Dr. Abhijit Das | Remote Sensing and Satellite Imagery Analysis | India

Published on by Computer Vision Awards
After Completing the Registration Process, your Biography will be displayed here.

Register by using your Entry ID: 8443

Register Here

Ibraheem Yousif | Remote Sensing and Satellite Imagery Analysis | Excellence in Research

Published on by Computer Vision Awards

Prof. Ibraheem Yousif | Remote Sensing and Satellite Imagery Analysis | Excellence in Research

Cairo University | Egypt

Dr. Ibraheem A. H. Yousif is a Professor of Soil Science at Cairo University, specializing in pedology, GIS, and remote sensing applications in agriculture. He has authored over 15 peer-reviewed publications, receiving more than 81 citations with an h-index of 7, reflecting a growing international research impact. His work focuses on desertification assessment, soil quality evaluation, and sustainable land management in arid regions. Dr. Yousif has collaborated with diverse international research teams and contributed to large-scale land development projects. His research supports climate resilience, food security, and evidence-based agricultural planning in environmentally vulnerable regions.

Citation Metrics (Scopus)

150

100

50

0

Citations
81

Documents
15

h-index
7

🟦 Citations 🟥 Documents 🟩 h-index

View Scopus Profile    View ORCID Profile
     View Google Scholar  Profile

Featured Publications


Land capability and suitability mapping in some areas of North-Western Coast, Egypt.

–Journal of Soil Sciences and Agricultural Engineering, Mansoura University, 9(3), 111–118. (2018). Cited By: 18

Integration of land cover changes and land capability of Wadi El-Natrun depression using vegetation indices.

– Egyptian Journal of Soil Science, 59(4), 385–402. (2019). Cited By: 10

Geostatistical approach for land suitability assessment of some desert soils.

– Egyptian Journal of Soil Science, 60(3), 195–209. (2020). Cited By: 35

Tian Gao | Remote Sensing and Satellite Imagery Analysis | Research Excellence Award

Published on by Computer Vision Awards

Dr. Tian Gao | Remote Sensing and Satellite Imagery Analysis | Research Excellence Award

The Information Engineering University | China

Dr. Tian Gao is a distinguished researcher in the field of remote sensing, specializing in multimodal image matching, Arctic sea ice motion analysis, and image registration for optical and SAR imagery. He completed his graduate studies at PLA Information Engineering University, Zhengzhou, China, focusing on geospatial information and advanced computational methods for Earth observation.Gao has authored 11 peer-reviewed publications, including in top-tier journals such as IEEE Sensors Journal, ISPRS Journal of Photogrammetry and Remote Sensing, and the International Journal of Applied Earth Observation and Geoinformation. His notable contributions include the development of SFA-Net, a SAM-guided focused attention network for multimodal remote sensing image matching, and innovative approaches to sharpened side phase fusion and self-similar adjacent self-convolutional feature registration. Gao’s work also encompasses keypoint-free feature tracking for Arctic sea ice motion retrieval, DEM super-resolution using attention-based and relative depth-guided methods, and GNSS-denied UAV geolocalization. These efforts have advanced both methodological innovation and practical applications in environmental monitoring, geospatial intelligence and disaster response.His research demonstrates extensive collaboration with domestic and international scholars, reflecting interdisciplinary engagement across remote sensing, UAV imaging, and geospatial data analysis. Gao’s publications have collectively received 51 citations, highlighting the growing impact of his work in the scientific community.Beyond methodological contributions Gao’s work has significant societal and environmental relevance enabling improved monitoring of polar ice dynamics, enhancing emergency response through UAV-assisted image stitching and supporting sustainable geospatial intelligence applications. With expertise spanning optical and SAR imagery multimodal data fusion and image registration, Tian Gao continues to contribute to cutting-edge research that bridges academic innovation with real-world solutions in Earth observation and remote sensing.

Profiles: ORCID | Scopus

Featured Publications

1.Wang, Y., Lan, C., Gao, T., Yao, F., & Mu, Z. (2025). Multimodal image matching using sharpened side phase fusion method. IEEE Sensors Journal.

2.Gao, T., Lan, C., Lv, L., Shi, Q., Huang, W., Wang, Y., & Mu, Z. (2025). Robust registration of multimodal remote sensing images using self-similar adjacent self-convolutional feature. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

3.Gao, T., Lan, C., Zhou, C., Zhang, Y., Huang, W., Wang, L., & Wang, Y. (2025). Arctic sea ice motion retrieval from multisource SAR images using a keypoint-free feature tracking algorithm. ISPRS Journal of Photogrammetry and Remote Sensing.  Cited By: 1

4.Huang, W., Sun, Q., Guo, W., Xu, Q., Wen, B., Gao, T., & Yu, A. (2025). Multi-modal DEM super-resolution using relative depth: A new benchmark and beyond. International Journal of Applied Earth Observation and Geoinformation.

5.Gao, T., Lan, C., Huang, W., & Wang, S. (2025). SFA-Net: A SAM-guided focused attention network for multimodal remote sensing image matching. ISPRS Journal of Photogrammetry and Remote Sensing.

Tian Gao’s research advances remote sensing and multimodal image analysis, enabling precise monitoring of Arctic sea ice, GNSS-denied UAV navigation, and environmental changes. His work bridges scientific innovation with practical applications, supporting disaster response, geospatial intelligence, and sustainable environmental management globally.

Mona Maze | Land Classification | Best Researcher Award

Published on by Computer Vision Awards

Assoc. Prof. Dr. Mona Maze | Land Classification | Best Researcher Award

Senior Researcher | Agricultural Research Center | Egypt

Dr. Mona Maze is a dedicated researcher specializing in agricultural climate, plant nutrition, and digital agriculture, with a strong focus on developing climate change adaptation strategies, precision farming approaches, and the use of remote sensing and machine learning in agriculture. She earned her PhD in Plant Nutrition from the Technical University of Munich, where her doctoral work addressed crop growth and yield modeling under water scarcity and changing climatic conditions. Over her professional career, she has actively contributed to national and international research projects in collaboration with institutions such as the European Commission, USAID, UNDP, and FAO, while also leading initiatives like the Digital Dynamic Agricultural Map of Egypt and Early Warning Systems for farmers. Her teaching experience and supervision of graduate students reflect her commitment to academic development, while her publication record in reputed journals such as Scientific Reports, ISPRS Journal of Photogrammetry and Remote Sensing, Agronomy, and Energies highlights her strong scientific contributions. Her research interests span climate-smart agriculture, soil fertility, plant nutrition, digital transformation in agriculture, and data-driven solutions for food security. She possesses advanced research skills in machine learning, deep learning, geospatial data analysis, crop modeling, and experimental design, complemented by professional certifications in business management, spatial data science, and AI-based systems. She has 54 citations by 54 documents, 11 publications, and an h-index of 4, reflecting her growing impact in the scientific community.

Profiles: Scopus | ORCID

Featured Publications

  1. Maze, M., Attaher, S., Taqi, M. O., Elsawy, R., Gad El-Moula, M. M. H., Hashem, F. A., & Moussa, A. S. (2025). Enhanced agricultural land use/land cover classification in the Nile Delta using Sentinel-1 and Sentinel-2 data and machine learning. ISPRS Journal of Photogrammetry and Remote Sensing.

  2. Salah, M., Maze, M., & Tonbol, K. (2024). Intersecting vulnerabilities: Climate justice, gender inequality, and COVID-19’s impact on rural women in Egypt. Multidisciplinary Adaptive Climate Insights.

  3. Maze, M., Taqi, M. O., Tolba, R., Abdel-Wareth, A. A. A., & Lohakare, J. (2024). Estimation of methane greenhouse gas emissions from livestock in Egypt during 1989 to 2021. Scientific Reports.

  4. El-Beltagi, H. S., Hashem, F. A., Maze, M., Shalaby, T. A., Shehata, W. F., & Taha, N. M. (2022). Control of gas emissions (N₂O and CO₂) associated with applied different rates of nitrogen and their influences on growth, productivity, and physio-biochemical attributes of green bean plants grown under different irrigation methods. Agronomy, 12(2), 249.

  5. Abd El-Fattah, D. A., Maze, M., Ali, B. A. A., & Awed, N. M. (2022). Role of mycorrhizae in enhancing the economic revenue of water and phosphorus use efficiency in sweet corn (Zea mays L. var. saccharata) plants. Journal of the Saudi Society of Agricultural Sciences.

Lipeng Jiao | Vegetation Disturbance Detection | Best Researcher Award

Published on by Computer Vision Awards

Dr. Lipeng Jiao | Vegetation Disturbance Detection | Best Researcher Award

Lecturer at Henan Normal University | China

Lipeng Jiao is a dedicated researcher and academic specializing in deep learning-based remote sensing, with a strong focus on vegetation time-series modeling and disturbance detection. Currently serving as a lecturer at the School of Tourism, Henan Normal University, China, he has developed expertise in integrating advanced computational methods with environmental monitoring and ecological analysis. His career reflects a balance of theoretical knowledge and practical applications, demonstrated by his active role in large-scale national research projects and collaborations with international institutions. With publications in highly regarded journals such as IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing and GIScience & Remote Sensing, he has established himself as a promising scholar in his field. His research contributions address global environmental challenges, particularly in sustainable land use and ecological monitoring. Through his work, he continues to contribute to both academic advancement and societal well-being.

Professional Profiles 

Scopus Profile | ORCID Profile 

Education

Lipeng Jiao has pursued a strong educational foundation in surveying, mapping, and geographic information systems, building a career rooted in both technical depth and interdisciplinary applications. He earned his bachelor’s degree in surveying and mapping engineering from Shangqiu Normal University, which provided him with the fundamental skills for spatial data analysis and geoscience research. He further advanced his expertise with a master’s degree in surveying and mapping engineering from the China University of Mining and Technology in Beijing, where he specialized in advanced mapping technologies and environmental data interpretation. He then completed his doctoral studies in cartography and geographic information systems at Beijing Normal University, focusing on remote sensing and ecological monitoring. In addition to his domestic education, he broadened his academic perspective through an international visiting scholar program at Virginia Tech in the United States, where he collaborated on advanced research in vegetation dynamics and remote sensing applications.

Professional Experience

Lipeng Jiao is currently serving as a lecturer at the School of Tourism, Henan Normal University, where he is actively engaged in teaching, research, and mentoring students in areas related to remote sensing and environmental studies. His professional journey is marked by extensive involvement in major research initiatives, including participation in national key research and development programs in China. He has contributed to projects that focus on global remote sensing monitoring, land use change, and ecological simulations, establishing himself as an integral member of multidisciplinary research teams. His international exposure as a visiting scholar at Virginia Tech in the United States allowed him to collaborate with leading experts and enhance his research perspective. In addition to his teaching and research responsibilities, he actively contributes to the dissemination of knowledge through publications in recognized journals. His professional experience reflects a commitment to combining scientific innovation with practical applications in environmental sustainability.

Research Interest

Lipeng Jiao’s research interests are centered on the application of deep learning techniques in remote sensing, with a particular emphasis on vegetation time-series modeling and the detection of ecological disturbances. He is passionate about developing advanced computational methods that can improve the monitoring and interpretation of environmental changes across diverse ecosystems. His studies focus on vegetation disturbance detection, attribution of change agents, and mapping of ecological processes, which are critical for understanding the impacts of climate change and human activities on natural resources. He is also interested in synergizing multi-source satellite data to achieve near real-time monitoring of phenomena such as burned areas and vegetation degradation. By integrating cutting-edge artificial intelligence methods with remote sensing data, his research contributes to the improvement of global ecological monitoring systems. His interests extend toward practical applications, aiming to support sustainable resource management and policy-making for environmental conservation.

Research Skill

Lipeng Jiao possesses a diverse set of research skills that enable him to address complex challenges in remote sensing and environmental monitoring. He is proficient in applying deep learning algorithms to process and analyze large-scale vegetation time-series data, allowing for the detection and attribution of ecological disturbances with high accuracy. His expertise extends to multi-source satellite data integration, enhancing the capability to conduct near real-time environmental assessments. He is skilled in geographic information systems, cartography, and advanced data analysis methods that support spatial and temporal modeling. His contributions to national research projects highlight his ability to work within interdisciplinary teams, manage data-intensive tasks, and produce impactful outcomes. Additionally, his international research exposure has strengthened his adaptability to diverse scientific approaches and collaborative environments. These skills position him as a researcher capable of advancing both theoretical innovations and practical applications in ecological monitoring and sustainability science.

Publications Top Notes

Title: Robust Identification of Vegetation Change Using Shapelet-Based Temporal Segmentation of Landsat Time-Series Stacks: A Case Study in the Qilian Mountains
Authors: Lipeng Jiao; Randolph H. Wynne
Year: 2025

Title: Near real-time mapping of burned area by synergizing multiple satellites remote-sensing data
Authors: Lipeng Jiao; Yanchen Bo
Year: 2022

Conclusion

Lipeng Jiao is a deserving candidate for the Best Researcher Award due to his significant contributions in applying deep learning to vegetation remote sensing, advancing the understanding of ecological changes and land use impacts. His work on vegetation disturbance detection, participation in major research projects, and high-quality publications demonstrate both scientific excellence and societal relevance. With his strong research foundation, international experience, and potential for leadership in collaborative and innovative projects, he is well-positioned to continue making impactful contributions to his field and the broader research community.

 

 

Alexandru Dandocsi | Remote Sensing | Best Researcher Award

Published on by Computer Vision Awards

Dr. Alexandru Dandocsi | Remote Sensing | Best Researcher Award 

Recognised Research at National Institute of Research and Development for Optoelectronics – INOE2000, Romania

Dr. Alexandru Marius Dandocsi is a leading researcher in atmospheric science with expertise in passive remote sensing, Earth observation, and environmental data analysis. He has worked across top institutions including the National Institute of Research and Development for Optoelectronics, the European Space Agency, and the European Commission. His work spans scientific algorithm development, satellite validation, and the integration of remote sensing data into environmental policy frameworks. He has contributed to key European initiatives such as Horizon Europe and the European Green Deal, and is a core member of ACTRIS and other global research infrastructures. Dr. Dandocsi has participated in numerous international field campaigns, workshops, and scientific conferences, showcasing both his technical skills and leadership capabilities. With an impressive list of peer-reviewed publications and collaborative projects, he stands out as a multidisciplinary scientist whose work bridges science, technology, and policy. His career reflects a strong commitment to research excellence and global environmental advancement.

Professional Profile 

Google Scholar | Scopus Profile | ORCID Profile 

Education

Dr. Alexandru Dandocsi completed his higher education in Romania, earning his PhD in Physics from the University Politehnica of Bucharest. His doctoral research focused on passive remote sensing methods for the retrieval of atmospheric gas concentrations, using instruments such as FTIR and MAX-DOAS. His thesis, written in Romanian, explored advanced methodologies to measure gas density in the atmosphere using optical remote sensing techniques. He graduated with the highest distinction, summa cum laude, which reflects his academic excellence and deep engagement with his field. His education laid a strong foundation in both theoretical and applied aspects of atmospheric physics and satellite remote sensing. Throughout his academic journey, he developed expertise in data analysis, radiative transfer modeling, and algorithm development. His educational background has been integral to his contributions to international research projects and policy-supporting scientific assessments. Dr. Dandocsi’s academic training continues to drive his innovative work in environmental monitoring and climate science.

Professional Experience

Dr. Dandocsi’s professional experience spans over a decade in high-impact roles within leading scientific institutions in Romania and across Europe. He began his career at the National Institute of Research and Development for Optoelectronics as a junior scientist, where he developed strong technical skills in operating and analyzing data from passive remote sensing instruments. He later served as a Research Fellow at the European Space Agency, where he contributed to satellite mission support, algorithm development, and coordination of scientific activities under ESA’s atmospheric research initiatives. His appointment as a Seconded National Expert at the European Commission further solidified his influence, allowing him to shape environmental policy and research agendas in alignment with EU space and sustainability objectives. Currently, he is a Recognised Researcher continuing his scientific contributions at the national and international level. Dr. Dandocsi’s career reflects a continuous upward trajectory marked by collaboration, innovation, and service to scientific advancement and environmental governance.

Research Interest

Dr. Alexandru Dandocsi’s research interests lie at the intersection of atmospheric science, environmental monitoring, and remote sensing technology. He is particularly focused on the development and validation of retrieval algorithms for passive remote sensing instruments, such as FTIR, MAX-DOAS, and solar/lunar photometers. He actively works on the integration of satellite-based data with ground-based observations to improve the accuracy of atmospheric gas measurements and aerosol characterization. His interests extend to studying the impact of human activities on air quality and climate, including assessments of greenhouse gas emissions and aerosol dynamics. He is also involved in developing tools for the calibration and validation of satellite missions such as Sentinel-5P and Aeolus, contributing to global networks like ACTRIS and EuroGEO. His interdisciplinary work bridges environmental science, physics, and data analysis, offering valuable insights for climate policy and scientific research. His work supports initiatives such as the European Green Deal and Horizon Europe.

Award and Honor

Dr. Alexandru Dandocsi has earned recognition within the scientific community for his contributions to Earth observation and atmospheric research. These competitive and prestigious positions are granted only to highly skilled scientists with proven research excellence and cross-disciplinary collaboration experience. His role in helping shape EU research and innovation policies related to the environment further highlights the trust placed in his scientific expertise. Additionally, his invitation to present at numerous international conferences and his involvement in ESA and Horizon Europe projects underline the respect he commands in the scientific field. His publications in high-impact journals and collaborations with international networks also speak to the recognition and value of his ongoing research contributions.

Research Skill

Dr. Dandocsi possesses a broad and advanced range of research skills developed through years of experience in satellite remote sensing and atmospheric science. He is proficient in programming languages such as Python and MATLAB, which he uses extensively for data analysis, algorithm development, and modeling. His technical expertise includes operating complex ground-based instruments such as FTIR, MAX-DOAS, and sun photometers, as well as analyzing data from satellite missions like TROPOMI and Aeolus. He has experience with designing and implementing scientific field campaigns, including technical preparations and instrument deployment. Dr. Dandocsi is also skilled in the development of calibration and validation procedures for remote sensing products, quality assurance assessment of scientific data, and interpretation of multi-source environmental observations. His ability to work with large datasets, combined with his understanding of atmospheric physics, makes him highly effective in applied research. He also demonstrates strong communication, problem-solving, and teamwork skills in cross-border collaborative projects.

Publications Top Notes

Title: Quantifying CH₄ emissions from hard coal mines using mobile sun-viewing Fourier transform spectrometry
Authors: A. Luther, R. Kleinschek, L. Scheidweiler, S. Defratyka, M. Stanisavljevic, …
Year: 2019
Citations: 70

Title: Multiyear typology of long-range transported aerosols over Europe
Authors: V. Nicolae, C. Talianu, S. Andrei, B. Antonescu, D. Ene, D. Nicolae, …
Year: 2019
Citations: 28

Title: Observational constraints on methane emissions from Polish coal mines using a ground-based remote sensing network
Authors: A. Luther, J. Kostinek, R. Kleinschek, S. Defratyka, M. Stanisavljević, …
Year: 2022
Citations: 27

Title: Online Chemical Characterization and Source Identification of Summer and Winter Aerosols in Măgurele, Romania
Authors: L. Mărmureanu, J. Vasilescu, J. Slowik, A.S.H. Prévôt, C.A. Marin, B. Antonescu, …
Year: 2020
Citations: 20

Title: Wintertime variations of gaseous atmospheric constituents in Bucharest peri-Urban area
Authors: C.A. Marin, L. Mărmureanu, C. Radu, A. Dandocsi, C. Stan, F. Ţoancă, …
Year: 2019
Citations: 15

Title: High potential for CH₄ emission mitigation from oil infrastructure in one of EU’s major production regions
Authors: F. Stavropoulou, K. Vinković, B. Kers, M. de Vries, S. van Heuven, P. Korbeń, …
Year: 2023
Citations: 14

Title: Extended aerosol and surface characterization from S5P/TROPOMI with GRASP algorithm. Part I: Conditions, approaches, performance and new possibilities
Authors: P. Litvinov, C. Chen, O. Dubovik, L. Bindreiter, C. Matar, D. Fuertes, A. Lopatin, …
Year: 2024
Citations: 8

Title: The impact of using assimilated Aeolus wind data on regional WRF-Chem dust simulations
Authors: P. Kiriakidis, A. Gkikas, G. Papangelis, T. Christoudias, J. Kushta, …
Year: 2023
Citations: 8

Title: Independent retrieval of aerosol type from lidar
Authors: D. Nicolae, J. Vasilescu, C. Talianu, A. Dandocsi
Year: 2016
Citations: 7

Title: Extended aerosol and surface characterization from S5P/TROPOMI with GRASP algorithm. Part II: Global validation and Intercomparison
Authors: C. Chen, P. Litvinov, O. Dubovik, L. Bindreiter, C. Matar, D. Fuertes, A. Lopatin, …
Year: 2024
Citations: 6

Conclusion

Dr. Alexandru Marius Dandocsi is a highly deserving candidate for the Best Researcher Award due to his outstanding contributions to atmospheric science and Earth observation. His work on passive remote sensing, satellite validation, and environmental monitoring has significantly advanced scientific understanding and supported critical environmental policies at the European level. Through active participation in international research initiatives and impactful publications, he has demonstrated both academic excellence and societal relevance. With his proven expertise, collaborative mindset, and growing leadership experience, Dr. Dandocsi holds strong potential to drive future innovations and play a key role in shaping global environmental research and policy.

Mr. Zitong Wen | Remote Sensing | Best Researcher Award

Published on by Computer Vision Awards

Mr. Zitong Wen | Remote Sensing | Best Researcher Award

University of Bristol, United Kingdom

👨‍🎓 Profiles

Scopus

Orcid

Google Scholar

Publications

How can we improve data integration to enhance urban air temperature estimations?

  • Author: Zitong Wen, Lu Zhuo, Meiling Gao, Dawei Han
  • Journal: International Journal of Applied Earth Observation and Geoinformation
  • Year: 2025

Potential methods to improve urban air temperature estimations

  • Author: Zitong Wen, Lu Zhuo, Dawei Han
  • Journal: AGU Fall Meeting Abstracts
  • Year: 2024

Data fusion for estimating high-resolution urban heatwave air temperature

  • Author: Zitong Wen, Lu Zhuo, Qin Wang, Jiao Wang, Ying Liu, Sichan Du, Ahmed Abdelhalim, Dawei Han
  • Journal: Remote Sensing
  • Year: 2023

Estimating air temperature with high spatio-temporal resolution in urban areas during heatwaves using genetic programming algorithm combined with multi-source datasets

  • Author: Zitong Wen, Lu Zhuo, Qin Wang, Dawei Han
  • Journal: EGU General Assembly Conference Abstracts
  • Year: 2023

Rapid Flood inundation mapping using SAR data with Google Earth Engine cloud platform

  • Author: Qin Wang, Lu Zhuo, Chen Li, Miguel Rico-Ramirez, Zitong Wen, Dawei Han
  • Journal: EGU General Assembly Conference Abstracts
  • Year: 2023

Prof. Yongil Kim | Remote Sensing | Best Paper Award

Published on by Computer Vision Awards

Prof. Yongil Kim | Remote Sensing | Best Paper Award

Professor at Seoul National University, South Korea

👨‍🎓 Profiles

Scopus

Publications

Unsupervised Image Super-Resolution for High-Resolution Satellite Imagery via Omnidirectional Real-to-Synthetic Domain Translation

  • Authors: Minkyung Chung, Yongil I. Kim
    Journal: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
    Year: 2025

Analysis of Urban Vitality in Democratic People’s Republic of Korea Using Geospatial Data and Nighttime Light Satellite Imagery

  • Authors: Yeseok Lee, Donghyeon Lee, Jiyong Kim, Yongil I. Kim
    Journal: Korean Journal of Remote Sensing
    Year: 2024

Optimal Hyperparameter Analysis of Segment Anything Model for Building Extraction Using KOMPSAT-3/3A Images

  • Authors: Donghyeon Lee, Jiyong Kim, Yongil I. Kim
    Journal: Korean Journal of Remote Sensing
    Year: 2024

Integrated Framework for Unsupervised Building Segmentation with Segment Anything Model-Based Pseudo-Labeling and Weakly Supervised Learning

  • Authors: Jiyong Kim, Yongil I. Kim
    Journal: Remote Sensing
    Year: 2024

High-Resolution Satellite Image Super-Resolution Using Image Degradation Model with MTF-Based Filters

  • Authors: Minkyung Chung, Minyoung Jung, Yongil I. Kim
    Journal: Korean Journal of Remote Sensing
    Year: 2023

Dr. Mingxi Zhang | Remote Sensing | Best Researcher Award

Published on by Computer Vision Awards

Dr. Mingxi Zhang | Remote Sensing | Best Researcher Award

Doctorate at Curtin University, Australia

👨‍🎓 Profiles

Google Scholar

Publications

A warming climate will make Australian soil a net emitter of atmospheric CO2

  • Authors: RA Viscarra Rossel, M Zhang, T Behrens, R Webster
  • Journal: npj Climate and Atmospheric Science
  • Year: 2024

Mid-infrared spectroscopy determines the provenance of coastal marine soils and their organic and inorganic carbon content

  • Authors: Lewis Walden, Oscar Serrano, Zefang Shen, Mingxi Zhang, Paul Lavery, Zhongkui Luo, Lei Gao, Raphael A Viscarra Rossel
  • Journal: Science of The Total Environment
  • Year: 2024

Estimating nutrient transport associated with water and wind erosion across New South Wales, Australia

  • Authors: Xihua Yang, John Leys, Mingxi Zhang, Jonathan M Gray
  • Journal: Geoderma
  • Year: 2023

Monitoring of sustainable land management using remotely sensed vegetation cover and variable tolerable soil erosion targets across New South Wales, Australia

  • Authors: Jonathan M Gray, John F Leys, Xihua Yang, Mingxi Zhang
  • Journal: Soil Use and Management
  • Year: 2023

Deep transfer learning of global spectra for local soil carbon monitoring

  • Authors: Zefang Shen, Leonardo Ramirez-Lopez, Thorsten Behrens, Lei Cui, Mingxi Zhang, Lewis Walden, Johanna Wetterlind, Zhou Shi, Kenneth A Sudduth, Philipp Baumann, Yongze Song, Kevin Catambay, Raphael A Viscarra Rossel
  • Journal: ISPRS Journal of Photogrammetry and Remote Sensing
  • Year: 2022

Dr. Ali Hosingholizade | Remote Sensing | Best Researcher Award

Published on by Computer Vision Awards

Dr. Ali Hosingholizade | Remote Sensing | Best Researcher Award

Doctorate at university of Tehran, Iran

👨‍🎓 Profiles

Scopus

Google Scholar

Publications

Assessment of Pine Tree Crown Delineation Algorithms on UAV Data: From K-Means Clustering to CNN Segmentation

  • Authors: Ali Hosingholizade, Yousef Erfanifard, Seyed Kazem Alavipanah, Virginia Elena Garcia Millan, Miłosz Mielcarek, Saied Pirasteh, Krzysztof Stereńczak
  • Journal: Forests
  • Year: 2025

Integration of weighted majority voting in machine learning algorithms to enhance pine tree crown mapping on UAV imagery

  • Authors: Ali Hosingholizade, Yousef Erfanifard, Seyed Kazem Alavipanah, Saied Pirasteh, Virginia Garcia Millan
  • Journal: ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
  • Year: 2024

Height estimation of pine (Pinus eldarica) single trees using slope corrected shadow length on unmanned aerial vehicle (UAV) imagery in a plantation forest

  • Authors: Ali Hosingholizade, Yousef Erfanifard, Seyed Kazem Alavipanah, Hooman Latifi, Yaser Jouybari-Moghaddam
  • Journal: Annals of Forest Research
  • Year: 2023

Tree Crown Delineation on Uav Imagery Using Combination of Machine Learning Algorithms with Majority Voting

  • Authors: A Hosingholizade, Y Erfanifard, SK Alavipanah, H Latifi, Y Jouybari-Moghaddam
  • Journal: ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
  • Year: 2025

Investigation of linear and logarithmic regression between measured and calculated parameters of Eldarica pine tree

  • Authors: Ali Hosingholizade
  • Journal: Intercontinental Geoinformation Days
  • Year: 2023