the most commonly used accuracy measure in semantic segmentation, as the standard metric. Meanwhile, considering that the coral boundary is very irregular and the evaluation index of IoU is not accurate enough, a new segmentation evaluation index based on boundary quality, Boundary IoU, is also used to evaluate the segmentation effect. The proposed trained network can accurately distinguish living from dead corals, which could reflect the health of the corals in the area of interest. The classification results show that we achieve state-of-the-art performance compared to other methods tested on the dataset provided in this paper on underwater coral images.
VEGETATION COVER MAPPING FROM RGB WEBCAM TIME SERIES FOR LAND SURFACE EMISSIVITY RETRIEVAL IN HIGH MOUNTAIN AREAS
Authors: Hiebl B. , Mayr A. , Kollert A. , Rutzinger M. , Bremer M. , Helm N. , Chytry K.
Published: May 2022

Abstract. Land Surface Temperature (LST) products from thermal infrared imaging rely on information about the spatial distribution of Land Surface Emissivity (LSE). For portable, broadband thermal cameras for drone- or ground-based measurements with camera to object distances up to a few kilometres and with meter-scale resolution, threshold-based retrieval of LSE from Fractional green Vegetation Cover (FVC) can be used. As seasonal changes in vegetation LSE over the year cannot be accounted for by single satellite images or aerial orthophotos, this study evaluates an approach for FVC retrieval via permanently installed RGB webcams and derived Excess Green vegetation index (ExG) time series at a high-mountain test site in the European Alps. Daily ExG values were derived from the imagery of 27 days between 12/07/2021 and 30/10/2021 and projected to a 0.5 m Digital Surface Model (DSM). FVC reference data from 765 in-situ vegetation plots were used to assess the relationship between ExG and the vegetation cover and to determine the thresholds of ExG for no vegetation cover and full vegetation cover. Despite the bad correlation between ExG and in-field FVC with an R² score of 0.15, an approach using a well-tested orthophoto-retrieved NDVI for FVC retrieval performs just slightly better. The comparison of the remotely sensed data and the field measurements therefore remains complex. Time series analysis of both ExG and FVC for highly vegetated areas showed a significant decrease from summer to autumn, which reflects the seasonal changes of LSE for senescent vegetation. Calculated emissivities for vegetated pixels ranged from the minimum of 0.95 to the maximum of 0.985 over the season, while emissivity values for less vegetated pixels stayed constant during the season. The results of this study will be used as input to a correction model for remote LST measurements in the context of micro-scale investigations of the thermal niche of Alpine flora.
AIRBORNE LASER SCANNING CHANGE DETECTION FOR QUANTIFYING GEOMORPHOLOGICAL PROCESSES IN HIGH MOUNTAIN REGIONS
Authors: Müller L. , Rutzinger M. , Mayr A. , Kollert A.
Published: May 2022

Abstract. Disturbance by geomorphic processes is a key factor for current and potential plant species distributions in high mountain regions. We implemented and tested an approach for the detection, quantification, and geomorphological classification of 3D topographic change using airborne laser scanning (ALS) data. This approach is applied to two point cloud epochs (from 2006 and 2017) to identify and analyse changes related to five different process categories in a study area in the European Alps: Fluvial Processes, Rock Glacier Movement & Landslides, Channel Erosion & Debris Flow, Rockfall Release & Deposition and Vegetation & Anthropogenic Change. The results are assessed through comparison with a manually produced geomorphological map. The analysis covering the eleven-year time period provides detailed information on the magnitudes and spatial distribution of change per identified geomorphological process. The study shows that the workflow is capable of providing the fundamental basis for mapping vegetation disturbance by geomorphic activity in a high alpine site.
CONTRIBUTION OF SUPER RESOLUTION TO 3D RECONSTRUCTION FROM PAIRS OF SATELLITE IMAGES
Authors: Imperatore N. , Dumas L.
Published: May 2022

Abstract. The photogrammetric 3D stereo reconstruction from pairs of strereo images is rising interest in the past few years in space field downstream. Nowadays, it is conceivable that a large production of DSMs from satellite images can become the primary source of 3D information on a global scale. However, in urban areas, DSMs produced with current technology suffer from poor quality. Indeed, even using very high resolution (VHR) images, there is too little information to generate disparity maps that reproduce very well defined shaped objects such as buildings.To address this issue, one solution may be to artificially increase image resolution beyond the sensor limits. Super resolution (SR) algorithms are designed to recover high frequencies, introducing significant information in a scene characterized by strong and frequent discontinuities such as a city. State-of-the-art methods relying on Deep Learning have shown remarkable results in this sense. The aim of this work is therefore to assess the contribution of single image SR Deep Learning techniques to the stereo matching and DSMs generation in an urban context, highlighting potential advantages and limitations that can show up when introducing such a technology in a multi-view stereo pipeline. The proposed contributions are: a methodology for super resolution of VHR data that takes into account realistic simulation of a satellite product; a testbed for the evaluation of the impact of super resolution on 3D photogrammetric reconstruction; a local analysis of the consequences of deep learning SR of VHR images on stereo matching.
A POINT-BASED LEVEL-SET APPROACH FOR THE EXTRACTION OF 3D ENTITIES FROM POINT CLOUDS – APPLICATION IN GEOMORPHOLOGICAL CONTEXT
Authors: Arav R. , Pöppl F. , Pfeifer N.
Published: May 2022

Abstract. The commonly used level-set models have been proven efficient in delineating and detecting objects in a variety of image processing applications. Such models do not require training or labelling, need little prior information as to existing objects, and want little adaptation between scenes. Despite their great potential, their utility for point cloud processing has been limited to 2D space. In this paper, we introduce a novel level-set-based approach which extracts entities directly from the point cloud while retaining their three-dimensional point form. To do so, we adapt the level-set scheme to 2D smooth manifolds represented by unstructured points in 3D space. Surface derivatives are then computed using a local surface parametrization based on a weighted least squares approximation. This alleviates the need for a triangulated mesh and facilitates the level-set evolution within the point cloud. As a driving force, we utilise visual saliency to focus on pertinent regions. As the saliency estimation is performed pointwise, the proposed model is completely point-based, resulting in three-dimensional entities extracted by their original points. We apply the proposed method to extract geomorphological entities in two fundamentally different scenes. Such entities present a challenge to existing extraction schemes, as they are embedded within their surrounding and they do not conform to closed parametric forms. The proposed approach enables the detection of various entities simultaneously, without prior knowledge of the scene, and regardless of their position. This promotes flexibility of form and provides new ways to quantitatively describe morphological phenomena and characterise their shape.
AUTOMATIC METHANE PLUMES DETECTION IN TIME SERIES OF SENTINEL-5P L1B IMAGES
Authors: Ouerghi E. , Ehret T. , de Franchis C. , Facciolo G. , Lauvaux T. , Meinhardt E. , Morel J.-M.
Published: May 2022

Abstract. Reducing methane emissions is essential to tackle climate change. Here, we address the problem of detecting automatically large methane leaks using hyperspectral data from the Level 1B product of the Sentinel-5P satellite. To do this, two features of TROPOMI (TROPOspheric Monitoring Instrument), the Sentinel-5P satellite sensor, are exploited. The first one is the fine spectral sampling of the data which allows to isolate features of the methane absorption spectrum in the shortwave infrared wavelength range (SWIR). The second one is the daily coverage of the whole Earth which allows to perform time series analysis. Our method involves three main steps: i) a pixel reconstruction, ii) an angle correction and iii) a plume detection with a time series. In the first step, a simplified absorption model is inverted to recover, for each pixel, a coefficient representative of the presence of methane which we call the methane coefficient. In the second step, a correction is made to the methane coefficient to take into account the viewing angle of the satellite. In the third step, the obtained coefficient is normalized spatially and then the detection is carried out pixel by pixel, by looking for anomalies in a time series. We validate our method by comparing the detected plumes against a recently published dataset of plumes manually detected in the Sentinel-5P L2 methane product. We then show how our method can complement the Sentinel-5P L2 methane product for the detection of methane plumes.
EFFICIENT TRAINING DATA GENERATION BY CLUSTERING-BASED CLASSIFICATION
Authors: Böge M. , Bulatov D. , Debroize D. , Häufel G. , Lucks L.
Published: May 2022

Abstract. Insufficient amount or complete absence of reference data for the training of classifiers is a general topic. Especially the state-of-the-art deep learning approaches have to deal with the availability or adaption of this reference data to produce the reliable results they are designed for. This paper will pursue different approaches according to the absence of training data for land cover classification from aerial images. First, we will analyze the performance of traditional classification in the absence of reference data using clustering techniques and salient features for the assignment of semantic labels. Second, we will transfer the results as training data to a DeepLabv3+ CNN with pre-trained weights to demonstrate the usability of the generated training data. Third, we expand the clustering approaches and combine them with a Random Forest classifier. Finally, if user interaction and manual annotation of training data are still necessary, we also introduce our labeling GUI that enables a simple, fast, and comfortable training data generation with only a few clicks. To evaluate our procedure, we used two datasets, including the Vaihingen benchmark, for which ground truth is available. Without any interactive steps except setting a few algorithm paremeters, we achieved an overall accuracy of 75% using the Deeplab method with image data only.
CALCULATING AND ANALYZING OF ECOSYSTEM SERVICE VALUE WITH BIG DATA BASED ON HIGH RESOLUTION LAND COVER INFORMATION
Authors: Cheng T. , Zheng X. , Xing X. , Su W.
Published: May 2022

Abstract. With the development of economics and society, the degradation of natural resources and environment is aggravated globally. The values of ecological environment and ecosystem service have been paid more and more attention popularly. However, the spatial-temporal changes in ecosystem service value (ESV) and their hotspots in China are not well understood. Chinese government has integrated the construction of ecological civilization into all aspects and processes of economic construction, political construction, culture construction and social construction, and clearly puts forward that we should establish the concept of natural value and natural capital, also natural ecosystem is valuable. During 2012 to 2021, an important project of China's First National Geographic Conditions Census and Continuously Monitoring had been completed by Chinese government. In this project, high resolution land cover product all over the country had been generated, and would be updated continuously every year. This achievement had received more and more attention, which brought the possibility of big data computing. A calculating method of land’s ecosystem service value was proposed by using this achievement. The characters of data type, data time phase, and data structure for all the remote sensing information was analyzed. Based on this, a revised ecosystem service value assessment model was used for calculating. Combining the standard of terrestrial ecosystem in China and the equivalent value factor per unit ecosystem area, the specific calculating algorithm based on the big data was designed. Wuhan 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 Wuhan city in 2015 was 9.42×1010 CNY, and the per capita ecosystem service value was 1.15×104 CNY. Specially, water supplied the most ecosystem service value which accounted for 79.95%, followed by forest, grassland, farmland, and desert. The research shows that high-precision quantification and spatialization ecosystem service value can be calculated and obtained by using the high resolution land cover product obtained in the project of China's First National Geographic Conditions Census and Continuously Monitoring; the independent research and development software has solved the problem of software requirements, and the operational efficiency and performance can meet the calculating needs; the big data calculating algorithm has solved the problems of design and computation of structured and unstructured big data computing models; The results of this study will help to maintain the sustainable development of an area, and supporting more effective decision-making around the implementation of ecological conservation policies.
SENTINEL-1 DATA TIME SERIES TO SUPPORT FOREST POLICE IN HARVESTINGS DETECTION
Authors: De Petris S. , Sarvia F. , Borgogno-Mondino E.
Published: May 2022

Abstract. Satellite remote sensing has long been used to monitor forest harvesting with accuracies appropriate for practical mapping across a wide range of forest types by using different sensors. Unfortunately, in Italy, most of the cuts take place in winter where the cloud cover is very high, making it impossible an early detection by optical data. In this framework, synthetic aperture radar (SAR) data such as Sentinel-1 (S1) allows a better land monitoring by penetrating cloud cover. In this work we tested some methods for time series breakpoint detection with the aim of mapping significant forest cover changes in 2019 over an Italian forested area. These maps can be useful tools to support the focusing of field surveys by forest police with the aim of increasing the monitorable areas and decreasing the related field survey costs. Four methods were proposed and compared based on the analysis of SAR polarimetric index time series (Cross Ratio index). In particular, adopted methods search for a breakpoint in the cross-ratio time series assuming it as moment after that forest canopy temporal behaviour significantly change. In general, high overall accuracy and user’s accuracy were found for all methods while producer’s accuracy and K values are lower denoting an underestimation of harvested areas by single method. Conversely, combining all methods into a final classification shows highest user’s accuracy (> 0.9) in detecting forests harvestings when more than two classification methods were adopted.
GEOGRAPHICAL TRANSFERABILITY OF LULC IMAGE-BASED SEGMENTATION MODELS USING TRAINING DATA AUTOMATICALLY GENERATED FROM OPENSTREETMAP – CASE STUDY IN PORTUGAL
Authors: Duarte D. , Fonte C. C. , Patriarca J. , Jesus I.
Published: May 2022

Abstract. Synoptic remote sensing systems have been broadly used within supervised classification methods to map land use and land cover (LULC). Such methods rely on high quality sets of training data that are able to characterize the target classes. Often, training data is manually generated, either by field campaigns and/or by photointerpretation of ancillary remote sensing imagery. Several authors already proposed methodologies to attenuate such labour-intensive task of generating training data. One of the preferred datasets that are used as input training data is OpenStreetMap (OSM), which aims at creating a publicly available vector map of the world with the input of volunteers. However, OSM data is spatially heterogenous (e.g., capital cities and highly populated areas often have high degrees of completion while unpopulated regions often have a lower degree of completion), where there are still large areas without OSM coverage. In this paper we present a set of experiments that aim at assessing the geographical transferability of satellite image-based segmentation models trained with OSM derived data. To this end, we chose two locations with different OSM coverage and disparate landscape (metropolitan region vs natural park region, in different landscape units), and assess how these models behave when trained in a region and applied in the other. The results show that the mapping of some classes is improved when considering a model trained in a different location.
SPATIOTEMPORAL SEGMENTATION OF SATELLITE IMAGE TIME SERIES USING SELF-ORGANIZING MAP
Authors: Silva B. L. C. , Souza F. C. , Ferreira K. R. , Queiroz G. R. , Santos L. A.
Published: May 2022

Abstract. Nowadays, researchers have free access to an unprecedentedly large amount of remote sensing images collected by satellites and sensors with different spatial, temporal, and spectral resolutions. This scenario has promoted the use of satellite image time series for spatiotemporal analysis. This paper presents a methodology for spatiotemporal segmentation of satellite image time series. Spatiotemporal segmentation finds homogeneous regions in space and time from remote sensing images based on spectral features. The proposed approach is unsupervised based on the self-organizing map (SOM) neural network and hierarchical clustering algorithm. It was implemented and applied to a region in the Mato Grosso state, Brazil. The results were evaluated using qualitative and quantitative approaches. In the qualitative approach, visual analysis was performed based on the land use and land cover map of the TerrraClass Cerrado project. In the quantitative approach, supervised and geometric metrics were used to analyze the quality of the produced segments. The results obtained are promising since the segments produced were homogeneous and with a low occurrence of over-segmentation.
BRIGHTEARTH: PIPELINE FOR ON-THE-FLY 3D RECONSTRUCTION OF URBAN AND RURAL SCENES FROM ONE SATELLITE IMAGE
Authors: Tripodi S. , Girard N. , Fonteix G. , Duan L. , Mapurisa W. , Leras M. , Trastour F. , Tarabalka Y. , Laurore L.
Published: May 2022

Abstract. With the growth of the availability and quality of satellite images, automatic 3D reconstruction from optical satellite images remains a popular research topic. Numerous applications, such as telecommunications and defence, directly benefit from the use of 3D models of both urban and rural scenes. While most of the state-of-the-art methods use stereo pairs for 3D reconstruction, such pairs are not immediately available anywhere in the world. In this paper, we propose an automatic pipeline for very-large-scale 3D reconstruction of urban and rural scenes from one high-resolution satellite image. Convolutional neural networks are trained to extract key semantic information. The extracted information is then converted into GIS vector format, and enriched by both terrain and object height information. The final classification step is applied, yielding a 16-class 3D map. The presented pipeline is operational and available for commercial purposes under the BrightEarth trademark.
ALEPPO PINE ALLOMETRIC MODELING THROUGH INTEGRATING UAV IMAGE-BASED POINT CLOUDS AND GROUND-BASED DATA
Authors: Aguilar F. J. , Nemmaoui A. , Aguilar M. A. , Jiménez-Lao R.
Published: May 2022

Abstract. Effective monitoring of Mediterranean forest is essential to determine the role of forest management in mitigating climate change and ensuring the maintenance of its environmental services. Most of the allometric models to estimate dry above-ground biomass (AGB) at tree level are based on knowing the diameter of the trunk at breast height (DBH). However, it is difficult, if not impossible, to estimate DBH from airborne/spaceborne sensors within the context of a remote sensing-oriented approach, being common to draw upon regression models to relate DBH to remotely sensed dendrometric variables such as total tree height (H) and tree crown diameter (CD). This study uses UAV (unmanned aerial vehicle) image-based data to estimate the dendrometric variables H and CD of the repopulated Aleppo pine (Pinus halepensis Mill.) located in a semiarid continental Mediterranean forest of Almería (southeast of Spain). DBH data were gathered through field work. Both bivariate (DBH = Φ(H)) and multivariate (DBH = Ψ(H, CD)) allometric models were developed by applying least-squares-based regression and machine learning regression methods. The results showed that multivariate allometric models performed better than bivariate at predicting DBH, both in terms of goodness-of-fit and stability against changes in training or testing samples. In addition, least-squares-based regression (linear and potential) provided statistically similar results to those obtained from complex machine learning ensemble algorithms. In this way, the easy-to-apply multivariate linear allometric model DBH = −4.84 + 1.73149H + 3.08114CD (R2 = 89.23%) would be recommended to locally estimate DBH in Aleppo pine from remotely sensed H and CD data.
COASTAL HABITAT MAPPING WITH UAV MULTI-SENSOR DATA: AN EXPERIMENT AMONG DCNN-BASED APPROACHES
Authors: Liu Y. , Liu Q. , Sample J. E. , Hancke K. , Salberg A.-B.
Published: May 2022

Abstract. With recent abundant availability of high resolution multi-sensor UAV data and rapid development of deep learning models, efficient automatic mapping using deep neural network is becoming a common approach. However, with the ever-expanding inventories of both data and deep neural network models, it can be confusing to know how to choose. Most models expect input as conventional RGB format, but that can be extended to incorporate multi-sensor data. In this study, we re-implement and modify three deep neural network models of various complexities, namely UNET, DeepLabv3+ and Dense Dilated Convolutions Merging Network to use both RGB and near infrared (NIR) data from a multi-sensor UAV dataset over a Norwegian coastal area. The dataset has been carefully annotated by marine experts for coastal habitats. We find that the NIR channel increases UNET performance significantly but has mixed effects on DeepLabv3+ and DDCM. The latter two are capable of achieving best performance with RGB-only. The class-wise evaluation shows that the NIR channel greatly increases the performance in UNET for green, red algae, vegetation and rock. However, the purpose of the study is not to merely compare the models or to achieve the best performance, but to gain more insights on the compatibility between various models and data types. And as there is an ongoing effort in acquiring and annotating more data, we aim to include them in the coming year.
IMPROVED ARTIFICIAL IMMUNE NETWORK CROP RECOGNITION ALGORITHM BASED ON DISPERSED VEGETATION INDEX GENETIC CHAIN
Authors: Zhou Y. , Lu X. , Zhang X. , Yu H. , Cai G.
Published: May 2022

Abstract. The rapid and accurate acquisition of crop planting spatial location information using remote sensing is one of the important guarantees to maintain the sustainable development of agriculture. However, the accuracy of crop identification by remote sensing is currently limited by many factors, such as the influence of other ground objects and the lack of time-series data. To overcome the above problems, this paper proposes an algorithm named improved artificial immune network crop recognition algorithm based on dispersed vegetation index genetic chain (IaiNet). This algorithm can be combined with multi-spectral data from Sentinel-2 series satellites for crop identification. As a test case, we identified and evaluated 3 different crop recognition scenarios in Henan, China. The results show that IaiNet can accurately identify the spatial distribution of crop planting. In all identification results, the accuracy is higher than 90%, and the kappa coefficient is greater than 0.9. In addition, the crop recognition results of IaiNet are significantly better than the random forest algorithm and support vector machine algorithm.
OPTIONS FOR ADAPTATION OF URBANIZED AREAS TO CLIMATE CHANGE INDUCED EXTREME RAINFALLS
Authors: Metelka T. , Janu T. , Sebek J.
Published: May 2022

Abstract. The impacts of the climate change progress in a form of heavy cloudbursts, have been being reported over the course of last decade from many world cities. The consequences of this phenomenon are causing damages to urban infrastructure and private properties, and they affect urban transport as well as other primary city functions. In this respect we should ask question, if and to which extent can our society be protected against these extreme hydrological processes. The paper focuses on current adaptation possibilities of cities and settlements to mitigate the impact of extreme heavy storms resulting in sudden flash floods, regardless of the location and elevation of the particular place. The impact of such cloudbursts on urban drainage performance in the city of Prague is analysed by means of coupled 1D and 2D simulation model supported by DEM and GIS technology as well as services of Google Maps. The influence of climate change in terms of growing rainfall intensities is assessed by an indicator called „Climate Factor“. The authors present that the only way of adapting cities to these rainfall extremes is based on a proper management of rainfall water outflow on the catchment surface.
ACCURACY ASSESSMENT OF CLOUD MASK DETECTION ALGORITHMS FOR CBERS-4 WFI IMAGERY
Authors: Oldoni L. V. , Sanches I. D. , Picoli M. C. A.
Published: May 2022

Abstract. Clouds limit the potential use of optical images. Proper clouds and cloud shadows detection are necessary steps for optical image applications. Few algorithms are flexible in detecting clouds in images with a limited number of bands, such as the Wide-Field Imager (WFI) sensor on board the China-Brazil Earth Resources Satellite (CBERS-4), which has four spectral bands (blue, green, red, and near-infrared). Therefore, this work aims to assess the accuracy of two cloud detection algorithms: CMASK and ATSA, and evaluate the influence of the ATSA configuration parameters. We selected four regions in Brazil for our analysis. In all cases, ATSA presented overall accuracy (OA) superior to CMASK. While the ATSA obtained OA greater than 0.91 for all analyzes, the OA from CMASK did not exceed 0.84. CMASK presented commission errors for the No Clear class (combination of Cloud and Cloud shadow) and inclusion errors for the Clear class close to zero. However, many errors of omission of clouds misclassified as the Clear class was observed. The ATSA algorithm presented a better balance between inclusion errors and omission errors. Our results can be used as guidance for choosing a cloud mask algorithm for the CBERS-4 WFI images and for analysis considering the images from WFI on board CBERS-4A and Amazonia-1, as they have similar characteristics.
MULTISENGE: A MULTIMODAL AND MULTITEMPORAL BENCHMARK DATASET FOR LAND USE/LAND COVER REMOTE SENSING APPLICATIONS
Authors: Wenger R. , Puissant A. , Weber J. , Idoumghar L. , Forestier G.
Published: May 2022

Abstract. This paper presents MultiSenGE that is a new large scale multimodal and multitemporal benchmark dataset covering one of the biggest administrative region located in the Eastern part of France. MultiSenGE contains 8,157 patches of 256 × 256 pixels for the Sentinel-2 L2A , Sentinel-1 GRD images in VV-VH polarization and a Regional large scale Land Use/Land Cover (LULC) topographic reference database. With MultiSenGE, we contribute to the recents developments towards shared data use and machine learning methods in the field of environmental science. The purpose of this dataset is to propose relevant and easy-access dataset to explore deep learning methods. We use MultiSenGE to evaluate the performance for urban areas using well-known deep learning techniques. These results serve as a baseline for future research on remote sensing applications using the multi-temporal and multimodal aspects of MultiSenGE. With all patches georeferenced at a 10 meters spatial resolution covering the whole Grand-Est Region, MultiSenGE provides an opportunity for environmental benchmark dataset will help to advance data-driven techniques for land use/land cover remote sensing applications.
UNSUPERVISED SAR CHANGE DETECTION METHOD BASED ON REFINED SAMPLE SELECTION
Authors: Peng Y. , Wei Z. , Cui B.
Published: May 2022

Abstract. In deep learning based synthetic aperture radar (SAR) change detection, selecting samples of high quality is a crucial step. In this work, we have proposed a refined sample selection algorithm for unsupervised SAR change detection. The propose and incorporation of volume control factors and multi-hierarchical fuzzy c-means (MH-FCM) algorithm generate samples of large diversity and high confidence, thus satisfying the needs for high quality samples. The method includes two phases: firstly, an enhanced difference image is constructed according to the difference consistency between single pixels and their neighbourhoods, and a triangular threshold segmentation method is then proposed to determine the volume control factors for sample selection. MH-FCM is developed to classify the log mean ratio difference image into 4 classes. Secondly, a dual-channel convolution neural network with an adaptive weighted loss is adopted to learn and predict the input and to obtain the change detection result. Experimental results of the Gaofen-3 dataset in Beijing have validated the effectiveness and usefulness of the proposed method.
THE WHOLE IS OTHER THAN THE SUM OF ITS PARTS: SENSIBILITY ANALYSIS OF 360° URBAN IMAGE SPLITTING
Authors: Beaucamp B. , Leduc T. , Tourre V. , Servières M.
Published: May 2022

Abstract. 360° imagery has been increasingly used to estimate the subjective qualities of the urban space, such as the feeling of safety or the liveliness of a place. These spherical panoramas offer an immersive view of the urban scene, close to the experience of a pedestrian. In recent years, Deep Learning approaches have been developed for this estimation task, only using flat images because these images are easier to annotate and process with standard CNNs. Thus to qualify the whole urban space, the panoramic images are divided into four flat sub-images that can be processed by the trained neural networks. The sub-images cover the 360° field of view, e.g. front, back, left, and right views. The four scores obtained are averaged to represent the level of the quality at the location of the panorama. However, this split introduces a bias since some elements of the urban space are halved over two images and the global context is lost. Based on the Place Pulse 2.0 dataset, this paper investigates the impact of splitting 360° panoramas on the perceptual scores predicted by neural networks. For each panorama, we predict the score for thirty-six overlapping sub-images. The scores were shown to have high variability and be highly dependent on the direction of the camera for the perspective images. This indicates that four images are not sufficient to capture the complexity of the perceptual qualities of the urban space.
Intercambios de cuidados y personas adultas mayores en el contexto de la pandemia en México
Authors: Estrada Iguíniz Margarita , Warn Cruz Lisa , Rojas García Georgina
Published: May 2022

El artículo se centra en el estudio de las formas de participación familiar de un grupo de personas adultas mayores mexicanas durante los primeros meses de la pandemia de COVID19. En un contexto en el que los integrantes de las familias debieron reorganizar su vida cotidiana debido al confinamiento y la reducción de las actividades económicas, las y los adultos mayores fueron una vez más fuente de apoyo y compañía. Estas personas pusieron a disposición de sus parientes cercanos sus recursos económicos y materiales, y su trabajo que se plasmó en los cuidados que brindaron a los suyos. Por su parte, hijos e hijas adultos buscaron la manera de proteger a sus progenitores. A través del intercambio de recursos y cuidados los y las integrantes las familias estudiadas lograron la organización de la vida diaria que requería el contexto de la pandemia, y paliar algunos de sus efectos más negativos como el empobrecimiento y el abandono.
Securitización urbana en el centro histórico de Valencia: actores, dispositivos y discursos
Authors: Fioravanti Hernán
Published: May 2022

Este artículo presenta un análisis etnográfico de los procesos de securitización urbana que están teniendo lugar en un barrio del centro histórico de Valencia. Trazamos una economía política de la securitización urbana en la escala local, fijándonos en el papel desempeñado por una pluralidad de actores entre los que encontramos vecindario, movimientos urbanos, cuerpos policiales e instituciones locales. El análisis se centra en una multiplicidad de dispositivos y tácticas securitarias, desde las arquitecturas preventivas y el urbanismo punitivo hasta la policialización de la convivencia vecinal y la implementación de ordenanzas de civismo, pasando por los sistemas privados de vigilancia. Nos interrogamos por los discursos e imaginarios colectivos que subyacen a esta dinámica, reflexionando sobre cómo se concibe la marginalidad urbana y la pertenencia vecinal. Concluimos apuntando a la relación entra la securitización urbana y los procesos de desplazamiento de población.
Gestionar y parodiar el trabajo digno. Sobre la creación de alternativas a la prostitución para mujeres trans y sus complejidades
Authors: Cutuli María Soledad
Published: May 2022

En este texto articulamos aportes de la antropología política y de los estudios de sexualidades y género, para abordar la centralidad de las tensiones entre prostitución y trabajo en la experiencia organizativa de las mujeres trans y travestis del área metropolitana de Buenos Aires, Argentina. En particular, nos preguntamos por los procesos de creación de emprendimientos cooperativos como alternativa a la prostitución para esta población, indagando en la vida cotidiana de uno de ellos. Basándonos en los resultados de un trabajo de campo etnográfico sostenido entre 2008 y 2012, exploramos las tramas, las tensiones y los sentidos involucrados en las categorías de trabajo y dignidad, a través de las cuales las personas involucradas procesaron sus experiencias
Causality between Environment and Financial Development in case of Pakistan: A Time Series Analysis
Authors: Amjad Ayesha , Manzoor Ayesha , Kulsoom Um-I-
Published: May 2022

Climate change is now widely regarded as one of the greatest issues of our era. As a result, it is critical to study the impact of various macroeconomic variables on the environment. The purpose of this research is to look at the link between financial development and environmental (CO2 emissions) in Pakistan from 1980 to 2014. The Auto Regressive Distributed Log (ARDL) technique was employed to track both long-term and short-term association between variables in this investigation. Granger causal testing is used to examine the causal relationship. Study results suggest that there is a unidirectional causality between CO2 and the financial development Index. However, the environment (CO2 emissions) in the long run will depend on the financial development that the financial development index (domestic credit to private sector aggregate market capitalization, and FDI) has in Pakistan. In the second model, the environment (energy consumption) also depends on financial development.
Climate Change and Agricultural Production in Pakistan
Authors: Shafiq Muhammad Nouman , Gillani Seemab , Shafiq Shaiza
Published: May 2022

Agriculture sector holds great value for Pakistan. Climate change is a major determinant of agricultural productivity all over the world. This sector is the most vulnerable sector to weather change. The productivity of this sector is being affected by several factors of climate change like temperature and rainfall patterns. The core objective of the study is to determine the impact of climate change on agricultural production (wheat, rice, and sugarcane) in Pakistan. Time series data from 1985–2018 is taken from the Pakistan Economic Survey, Metrology department of Pakistan, and the World Bank for the current analysis. Autoregressive Distributed Lag model is used to analyze the impact of climate change on agricultural production in Pakistan. The results show that climate change has an influential impact on agricultural production in Pakistan. The increase in mean temperature reduces agricultural production while the rain positively affects agricultural production. It is suggested that the government should give subsidies on agrarian tools, seeds, and fertilizers.
Impact of Renewable Energy Consumption and Health Expenditure on Air Pollutants: Implications for Sustainable Development in ASEAN Countries
Authors: Nawab Tabish , Muneza Christian , Afghan Mohibullah
Published: May 2022

This study examined the association between economic development and environmental degradation in the presence of health expenditure and renewable energy for selected 6 ASEAN nations covering the time span of 2000 to 2018. Before estimating the model, firstly check the stationary of variables for this purpose Levin Lin Chu unit root applied, results indicate that all variables are stationary at level. GMM is efficient in eliminating fixed/random effects in the model and also overcome the problem of endogeneity in the model. For causal analysis used Granger causality test and results indicates that there exists a bi-directional association among the consumption of energy and carbon emission. There exits uni-directional association exists among Economic development, expenditure on health and environmental degradation. GMM results indicate that spending on the health sector and the consumption of renewable energy sectors decline the environmental degradation level; on the other hand, economic growth boosts the ASEAN nations' carbon emission level. The recommendation of the study is to increase the level of economic development by enhancing the energy sector from non-renewable energy to renewable energy because it boosts the level of economic growth, which ultimately improve the health development and technologies which will strengthen the health facilities in the nation and also reduce the level of carbon production that helps to keep the environment healthy and clean.
Exploring the Challenges of Digital Literacy among Students with Visual Impairment Studying at Higher Education Level
Authors: Iqbal Kashif , Majeed Zahid , Ashraf Samina
Published: May 2022

Education is the beacon for every human around the globe. Students with visual impairment are also supposed to be part of the quality learning process. This is a great expedition for students with visual impairment to study at graduate or post-graduate levels in the recent wake of digital literacy. Meeting the academic needs of students with visual impairment in this era of the digital world requires a sufficient level of technological resources along with mastery to use them. This study aims to explore the challenges faced by students with visual impairment in digital literacy studying at the graduate and post-graduate levels.  A total number of 85 students with a visual impairment from graduate-level (N=50), post-graduate level, and above (35=50) were taken as a sample of this study. A questionnaire was developed as a tool of this study based on three main components. Statistical data were analyzed with IBM version22. Both the descriptive and inferential statistical analysis techniques were used to analyze the data. Frequencies and percentages of the responses along with the Mann-Whitney test were performed to analyze the data. The findings of the research showed that owning a basic level of digital literacy skills and a lack of advanced digital devices/resources causes challenges in academics for visually impaired students. It is recommended strongly that students with visual impairment must be provided with adequate and latest assistive technological facilities to enhance their digital skills and combat the challenges of digital literacy.
Narcissistic Personality, Social Media and Mental Health in Early Adulthood
Authors: Batool Memoona , Ahmad Laraib , Sadaqat Rashida , Tariq Shahnila
Published: May 2022

The purpose of this study was to examine the relationship between social media, narcissism, and the mental health of young adults. It was hypothesized that there is likely to be a relationship between narcissism, social media usage, and mental health in early adulthood. A correlation research design was used to collect data (online) from (N= 160) university students (male =31; female=129) with the age range of 18-25 years (M=19.58, SD=5.27). The sample was recruited by using a convenient sampling technique from different private sectors universities. Social Media Addiction Scale (SMAS-SF), Patient Health Questionnaire (PHQ), and 16 items pair of narcissism were used to collect data.  Patient Health Questionnaire introduced by Robert, Janet, and William. Pearson product-moment correlation analysis showed that mental health is positively correlated with social media whereas narcissism is negatively correlated with mental health. Implications show that seminar is very important for awareness about mental health and excessive social media usage. Results show that using hours of social media is significantly predicts mental health. Virtual problems, virtual communication, virtual information social media significantly correlated with mental health. Moreover, there are no gender differences in social media and mental health. So our hypothesis is proved. Our results contradicted the results of previous research which showed a relationship between Narcissism, Social Media, and their impact on mental health.
The Effect of Corruption and Political Instability on Firm’s Performance: Evidence from Low Income Economies
Authors: Kazmi Syed Mumtaz Ali Kazmi , Imran Syed Muhammad , Farooqi Khalid Farooq , Shahid Muhammad
Published: May 2022

Low-income economies have characteristics of high corruption and political instability. The underdeveloped corrupt financial system of low-income nations with political instability may constrain firms’ performance. The objective of the current study was to estimate the effect of corruption and political instability on firms’ performance in low-income economies. The recent study used firm investment in human capital and exports as a proxy to measure the firm’s performance.  We have applied logistic regression to the World Enterprise Survey dataset to find the probability of firms’ investment in human capital. The study concluded that corruption and political instability decrease the likelihood of a firm’s human investment in human capital. Firm-specific characteristics increase the probability of firms’ exports. For policy purposes, corruption must be reduced to increase firms’ investment in human capital.
The Role of Islamic Banking Development and its impact on Financial Stability: Evidence from Morocco’s Financial Institutions
Authors: Zehra Nida , Umair Hemal , Shabbir Malik Shahzad , Mallouli Amal EL
Published: May 2022

This paper aims to study the major phases of participative Islamic banks development in the country of Morocco, and the evolution of the participative banking landscape from the introduction of this new financial industry. The paper starts by presenting a historical overview of the first steps of Islamic finance in Morocco. This study sheds light on the first experience of launching Islamic banking products and the constraints that have hindered their development. Secondly, the paper presents the new successful start of Islamic finance in Morocco, which is reflected by the effective launch of participative banks and windows. The paper presents and analyzes the evolution of the participative banking landscape in Morocco between 2017 and 2019, based on statistical data from reports published by the central bank of this country (Bank Al-Maghrib). The findings of this study show that Morocco's monetary authorities have drawn important lessons from the first experience of launching Islamic banking products so-called alternative products. Indeed, the successful development of participative banks in Morocco requires a favorable and encouraging political, economic, and legal environment. This paper also shows that although the ecosystem of this new banking industry is still incomplete, there is a rapid evolution of Morocco's participative banking landscape.