bluuebunny/crossref_metadata_2025 huggingface dataset:
Job Portal
Authors: G Ramarao  , P Anjaniuma  , E Supriya  , Md Abdulrahman  , Ch Chakradhar  , K Karthik
Published: May 2024

In the current scenario, there is a rat race in each and every professional field. It is also true for job market. A job portal is a website dedicated for online information about recruiters as well as job seekers. A job portal helps both the job seekers and recruiters finding the right organization for the employees. In the case of job seekers, according to their educational qualification, experience and their preferences, the job portal shows the list of companies to the job seeker. And, to the recruiters, provides the suitable candidates from a pool of lacks. So, a job portal is the perfect online arena, where both the job seekers and the employers find their goal in the pursuit of getting a top notch company for the suitable candidates. The web application “Job Portal” provides an easy and convenient search application for the job seekers to find their desired jobs and for the recruiters to find the right candidate. Job seekers from any background can search for the current job openings. Job seekers can register with the application and update their details and skill set. They can search for available jobs and apply to their desired positions.
PREVALENCE OF INSULIN RESISTANCE IN PREDIABETIC PATIENTS WITH HYPERTENSION
Authors: Tran Vinh Thanh , Le Thanh Van , Doan Sang , Duong Linh Ha Khanh , Kieu Dung Ngoc , Nguyen Thuc Tri
Published: May 2024

stage between normal people and patients with type 2 diabetes mellitus. The pathogenesis is mainly related to insulin resistance (IR). The relationship between IR and hypertension is a complex and multifactorial phenomenon. We examined the prevalence of IR in prediabetic patients with hypertension.
Objectives: The present study aimed to determine the incidence of IR according to the HOMA2 model in the group of prediabetes with hypertension, compared with the group of prediabetes without hypertension.
Subjects and Methods: The cross-sectional study described and collected data from 191 people with prediabetes from February 2023 to September 2023 at the Examination Department of Cho Ray Hospital. IR was determined by the HOMA2 calculator, using a threshold of 1.29 for IR. Prediabetes and hypertension were diagnosed according to ADA 2022 and ISH 2020 criteria, respectively.
Results: The number of subjects selected to analyze the data was 179 patients, including 89 prediabetes patients without hypertension and 90 prediabetes patients with hypertension. The prevalence of IR in the overall prediabetes group was 56.9%. The prevalence of IR in the hypertensive group was 66.7%, higher than the non-hypertensive group at 47.2%, which was statistically significantly different. The values of the index in HOMA2 did not differ statistically significantly between non-hypertensive and hypertensive populations.
Conclusions: The prevalence of IR in prediabetes patients with hypertension was 66.7%, greater than the prediabetes patients without hypertension of 47.2%, P = 0.013. Attention should be paid to good control of blood pressure in patients with prediabetes.
CYTOTOXIC ACTIVITY OF FRACTIONS AND COMPOUNDS FROM PEPEROMIA PELLUCIDA
Authors: Tran Khanh Linh , Nguyen Thi Anh Lam , Ngo Thi Quynh-Mai
Published: May 2024

Peperomia pellucida is a widely distributed herb and commonly used in folk medicine for the treatment of various disorders. The present study reported the isolation, elucidation of compounds from the whole plant and their cytotoxic activities on Hep G2 (HB - 8065TM, hepatocellular carcinoma), A549 (CCL-185™, pulmonary adenocarcinoma), and MCF-7 (HTB - 22TM, breast cancer) cell lines. As a result, velutin exhibited the most significant cytotoxicity against Hep G2, A549, and MCF-7 cell lines with IC50 values of 34.00, 30.85, and 41.33 μg/mL, respectively.
CONTRIBUIÇÕES DA ÉTICA NO COTIDIANO DAS PROFISSÕES E DA EDUCAÇÃO
Authors: Do Nascimento Alice Lemos , Araújo Cristina Onasses Viana , Da Costa Terezinha Gonçalo
Published: May 2024

The present work takes a brief look at ethics from two perspectives - regarding the profession and regarding education. It is known that it is impossible to exhaust such a vast subject, but here we seek briefly and simply to understand what ethics is, its relationships with professional life, and how its reflections can contribute to an education increasingly centered on emancipatory values. The article was built based on bibliographic research, as it sought contributions from other authors and thinkers on the topic. The method used was qualitative.
Odevixibat (Bylvay)
Authors: CADTH
Published: May 2024

CADTH reimbursement reviews are comprehensive assessments of the clinical effectiveness and cost-effectiveness, as well as patient and clinician perspectives, of a drug or drug class.
The assessments inform non-binding recommendations that help guide the reimbursement decisions of Canada's federal, provincial, and territorial governments, with the exception of Quebec.
This review assesses odevixibat (Bylvay) 200 mcg, 400 mcg, 600 mcg, 1,200 mcg capsules.
Indication: For the treatment of pruritus in patients aged 6 months or older with progressive familial intrahepatic cholestasis.
Gambaran Karakteristik Individu dan Kondisi Lingkungan Rumah pada Penderita TB Paru di Desa Sayambongn Kecamatan Nambo
Authors: Ibrahim Alisafira , Balebu Dwi Wahyu , Syahrir Muhammad
Published: May 2024

Tuberkulosis (TB Paru) adalah penyakit menular yang disebabkan oleh mycobacterium Tuberculosis, yang dapat menyerang paru dan organ lainnya. Tuberkulosis masih menjadi masalah kesehatan masyarakat yang menimbulkan kesakita, kecatatan, dan kematian yang tinggi sehingga perlu dilakukan upaya penaggulangan. Tujuan penelitian ini adalah untuk mengetahui gambar karakteristik individu dan kondisi lingkungan rumah dengan TB paru di Desa Sayambongin Kecamatan Nambo Tahun 2023. Jenis penelitian ini dilakukan dengan rancangan penelitian deskriptif dengan jenis data kuantitaif. Teknik pengambilan sampel dalam penelitian ini adalah teknik sampling jenuh atau teknik penentuan sampel yaitu sebanyak 43 penderita. Metode pengumpulan data yang dilakukan melalui pengamatan langsungatau observasih serta dokumentasi dan wawancara. Metode analisis dalam penelitian ini menggunakan analisis univariat. Hasil penlitian yang di dapatkan bahwa riwayat pengobatan yang rutin 43 orang (100%), pencehayaan rumah yaitu paling banyak yaitu 43 (100%) dan jenis lantai lantai rumah 31 (72,1%). Saran pemerintah lebih memperhatikan lagi derajat kesehatan masyarakat khsusnya pada pada penderita TB Paru. 
Tuberculosis (pulmonary TB) is an infectious disease caused by the mycobacterium Tuberculosis, which can attack the lungs and other organs. Tuberculosis is still a public health problem that causes high levels of illness, disability and death, so efforts to control it need to be made. The aim of this research is to determine the characteristics of individuals and the conditions of the home environment with pulmonary TB in Sayambongin Village, Nambo District in 2023. This type of research was carried out with a descriptive research design with quantitative data types. The sampling technique in this research was a saturated sampling technique or sample determination technique, namely 43 patients. The data collection method is carried out through direct observation or observation as well as documentation and interviews. The analytical method in this research uses univariate analysis. The results of the research showed that 43 people (100%) had a history of routine treatment, 43 (100%) had the most house lighting and 31 (72.1%) house floor types. The government's advice is to pay more attention to the health status of the community, especially pulmonary TB sufferers.
HERANÇA DIGITAL: ASPECTOS JURÍDICOS ACERCA DA VIABILIDADE DA SUCESSÃO DE BENS DIGITAIS DEIXADOS PELO DE CUJUS
Authors: Moura Anna Luisa Duarte Nunes Mendes de , Paulo Vanessa Souto
Published: May 2024

O presente estudo visa analisar a viabilidade jurídica da sucessão de bens digitais acumulados em vida, uma área ainda não regulamentada especificamente pelo Código Civil brasileiro. Diante da crescente acumulação de patrimônio digital, emerge a necessidade de estabelecer mecanismos legais para a transferência desses bens após o falecimento do titular. O objetivo geral desta pesquisa é explorar a integração da herança digital ao ordenamento jurídico brasileiro. Especificamente, busca-se identificar lacunas na legislação vigente relativas à sucessão digital, analisar jurisprudências relevantes e realizar uma revisão bibliográfica abrangente. Adicionalmente, investiga-se a possibilidade de o testamento facilitar a regulamentação da herança digital, examinam-se os impactos nos direitos de personalidade do de cujus e discute-se a transmissão de contas em redes sociais por meio de testamentos. Utilizando-se de uma metodologia bibliográfica, com revisão de literatura em livros, websites especializados, teses, legislação e artigos científicos, este trabalho evidencia a importância de compreender a herança digital. A relevância deste estudo advém tanto das divergências doutrinárias existentes quanto da proposição de novos projetos de lei, refletindo a urgência de adaptar o Direito às transformações sociais decorrentes da digitalização dos patrimônios.
O CONTROLE DE LEGALIDADE DE ACORDO COM A LEI Nº 13.964/2019: UMA ANÁLISE DA IMPARCIALIDADE DO JUIZ SINGULAR FRENTE AO INSTITUTO DO JUIZ DAS GARANTIAS
Authors: Santos Edimilson Cosme dos , Oliveira Bruno Vinícius Nascimento
Published: May 2024

O Juiz das Garantias, introduzido pela Lei nº 13.964/2019 no Brasil, busca fortalecer as garantias individuais e a imparcialidade nos processos criminais, separando as funções de investigação e julgamento. Esta pesquisa tem como objetivo geral analisar os desafios jurídicos na implementação do Juiz das Garantias no sistema judicial brasileiro, compreendendo seus impactos e identificando estratégias para superá-los. Os objetivos específicos incluem investigar aspectos operacionais e logísticos da implementação, avaliar a compatibilidade constitucional, examinar os impactos na efetividade processual e identificar entraves e soluções para garantir uma administração da justiça mais ágil. Utilizando abordagem bibliográfica jurídica, esta pesquisa justifica-se pela significativa mudança na condução dos processos criminais, impactando diretamente a proteção dos direitos individuais e a confiança na justiça. Resultados indicam a necessidade de capacitação, aprimoramento da estrutura judiciária, padronização de procedimentos, monitoramento constante, transparência e participação social. A introdução do Juiz das Garantias é crucial para salvaguardar direitos fundamentais, promovendo equidade no sistema de justiça e evitando a concentração excessiva de autoridade. No entanto, desafios práticos devem ser reconhecidos e enfrentados para garantir uma implementação eficaz e adequada às necessidades específicas do sistema jurídico brasileiro.
O PRINCÍPIO CONSTITUCIONAL DA PROTEÇÃO INTEGRAL DA CRIANÇA E DO ADOLESCENTE E O PAPEL DO JUIZ NO ÂMBITO JURÍDICO SOCIAL
Authors: Freitas Maria Luiza de Moura de Mello
Published: May 2024

A pesquisa teve como objetivo analisar o princípio da Proteção Integral da criança e do adolescente no Brasil, para o efetivo cumprimento do art.227 da CF e art. 4º do ECA, que trata da prioridade absoluta. Entende-se que a família, a sociedade e o Estado devem trabalhar em conjunto para que se efetivem as garantias das crianças e adolescentes. Não só porque existem leis tutelando os direitos das crianças e adolescentes quer dizer que os mesmos são efetivados na prática. Por isso é necessária uma conjuntura de políticas sociais em consonância com a legislação juvenil, com apoio da sociedade e da família, bem como com a presença do judiciário para que a efetivação desses direitos tenham chance de proteger crianças e adolescentes. Sendo assim, o judiciário se faz essencial para reger todos os elementos componentes da rede de proteção integral das crianças e adolescentes. No entanto, o seu papel numa seara de direitos jamais pode se descuidar do social. A metodologia utilizada foi a pesquisa bibliográfica, compreendendo livros, artigos e consultas a internet. Foi realizado ainda um estudo de direito comparado com intuito de maior aprofundamento dos direitos da criança e do adolescente e a Doutrina da Proteção Integral nas Convenções Internacionais, Constituições e leis infraconstitucionais no Brasil.
A Brief Discussion on the Overall Classification Algorithm of Airborne LiDAR Point Cloud
Authors: Hu Jinrong , Li Juanjuan
Published: May 2024

Abstract. Point cloud classification is the most important problem in airborne LiDAR point cloud data processing. In recent years, classification strategies with new theoretical background keep emerging, so it is urgent to make a more systematic and detailed summary of existing point cloud filtering algorithms, so that relevant researchers can have a more macroscopic and clear understanding of various algorithms and their advantages and disadvantages. Based on the characteristics of airborne LiDAR point cloud data, this paper combs the general process of point cloud classification. This paper summarizes the current mainstream classification methods and analyses their application effects in different scenarios, aiming at exploring and customizing suitable point cloud classification methods according to specific purpose objectives or industry standards. The point cloud classification is classified into three-level classification strategy. The first-level classification is gross error elimination, the second-level classification is point cloud filtering, which is to distinguish ground points from non-ground points. The third-level classification is to extract thematic point clouds from non-ground points according to application requirements. At present, the primary and secondary classification methods are relatively diverse and mature, reaching a certain level of application, while the tertiary classification is still in the initial stage of exploration, and large-scale application is not widespread.
PCINet: a Prototype- and Concept-based Interpretable Network for Mutli-scene Recognition
Authors: Hua Yuansheng , Zhu Jiasong , Li Qingquan
Published: May 2024

Abstract. With the development of remote sensing techniques, a large number of high-resolution aerial images is now available and benefit many applications. Multi-scene recognition plays a key role in applying remote sensing images to these applications, which refers to predicting multiple scenes coexisted in an aerial image and has attracted an increasing attention. Recently, most researchers tend to invent deep learning-based recognition models and has gained great achievements. However, few efforts have been deployed to explaining the success of deep neural networks in multi-scene recognition. To address this, we introduce concept bottleneck model (CBM) to interpreting model performance and propose a novel network, namely Prototype- and Concept-based Interpretable Network (PCINet), that projects aerial imagery into a prototype-concept memory bank and encode their correlations for explaining how a network can identify coexisting scenes in an aerial image. Specifically, the proposed network mainly consists of two branches: prototype matching that measures similarity scores between image features and scene prototypes, and concept bottleneck branches that aligned image features to textual embeddings and compute their relations with concept embeddings. Afterwards, Outputs are integrated for inferring scene categories. Experimental results show that the model enhances interpretability, providing valuable insights for urban planning and resource management, thereby bridging the gap between deep learning models and practical applications.
Image Data Stream Organization and Online Analysis Application Based on Data Cube Technology
Authors: Huang Xinyuan , Gao Xiaoming , Ouyang Sida , Fu Zhengbo
Published: May 2024

Abstract. This study aims to explore the important role of data-like cube structures in modern remote sensing data processing and data analysis through ArcPy and Python multiprocessing techniques. A multi-scale spatial data cube is innovatively developed to improve the efficiency of remote sensing data management and optimize data analysis. The core of this study is to define and implement grid cells of different sizes that form the basis of data cube, and to quantify the efficient coverage of specific areas using Python multiprocessing techniques. Experiments were conducted in Hainan Province, and efficient data coverage of the whole Hainan Province was realized using the grid data method, which significantly reduced the amount of remote sensing data and processing time required. This shows that the method has successfully improving data coverage capacity and utilization efficiency. The results of this study not only demonstrate the effective application of data-like cubes in remote sensing data processing and analysis, but also provide new perspectives and methods for future complex spatial data analysis and large-scale remote sensing data processing.
Seasonal changes of Chlorophyll-A concentration in Jiujiang city based on remote sensing
Authors: Jiang Wei , Kong Fanping , Ding Xiaohui , Adam Elhadi , Cui Shiai , Luo Gan
Published: May 2024

Abstract. The Chlorophyll-a (Chla) concentration is an important parameter characterizing the water quality of rivers and lakes. Satellite remote sensing provides new opportunities for the quantitative monitoring of Chla concentration in large-scale water bodies. In this study, Sentinel-2 satellite remote sensing data integrated on the Google Earth Engine (GEE) remote sensing big data platform are employed, along with hourly measured water quality site data, to establish a quantitative inversion model for Chla concentration water quality parameters in Jiujiang City within Jiangxi Province, China. The Chla concentration is estimated for each quarter from 2020 to 2022, and the spatial distribution is analyzed, revealing the changing trend of Chla concentration over the past three years. The key findings are as follows: (1) The quantitative inversion model for Chla concentration has been validated with measured data, achieving a model accuracy of 0.53; (2) The spatial inversion results of Chla concentration exhibit an increasing trend that is consistent with actual measurement site results; (3) Owing to the influence of human activities and the peak-low water level of rivers and lakes, Chla concentration shows a discernible seasonal variation pattern. This methodology offers a new perspective for analyzing the seasonal variation characteristics of Chla concentration in rivers and lakes, providing valuable insights for the sustainable management of river and lake water quality.
An Ensemble Learning Framework for Anomaly Detection of Important Geographical Entities
Authors: Li Haolin , Tian Jiaojiao , Wang Shan , Song Ping , She Yi
Published: May 2024

Abstract. Due to the complex landforms and the limited resolution of remote sensing imagery, it is difficult to avoid the problem of incorrectly capturing geographical entities, such as buildings. Therefore, anomaly detection of important geographical entities is of great significance to ensure the authenticity and accuracy of geographical entity data. In this paper, we propose an ensemble learning framework for anomaly detection of geographical entity by aggregating the predicted labels generated by multiple deep learning models. In detail, we explore multiple change detection and semantic segmentation model and fully utilize the advantages of various deep learning neural network architectures. The proposed anomaly detection strategy of buildings has been performed on two benchmark datasets, including WHU Building change detection dataset and LEVIR building change detection dataset, the experimental results prove that the proposed method can achieve a more robust and better performance than using single change detection model in terms of quantitative performance and visual performance.
Mobile LiDAR-based Real-time Identification of Transmission Lines
Authors: Li Minglei , Xu Li , Li Mingfan , Qu Guoyuan , Wei Dazhou , Li Wei
Published: May 2024

Abstract. This paper proposes a method for identifying 3D point cloud of transmission line acquired by light detection and ranging (LiDAR) real-time mobile scanning. Since the single frame of point cloud obtained by LiDAR is sparse, the method employs a sliding spatial window strategy with Kalman filtering for dynamic point cloud registration. Then, a 3D point cloud deep learning neural network that utilizes uniform sampling and local feature aggregation (LFA) is designed specifically for transmission line objects. The network handles the problem of long-span objects and a large amount of point cloud. Finally, the instantiated transmission line objects are extracted from the top-down projection of the semantically segmented 3D point cloud by fast Euclidean clustering algorithm. Experiments demonstrate that the method achieves a classification accuracy of 94.7% and a mean intersection over union of 81.6% on 3D point cloud datasets of transmission line obtained from LiDAR mobile scanning, validating its ability to achieve real-time identification and distance measurement of transmission line objects.
Terrestrial Laser Scanning (TLS) Survey and Building Information Modeling (BIM) of The Edmund Pettus Bridge: A Case Study
Authors: Liu Junshan , Li Botao
Published: May 2024

Abstract. The comprehensive digital documentation of the Edmund Pettus Bridge, a symbol of the American Civil Rights Movement located in Selma, Alabama, serves as an example of integrating Terrestrial Laser Scanning (TLS) and Building Information Modeling (BIM) technologies for the conservation of heritage infrastructure. This study aimed to employ advanced TLS and other Reality Capture (RC) techniques to generate a high-resolution, three-dimensional representation of the bridge, thereby aiding in its structural assessment and preservation. Utilizing TLS alongside 360-degree photography and UAV surveys, the project achieved detailed coverage of the bridge's architectural and structural features. The data collected was processed into a BIM model using Autodesk Revit, offering comprehensive analysis and preservation planning. The findings from this research highlighted previously undetected areas of material degradation and structural vulnerabilities, emphasizing the value of TLS and BIM in revealing critical insights into the condition of heritage structures. This project preserved the digital memory of an iconic Civil Rights landmark and set a framework for applying digital documentation technologies in heritage infrastructure conservation. The success of this methodology offers a valuable precedent for future conservation efforts, showcasing how RC technologies can enhance the preservation of historical sites and ensure their legacy is maintained for future generations.
Suitability analysis for implementing wind and solar farms based AHP method: Case study in Inner Mongolia, China
Authors: Liu Ting , Zhang Tao , Zou Yunjia , Wang Guanghui , Dai Hailun , Zhang Wei
Published: May 2024

Abstract. As important green energy, wind power and photovoltaic power have great development prospects. The suitability evaluation of wind and solar power plants is a popular research field, which is related to the sustainable and healthy development of wind and solar power generation. In this paper, based on multiple dimensions such as land types, climatic conditions, topographic features and policy environment, we selected 10 indicators and combined (analytical hierarchy process) AHP method to build a suitability assessment model for evaluating the suitability of solar and wind power in Inner Mongolia, China. The findings revealed that, Inner Mongolia has a great potential to generate wind and solar electricity, for wind power, the category of ‘excellent’ regions covers 83855 km2 and represents 7.10% of the total surface area; for solar power, 7.66% (nearly 90420 km2) are classified as ‘excellent’. The suitability of both solar and wind energy in the western region is considered to have the most suitable development region, parts of Alxa League and Bayannur City have great potential for combined wind and solar power generation. The research results could provide important technical and data support for capacity evaluation and power station location decision.
Hourly near-ground NO2 concentration retrieval from geostationary satellite observations
Authors: Song Jie , Zang Lin , Mao Feiyue , Zhang Yi , Chen Jiangping
Published: May 2024

Abstract. Nitrogen dioxide (NO2) is an important contributor to the formation of acid rain, photochemical smog and aerosol particles, which seriously endangers public health. At present, remote sensing of polar-orbiting satellites is a conventional means to obtain large-scale NO2 distribution, but it cannot capture the rapid change because of long revisit periods. The Advanced Himawari Imager (AHI) on the Himawari-8 geostationary satellite has the advantage of high time resolution, which makes it possible to realize near-real-time atmospheric monitoring. Here, based on the absorption characteristics of NO2 in infrared radiation, hourly near-surface NO2 concentrations are retrieved based on the brightness temperature from AHI and auxiliary information such as meteorology and aerosol. The results of 10-fold cross-validation show that NO2 estimations from satellite are in good agreement with in-situ measurements, and their determination coefficient (R2) can reach 0.79. Due to different emission and atmospheric diffusion conditions at different time, the model performance presents a diurnal variation of high accuracy in the noon and afternoon but low accuracy in the morning. Based on the retrieval dataset, it is found that high NO2 concentrations are mainly concentrated in the densely populated and industrial areas such as the North China area. In addition, NO2 pollution mainly occurs in autumn and winter, and the average NO2 concentration in winter is about 1.63 times that in summer in 2021. This study provides a new insight for satellite retrieval of NO2, which is of great significance for real-time pollution monitoring and public health protection.
Experiments with Combining LiDAR and Camera Data Acquired by UAS and Smartphone to Support Mapping
Authors: Tamimi Rami , Toth Charles
Published: May 2024

Abstract. Aerial mapping using Unmanned Aerial Systems (UAS), such as the DJI Mavic 3 Enterprise, has revolutionized photogrammetry, enabling efficient data capture for small-scale projects. The typical nadir perspective of UAS mapping, however, imposes limitations on capturing critical details of features due to its predominantly vertical viewpoint. Overcoming this challenge often requires manual, low-altitude flights by experienced UAS pilots to achieve high-angle oblique perspectives, unless gimbled camera mount is used. This study explores the integration of high oblique angle perspectives using the iPhone 15 Pro, which boasts advanced camera capabilities and an integrated LiDAR sensor, to complement UAS imagery. The iPhone 15 Pro's camera sensors provide a Ground Sampled Distance (GSD) comparable to UAS cameras, while its LiDAR sensor, with about five meters of range, enhances mapping capabilities by delivering accurate depth measurements in close range. By utilizing various georeferencing options for the imagery and LiDAR data from the iPhone 15 Pro with UAS nadir imagery, we can achieve a more comprehensive object space reconstruction, significantly improving the accuracy of geospatial mapping. Both the Mavic 3 Enterprise and the iPhone 15 Pro, though operating independently on their respective platforms, support Real-Time Kinematic (RTK) corrections, facilitating precise positioning for the entire system trajectory. Strategic placement and utilization of Ground Control Points (GCPs) aid in the georeferencing of the complete dataset, enhancing its overall accuracy. To validate the accuracy of the acquired data, checkpoints are established on-site. The positions derived from the integrated UAS and iPhone 15 Pro data are compared against these checkpoints to quantify the accuracy and reliability of the data. Additionally, Post-Processed Kinematic (PPK) techniques are employed to validate the trajectories of all data collection systems, ensuring the reliability of the acquired data, especially in instances where RTK corrections may be lacking. In summary, this research showcases comprehensive, multi-dimensional geospatial datasets by conducting validation studies that assess the accuracy and reliability of georeferenced datasets against known ground truth checkpoints. Such validation studies are crucial for identifying gaps in current methodologies and suggesting areas for improvement, thereby aiming to enhance the quality and accuracy of geospatial mapping applications. Through the integration of UAS and smartphone mapping, complemented by rigorous validation efforts, we aspire to achieve improved geospatial mapping accuracy.
A Scene Unmixing Deep Learning Network for Local Climate Zone Mapping and Analysis Using Very High Resolution Remote Sensing Imagery
Authors: Tian Xinji , Li Jiayi , Huang Xin
Published: May 2024

Abstract. With the acceleration of urbanization, the environment and climate necessary for our survival have gradually deteriorated, leading to the increasing prominence of the Urban Heat Island (UHI) effect. Local Climate Zone (LCZ) classification, as a standard of urban morphology, has become an essential tool for monitoring the UHI effect and conducting temperature studies. Deep Learning (DL) models have the ability to represent high-level semantic features. Therefore, this paper proposes amixed scene unmixing DL framework for LCZ mapping and analysis using Very High Resolution (VHR) remote sensing images. This framework consists of a two-stream deep network, including a pure scene classification network (PS-Net) and a mixed scene unmixing network (MSU-Net). We conducted random sampling tests in Wuhan, China in the experiment A. The results show that this model achieved a satisfactory accuracy with the Overall Accuracies (OAs) is 96.78% and a mixed scene unmixing Mean Absolute Error (MAE) of 0.0495. Furthermore, we applied the proposed model to generate LCZ map for five districts in Wuhan in the experiment B. The test accuracy between two experiments differs very slightly. These results demonstrate the applicability and potential of our model for LCZ mapping and urban climate analysis.
UAV and Deep Learning: Detection of selected riparian species along the Ganga River
Authors: Tripathi Ravindra Nath , Ramachandran Aishwarya , Agarwal Karan , Tripathi Vikas , Badola Ruchi , Hussain Syed Ainul
Published: May 2024

Abstract. Environmental protection and sustainable natural resource management are being recognised worldwide as essential goals to safe guard human health and well-being. Riparian zones, that face the highest decline in freshwater biodiversity, are of prime conservation priority because they are essential for regulating climate, preserving aquatic-terrestrial biodiversity, maintaining ground water recharge and restoring rivers. In today's fast-paced data-driven environment, artificial intelligence (AI) is the precise answer to a wide range of problems including biodiversity conservation and wildlife management. Leveraging advancements like Uncrewed/Unmanned Aerial Vehicles (UAVs) and AI has resulted innovative strides in wildlife conservation. This study utilised UAV imagery to record high-resolution data of aquatic habitat and species along the Ganga River and employed deep learning algorithms to analyse the data. Through extensive field surveys in the Hastinapur Wildlife Sanctuary, 7,025 photos representing a variety of environments, including 20,000 annotated samples of aquatic animals such as turtles and gharials were generated. Vision based computing capabilities such as pattern recognition model were developed to identify these species. To enrich and enhance the dataset and model, we used different image pre-processing techniques. Slight rotation (±5 degrees), minor cropping (up to 10%), and adjustments in brightness, saturation, and shear (±15%) were applied. Controlled blur (up to 0.5%) and exposure modifications (±5%) were also implemented on the image dataset to improve accuracy. Three Convolutional Neural Network (CNN) architectures, single-stage detectors named YOLO v7, YOLO v8, and Roboflow 3.0, were used for detecting the select species. Results show YOLO v8 excels, achieving mean average precision (mAP) of 98.8% for gharial and 92.2% for turtle detection, with a rapid average detection time of 0.308 seconds per frame at 3200 × 3200 resolution. Additionally, our model demonstrates real-time species detection capability through innovative frame sampling techniques with UAVs. This methodology provides promising technique to collect scientific data on IUCN red listed turtles and critically endangered gharials, allowing detection, monitoring, and real time counting with minimal intrusion. In conclusion, the fusion of UAVs and deep learning promises to revolutionize habitat monitoring, aiding conservation decision-making.
PRISMA Hyperspectral Satellite Imagery Application to Local Climate Zones Mapping
Authors: Vavassori Alberto , Oxoli Daniele , Venuti Giovanna , Brovelli Maria Antonia , Mohamed Ali Badr Eldin Ali , Moazzam Afshin , Siciliani de Cumis Mario , Sacco Patrizia , Tapete Deodato
Published: May 2024

Abstract. The urban heat island effect exacerbates the vulnerability of cities to climate change, emphasizing the need for sustainable urban planning driven by data evidence. In the last decade, the Local Climate Zone (LCZ) model emerged as a key tool for categorizing urban landscapes, aiding in the development of urban temperature mitigation strategies. In this work, the contribution of hyperspectral satellite imagery to LCZ mapping, leveraging the Italian Space Agency (ASI)’s PRISMA satellite, is investigated. Mapping performances are compared with traditional multispectral-based LCZ mapping using Sentinel-2 satellite imagery. The Random Forest algorithm is utilized for LCZ classification, with evaluation conducted through spectral separability analysis and accuracy assessment between PRISMA and Sentinel-2 derived LCZ maps as well as with the benchmark LCZ Generator mapping tool. An initial experiment on the effect of PRISMA image pan-sharpening on LCZ spectral separability is also presented. Results obtained for Milan (Northern Italy) demonstrate the potential of hyperspectral imagery in enhancing LCZ identification compared to multispectral data, with promising improvements in LCZ maps overall accuracy. Finally, air temperature patterns within each LCZ class are explored, qualitatively confirming the influence of urban morphology on thermal comfort.
Multitemporal Seagrass Mapping and Monitoring of Posidonia Meadows and Banquettes for Blue Carbon Conservation: The Poseidon Project
Authors: Ceccherelli Giulia , Chiabrando Filiberto , Gallitto Francesca , Lingua Andrea , Longhi Valeria , Maschio Paolo , Matrone Francesca , Menna Fabio , Nocerino Erica , Scalici Massimiliano , Secco Silvia , Tanduo Beatrice
Published: May 2024

Abstract. In the framework of mapping underwater environments, Posidonia Oceanica (PO) is a fundamental indicator for environmental monitoring and related studies and analyses for climate change. The POSEIDON project aims to study, develop and test new methodologies to provide knowledge, as well as ultra-high-resolution (UHR) and beyond ultra-high-resolution (BUHR) mapping and monitoring data for the conservation of the PO and related meadows and banquettes in the Mediterranean area. The project is funded by the National Recovery and Resilience Plan and stems from the recommendation of international organizations (European Environmental Directives, Mediterranean PO Network, International Partnership for Blue Carbon, Intergovernmental Oceanographic Commission of UNESCO in the Save the Wave project). Starting from new integrated survey methods and legacy data, the PO, ichthyofauna and seagrass banquettes will be mapped to understand their evolution and extract indicators and useful trends. Furthermore, the produced information will be stored and shared in a webGIS and a dedicated app to prevent unexpected damages during sailing or other activities. POSEIDON will use imaging sensors (RGB and multi/hyperspectral cameras) to extract information. With specific lighting equipment for significant water depths, these sensors will be conducted (by scuba divers or Autonomous Underwater Vehicles) next to the object to acquire BUHR data. Finally, to monitor the fauna (fish, other species, invertebrates), terrestrial and underwater videogrammetry will be applied using mono and stereo-cameras with object detection algorithms.
Comparing firn temperature profile retrieval based on the firn densification model and microwave data over the Antarctica
Authors: Wang Xiaofeng , An Lu , Langen Peter L. , Li Rongxing
Published: May 2024

Abstract. The firn temperature is a crucial parameter for understanding firn densification processes of the Antarctic Ice Sheet (AIS). Simulations with firn densification models (FDM) can be conceptualized as a function that relies on forcing data, comprising temperature and surface mass balance, together with tuning parameters determined based on measured depth-density profiles from different locations. The simulated firn temperature is obtained in the firn densification models by solving the one-dimensional heat conduction equation. Microwave satellite data on brightness temperature at different frequencies can also provide remote sensing monitoring of firn temperature variations across the AIS (i.e., the L-band up to 1500 meters). The firn temperature can be estimated by the microwave emission model and the regression method, but these two methods need more observations of temperature profiles for correction and validation. Therefore, we compiled a dataset with temperature profiles and temperature observations with depth around 10 meters. In this work, two methods were used to simulate/retrieve firn temperature across the Antarctic ice sheet. One method estimated the temperature profiles by solving the one-dimensional heat conduction equation driven by reanalyses and regional climate models, which are used in the simulation of FDMs. The other one established a relationship between the multi-frequency brightness temperature data from microwave remote sensing satellites and the firn temperature.
Strategic Planning of Rural Telecommunication Infrastructure: A Multi-Source Data Fusion and Optimization Model
Authors: Chaabane Ferdaous , Réjichi Safa , Ben Salem Houssem , Elmabrouk Hassen , Tupin Florence
Published: May 2024

Abstract. The United Nations has underscored the critical role of digital connectivity by integrating it into several sustainable development goals, with the ambition for nations worldwide to achieve comprehensive access by 2030. Over thousand million people, primarily in rural areas, are disconnected from the digital world, highlighting the urgent need for viable and sustainable telecommunications solutions. These areas are characterized by sparse populations and lower economic levels, presenting great challenges for connectivity. This work introduces a strategy for enhancing rural telecommunication planning using geospatial and remote sensing data, deep learning-based clustering techniques, network graphs, and terrain analysis. The objective is to develop an optimal network topology and identify prime locations for telecommunications infrastructure, such as towers or relay stations. The methodology begins with the application of an adapted Deep Embedded Clustering (DEC) technique to identify community boundaries accurately. Then, it combines geospatial data (such as roads, terrain slope, flatness, etc.) and remote sensing data (vegetation, waterways, etc.) through an optimization algorithm. This process aims to determine the most suitable sites for infrastructure placement and the best network topology for connecting these areas. The study focuses on the region of Congo, offering a detailed case study on the application of this approach. Experimental results are presented to demonstrate the effectiveness of the proposed telecommunications expansion strategy.
Towards Integration of IndoorGML and GDF for Robot Navigation in Warehouses
Authors: Xiang Ziwei , Yu Kunlin , Wang Zhiyong
Published: May 2024

Abstract. With the development of the navigation technology, the outdoor navigation has made great progress, whereas the indoor navigation has some areas which is underdeveloped, insufficient to meet the rapidly increasing demands of people as well as the robotics. Even though, the advance in indoor navigation technology still has really brought a wide range of applications and a broad market, for instance, the flourishing intelligent warehouse system utilizes multi-robot operation which have the certain requirement for an accurate indoor navigation system. As for the indoor navigation, the OGC standard IndoorGML has been released and undergoing revision constantly. While the document really provides more advantageous support for the applications of Indoor Location-Based Services (LBS), in some aspects, especially the door-to-door navigation and the warehouse environment, it is not sufficiently adaptable, with still some room for improvement. IndoorGML is powerful for the common indoor scenarios like malls and offices, while as for carefully-arranged warehouse environment and other large-scale operation scenarios with multi-robots that is more similar to an ordered system, it is obviously insufficient. In this paper, we discuss about the potential to combination of IndoorGML and ITS standard ISO 20524 (GDF5.1), and extend the OGC standard indoorGML. We analyze the definition as well as function of related concepts, making some comparisons between these two standards. We conclude that these two standards are well-matched with vital potential to merge and unify the indoor and outdoor systems for spatial information.
Crop Phenology Estimation in Rice Fields Using Sentinel-1 GRD SAR Data and Machine Learning-Aided Particle Filtering Approach
Authors: Yang Jie , Shi Hongtao , Xie Qinghua , Lopez-Sanchez Juan M. , Peng Xing , Yu Jianghao , Chen Lei
Published: May 2024

Abstract. Monitoring crop phenology is essential for managing field disasters, protecting the environment, and making decisions about agricultural productivity. Because of its high timeliness, high resolution, great penetration, and sensitivity to specific structural elements, synthetic aperture radar (SAR) is a valuable technique for crop phenology estimation. Particle filtering (PF) belongs to the family of dynamical approach and has the ability to predict crop phenology with SAR data in real time. The observation equation is a key factor affecting the accuracy of particle filtering estimation and depends on fitting. Compared to the common polynomial fitting (POLY), machine learning methods can automatically learn features and handle complex data structures, offering greater flexibility and generalization capabilities. Therefore, incorporating two ensemble learning algorithms consisting of support vector machine regression (SVR), random forest regression (RFR), respectively, we proposed two machine learning-aided particle filtering approaches (PF-SVR, PF-RFR) to estimate crop phenology. One year of time-series Sentinel-1 GRD SAR data in 2017 covering rice fields in Sevilla region in Spain was used for establishing the observation and prediction equations, and the other year of data in 2018 was used for validating the prediction accuracy of PF methods. Four polarization features (VV, VH, VH/VV and Radar Vegetation Index (RVI)) were exploited as the observations in modeling. Experimental results reveals that the machine learning-aided methods are superior than the PF-POLY method. The PF-SVR exhibited better performance than the PF-RFR and PF-POLY methods. The optimal outcome from PF-SVR yielded a root-mean-square error (RMSE) of 7.79, compared to 7.94 for PF-RFR and 9.1 for PF-POLY. Moreover, the results suggest that the RVI is generally more sensitive than other features to crop phenology and the performance of polarization features presented consistent among all methods, i.e., RVI＞VV＞VH＞VH/VV. Our findings offer valuable references for real-time crop phenology monitoring with SAR data.
Efficient Geometric Correction Workflow for Airborne Hyperspectral Images through DEM-Driven Correction Techniques
Authors: Yu Junchuan , Li Yichuan , Ge Daqing , Chen Yangyang , Wu Qiong , Ma Yanni
Published: May 2024

Abstract. Geometric correction, a pivotal step in the preprocessing of airborne remote sensing imagery, is critical for ensuring the accuracy of subsequent quantitative analyses. Achieving precise and efficient geometric correction for airborne hyperspectral data remains a significant challenge in the field. This study presents a new method for system-level and fine-scale geometric correction of uncontrolled airborne images utilizing DEM data, which integrates forward and inverse transformation algorithms. Furthermore, an optimized workflow is proposed to facilitate the processing of large-scale hyperspectral datasets. The effectiveness of the proposed method is demonstrated through an application analysis using airborne HyMap imagery, with experimental outcomes indicating high application accuracy and enhanced processing efficiency.
Emerging Spatio-temporal Hot Spot Analysis of Beijing Subsidence Trend Detection Based on PS-InSAR
Authors: Zhang Wei , Zhang Tao , Fu Zhengbo , Ai Ping , Yao Guoqing , Qi Jianwei
Published: May 2024

Abstract. Scholars have done a lot of research on urban settlement, but it is difficult to give consideration to the temporal and spatial attributes of settlement at the same time in its display and analysis. Most of them focused on the analysis of regional settlement, single point settlement curve and settlement rate map at a certain time, but few combined time and space for collaborative analysis. Therefore, in this paper, 32 scenes Sentinel-1B SAR data are used to obtain settlement data of Beijing via PS-InSAR method. Secondly, combined with the temporal and spatial attributes of settlement results, the subsidence law revealed by using spatio-temporal cube slicing and attribute filtering. Finally, subsidence development trend and the detection of abnormal subsidence are explored by emerging hot spots (ESH) analysis. The experimental results show that the settlement funnel center in Beijing is mainly concentrated near the junction of Chaoyang district and Tongzhou district. The settlement range tends to expand. There are several local continuous subsidence areas in the settlement oscillating area. Spatio-temporal analysis makes the development trend of urban settlement more intuitive. Emerging hotspot analysis combined with Getis-Ord Gi* statistics and Mann-Kendall trend test could more effectively analyze the settlement trend of the study area and detect new potential settlement centers, so that to provide auxiliary decision-making for urban safety early warning and city development.