Jinming yang | Remote sensing disaster detection | Best Researcher Award

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Assist. Prof. Dr. jinming yang | Remote sensing disaster detection | Best Researcher Award

Dr. JinMing Yang is an Assistant Researcher at the Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences. With a Ph.D. in Geography from Xinjiang University, his expertise lies in snow ecohydrology and disaster science in arid regions. His research integrates physical experiments, remote sensing technologies, and mathematical modeling to study snow and avalanche phenomena, focusing on precipitation-ice-snow runoff processes and snow disaster assessment. He has developed advanced remote sensing models for detecting avalanche debris and contributed significantly to understanding the dynamic evolution of mountain disasters. He has published influential articles in SCI-indexed journals, including innovations in automatic avalanche detection using C-band SAR data. He serves as a reviewer for Cold Regions Science and Technology and holds membership in the Xinjiang Natural Resources Society. His scientific contributions are helping to improve snow safety and disaster risk management, making him a prominent figure in his field.

Profile

🎓 Education

Dr. JinMing Yang earned his Ph.D. in 2017 from Xinjiang University, China, majoring in Geography with a specialization in Physical Geography. His doctoral studies provided a strong foundation in environmental and geospatial sciences, with a specific focus on the cryosphere in arid regions. He received intensive training in field-based observation, experimental research, remote sensing data analysis, and mathematical modeling techniques. Throughout his academic career, he has demonstrated a high level of scientific rigor and innovation, especially in integrating electromagnetic spectrum characteristics with snow and avalanche studies. His education combined theoretical depth with applied research, equipping him with the skills to investigate ecohydrological processes, snow accumulation dynamics, and avalanche risk modeling. This academic background not only enhanced his capacity for independent research but also prepared him for interdisciplinary collaborations aimed at understanding and mitigating natural disasters in vulnerable mountainous terrains.

🧪 Experience

Since completing his Ph.D., Dr. JinMing Yang has served as an Assistant Researcher at the Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences. He has accumulated significant experience in snow ecohydrology, particularly within arid and mountainous environments. His work centers on monitoring and modeling snow-related hazards using advanced remote sensing technologies, including SAR data, to improve avalanche detection accuracy. He led a National Natural Science Foundation of China project focused on spatiotemporal evolution of avalanches in the western Tianshan Mountains, employing multi-modal electromagnetic spectrum coupling. Dr. Yang has also contributed to disaster risk assessment frameworks and snow safety strategies, often working with cross-disciplinary teams. His methodological expertise in fusing remote sensing, geostatistics, and simulation models makes him an asset in both academic and practical contexts. Additionally, his editorial and peer-review contributions reflect his growing leadership in the cryosphere and natural hazards research community.

🏅 Awards and Honors

Dr. JinMing Yang has emerged as a notable young researcher in the field of snow disasters and mountain hazard assessment. While specific awards are not listed in the provided information, his nomination for the “Best Researcher Award” highlights his recognized excellence and impact in scientific research. His groundbreaking work on avalanche detection using SAR data and multi-source model integration has earned him reviewer responsibilities for prestigious journals like Cold Regions Science and Technology. His citation index of 29 indicates growing academic recognition, and his project funding from the National Natural Science Foundation of China signifies trust in his expertise and innovative capacity. His active role in professional societies like the Xinjiang Natural Resources Society further reflects his integration into influential academic networks. His contributions are instrumental in shaping practical policies for snow safety in arid mountain environments, positioning him as a strong candidate for national and international research accolades.

🔬 Research Focus

Dr. JinMing Yang’s research is focused on snow ecohydrology and disaster risk modeling in arid and mountainous regions, with snow as the central element. He explores the precipitation-ice-snow runoff process and studies the mechanisms driving avalanche formation and development. A major aspect of his work involves remote sensing-based detection and assessment of snow disasters, particularly the use of SAR data to automate avalanche debris identification. His innovative research has led to the development of independently constructed models combining multi-source variables to quantify avalanche risk spatially and temporally. He also investigates the seasonal dynamics and causal variable influences on avalanche behavior, contributing to mountain safety science. These models offer insights into hazard evolution, aiding in effective disaster prevention and response. His methodology relies heavily on geospatial analysis, electromagnetic spectrum modeling, and simulation techniques, bridging theoretical understanding with real-world applications for environmental safety and planning.

✅ Conclusion

Dr. JinMing Yang stands out as an accomplished researcher whose innovative contributions to snow ecohydrology and remote sensing-based disaster assessment have advanced scientific understanding and practical management of snow-related hazards in arid mountain regions.

Publications

  • Dynamic spatiotemporal quantification of avalanches in the Central Tianshan Mountains by integrating air–space–ground collaborative sensing and snow field–terrain filters

    International Journal of Digital Earth
    2024-12-31 | Journal article
    DOI: 10.1080/17538947.2024.2432525
    Part ofISSN: 1753-8947
    Part ofISSN: 1753-8955
    CONTRIBUTORS: JinMing Yang; LanHai Li; Yang Liu

  • Moderate-resolution snow depth product retrieval from passive microwave brightness data over Xinjiang using machine learning approach

    International Journal of Digital Earth
    2024-12-31 | Journal article
    DOI: 10.1080/17538947.2023.2299208
    Part ofISSN: 1753-8947
    Part ofISSN: 1753-8955
    CONTRIBUTORS: Yang Liu; Jinming Yang; Xi Chen; Junqiang Yao; Lanhai Li; Yubao Qiu

  • Snow avalanche susceptibility mapping from tree-based machine learning approaches in ungauged or poorly-gauged regions

    CATENA
    2023-05 | Journal article
    DOI: 10.1016/j.catena.2023.106997
    Part ofISSN: 0341-8162
    CONTRIBUTORS: Yang Liu; Xi Chen; Jinming Yang; Lanhai Li; Tingting Wang

  • Winter–Spring Prediction of Snow Avalanche Susceptibility Using Optimisation Multi-Source Heterogeneous Factors in the Western Tianshan Mountains, China

    Remote Sensing
    2022-03-10 | Journal article
    DOI: 10.3390/rs14061340
    Part ofISSN: 2072-4292
    CONTRIBUTORS: Jinming Yang; Qing He; Yang Liu

  • Mapping snow avalanche debris by object-based classification in mountainous regions from Sentinel-1 images and causative indices

    CATENA
    2021-11 | Journal article
    DOI: 10.1016/j.catena.2021.105559
    Part ofISSN: 0341-8162
    CONTRIBUTORS: Yang Liu; Xi Chen; Yubao Qiu; Jiansheng Hao; Jinming Yang; Lanhai Li

  • Assimilation of D-InSAR snow depth data by an ensemble Kalman filter

    Arabian Journal of Geosciences
    2021-03 | Journal article
    DOI: 10.1007/s12517-021-06699-y
    Part of ISSN: 1866-7511
    Part of ISSN: 1866-7538
    CONTRIBUTORS: Jinming Yang; Chengzhi Li

zeenat khadim | Remote Sensing | Best Research Article Award

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Dr. zeenat khadim | Remote Sensing | Best Research Article Award

Zeenat Khadim Hussain is a passionate PhD Researcher 🧠 at Wuhan University, China 🇨🇳 specializing in Photogrammetry and Remote Sensing 🌍. With strong analytical skills and innovative thinking 💡, she explores the fusion of Deep Learning 🤖 with geospatial technologies for solving real-world problems 🌱. Her research has made contributions to urban sustainability 🌇, building detection 🏢, and solar energy potential mapping ☀️. Zeenat’s scholarly journey is marked by impactful publications 📚 in reputed journals, advancing cutting-edge solutions for environmental monitoring 🌐. She actively engages in international research collaborations 🤝 and industry-driven projects like Pakistan’s Flood Emergency Reconstruction 🌊. Zeenat remains committed to promoting sustainable development goals (SDGs) through scientific research 📖 and practical applications. Her work reflects dedication to empowering urban resilience 🏙️ and advancing geospatial science 📡 for a smarter and greener future 🌿.

Profile

Education 🎓

Zeenat Khadim Hussain holds an exceptional academic background 🏅. Currently pursuing a PhD in Photogrammetry and Remote Sensing 🛰️ at Wuhan University, China 🇨🇳, her education combines theoretical excellence 📚 and real-world applications 🌍. Prior to this, she achieved commendable academic success in her undergraduate and postgraduate studies 🎓, where she cultivated her passion for geospatial technologies 🔭 and machine learning 💻. Through rigorous training at Wuhan University’s prestigious School of Remote Sensing 🏫, she honed her research methodology, scientific writing ✍️, and computational modeling skills 🧠. Her strong educational foundation underpins her ability to bridge the gap between advanced technology 💡 and environmental sustainability 🌱, positioning her as a forward-thinking researcher 🔬 with global perspectives 🌐. Zeenat’s academic record reflects her commitment to lifelong learning 📖 and excellence in scientific inquiry 🏆.

Experience 👨‍🏫

Zeenat Khadim Hussain has rich research and project experience 🏗️, including 3+ major research projects on urban analysis 🏙️, solar irradiance assessment ☀️, and building footprint extraction 🧱 using deep learning algorithms 🤖 and Sentinel-2 satellite imagery 🛰️. She has also contributed to Pakistan’s Flood Emergency Reconstruction and Resilience Project 🌊, applying her expertise to real-world disaster management 🌐. Zeenat has authored 6 publications in high-impact journals (SCI, Scopus) 📰 and is continuously expanding her research collaborations 🤝. Her commitment to developing AI-powered geospatial solutions 💡 for urban sustainability and disaster resilience 🚧 has made her a rising star 🌟 in remote sensing. From conceptualizing research frameworks 📋 to deploying machine learning pipelines ⚙️, Zeenat’s experience spans both academic and applied research environments 🧪, establishing her as a dynamic and results-oriented researcher 🔬.

Awards & Recognitions 🏅

Zeenat Khadim Hussain’s academic journey is distinguished by multiple honors 🎖️ and global recognition 🌐. Her groundbreaking work on building detection and rooftop solar assessment 🏢☀️ has earned commendation in academic conferences and journal publications 📚. She has been nominated for competitive research awards 🥇 such as the Best Research Scholar Award 🧠 and Excellence in Research 🌟, highlighting her commitment to scientific innovation 🔬. Her practical impact through Pakistan’s Flood Reconstruction Project 🌊 and interdisciplinary collaborations 🤝 showcases her leadership potential and societal contribution 💪. Zeenat’s consistent publication record in reputed platforms 📰 and successful completion of funded research projects 💸 emphasize her growing academic influence 🚀. Her career reflects resilience, intellectual curiosity 🧠, and a passion for using technology to solve environmental and urban challenges 🌍.

Research Interests 🔬

Zeenat Khadim Hussain’s research focuses on the fusion of Deep Learning 🤖, Photogrammetry 📏, and Remote Sensing 🛰️ to tackle urban, environmental, and energy-related challenges 🌍. She specializes in building footprint detection 🏢, rooftop solar potential mapping ☀️, and disaster risk management 🌊 using geospatial data and AI algorithms ⚙️. Her work bridges the gap between theoretical modeling 📐 and real-world applications in sustainable urban planning 🌱 and renewable energy assessment ⚡. She is especially passionate about translating satellite data into actionable insights 🔎 for climate adaptation, resilience, and urban growth 📊. Zeenat’s latest research introduces deep learning architectures 🧠 that enhance rooftop extraction accuracy, as published in Remote Sensing Applications: Society and Environment 🌐. Her aim is to push the frontiers of automated geospatial analysis 🚀, enabling smarter and greener cities 🏙️ for future generations 🌿.

Publications 

  • Spatiotemporal optimization for communication-navigation-sensing collaborated emergency monitoring

    International Journal of Digital Earth
    2025-12-31 | Journal article
    DOI: 10.1080/17538947.2025.2475517
    Part ofISSN: 1753-8947
    Part ofISSN: 1753-8955
    CONTRIBUTORS: xicheng tan; Bocai Liu; Chaopeng Li; Zeenat Khadim Hussain; Kaiqi Wang; Kai Wang; Mengyan Ye; Danyang Yang; Zhiyuan Mei

  • A Novel Architecture for Building Rooftop Extraction Using Remote Sensing and Deep Learning

    Remote Sensing Applications: Society and Environment
    2025-04 | Journal article | Author
    DOI: 10.1016/j.rsase.2025.101551
    Part ofISSN: 2352-9385
    CONTRIBUTORS: zeenat khadim; Jiang Congshi; Muhammad Adrees; Hamza Chaudhary; Rafia Shafqa

  • 3D spatial evolutionary particle swarm algorithm based emergency communication spatial deployment optimization

    Geo-spatial Information Science
    2025-03-11 | Journal article
    DOI: 10.1080/10095020.2025.2472006
    Part of ISSN: 1009-5020
    Part of ISSN: 1993-5153
    CONTRIBUTORS: Yumin Chen; xicheng tan; Jinguang Jiang; Xiaoliang Meng; Zeenat Khadim Hussain; Jianguang Tu; Huaming Wang; You Wan; Zongyao Sha

Alvaro Garcia | Computer vision | Best Researcher Award

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Dr. Alvaro Garcia | Computer vision | Best Researcher Award

Álvaro García Martín es Profesor Titular en la Universidad Autónoma de Madrid, especializado en visión por computadora y análisis de video. 🎓 Obtuvo su título de Ingeniero de Telecomunicación en 2007, su Máster en Ingeniería Informática y Telecomunicaciones en 2009 y su Doctorado en 2013, todos en la Universidad Autónoma de Madrid. 🏫 Ha trabajado en detección de personas, seguimiento de objetos y reconocimiento de eventos, con más de 22 artículos en revistas indexadas y 28 en congresos. 📝 Ha realizado estancias en Carnegie Mellon University, Queen Mary University y Technical University of Berlin. 🌍 Su investigación ha contribuido al desarrollo de sistemas de videovigilancia inteligentes, análisis de secuencias de video y procesamiento de señales multimedia. 📹 Ha sido reconocido con prestigiosos premios y ha participado en múltiples proyectos europeos de innovación tecnológica. 🚀

Profile

Education 🎓

🎓 Ingeniero de Telecomunicación por la Universidad Autónoma de Madrid (2007). 🎓 Máster en Ingeniería Informática y Telecomunicaciones con especialización en Tratamiento de Señales Multimedia en la Universidad Autónoma de Madrid (2009). 🎓 Doctor en Ingeniería Informática y Telecomunicación por la Universidad Autónoma de Madrid (2013). Su formación ha sido complementada con estancias en reconocidas universidades internacionales, incluyendo Carnegie Mellon University (EE.UU.), Queen Mary University (Reino Unido) y la Technical University of Berlin (Alemania). 🌍 Durante su doctorado, recibió la beca FPI-UAM para la realización de su investigación. Su sólida formación académica le ha permitido contribuir significativamente al campo del análisis de video y visión por computadora, consolidándose como un experto en la detección, seguimiento y reconocimiento de eventos en secuencias de video. 📹

Experience 👨‍🏫

🔬 Se unió al grupo VPU-Lab en la Universidad Autónoma de Madrid en 2007. 📡 De 2008 a 2012, fue becario de investigación (FPI-UAM). 🎓 Entre 2012 y 2014, trabajó como Profesor Ayudante. 👨‍🏫 De 2014 a 2019, fue Profesor Ayudante Doctor. 📚 De 2019 a 2023, ocupó el cargo de Profesor Contratado Doctor. 🏛️ Desde septiembre de 2023, es Profesor Titular en la Universidad Autónoma de Madrid. 🏆 Ha participado en múltiples proyectos europeos sobre videovigilancia, transmisión de contenido multimedia y reconocimiento de eventos, incluyendo PROMULTIDIS, ATI@SHIVA, EVENTVIDEO y MobiNetVideo. 🚀 Ha realizado estancias de investigación en Carnegie Mellon University, Queen Mary University y Technical University of Berlin. 🌍 Su experiencia docente abarca asignaturas en Ingeniería de Telecomunicaciones, Ingeniería Informática e Ingeniería Biomédica.

Research Interests 🔬

🎯 Su investigación se centra en la visión por computadora, el análisis de secuencias de video y la inteligencia artificial aplicada a entornos de videovigilancia. 📹 Especialista en detección de personas, seguimiento de objetos y reconocimiento de eventos en video. 🧠 Desarrolla algoritmos de aprendizaje profundo y visión artificial para mejorar la seguridad y automatización en ciudades inteligentes. 🏙️ Ha trabajado en proyectos sobre videovigilancia, transmisión multimedia y detección de anomalías en video. 🔬 Su investigación incluye procesamiento de imágenes, análisis semántico y redes neuronales profundas. 🚀 Participa activamente en proyectos internacionales y colabora con universidades como Carnegie Mellon, Queen Mary y TU Berlin. 🌍 Ha publicado en IEEE Transactions on Intelligent Transportation Systems, Sensors y Pattern Recognition, consolidándose como un referente en el campo de la visión por computadora. 📜

Awards & Recognitions 🏅

🥇 Medalla “Juan López de Peñalver” 2017, otorgada por la Real Academia de Ingeniería. 📜 Reconocimiento por su contribución a la ingeniería española en el campo de la visión por computadora y análisis de video. 🏛️ Ha recibido financiación para múltiples proyectos de investigación europeos y nacionales. 🔬 Ha participado en iniciativas de innovación en videovigilancia y análisis de video para seguridad. 🚀 Sus contribuciones han sido publicadas en las principales conferencias y revistas científicas del área. 📚 Su trabajo ha sido citado más de 4500 veces y cuenta con un índice h de 16 en Google Scholar. 📊

Publications 

1. Rafael Martín-Nieto, Álvaro García-Martín, Alexander G. Hauptmann, and Jose. M.
Martínez: “Automatic vacant parking places management system using multicamera
vehicle detection”. IEEE Transactions on Intelligent Transportation Systems, Volume 20,
Issue 3, pp. 1069-1080, ISSN 1524-9050, March 2019.

2. Rafael Martín-Nieto, Álvaro García-Martín, Jose. M. Martínez, and Juan C. SanMiguel:
“Enhancing multi-camera people detection by online automatic parametrization using
detection transfer and self-correlation maximization”. Sensors, Volume 18, Issue 12, ISSN
1424-8220, December 2018.

3. Álvaro García-Martín, Juan C. SanMiguel and Jose. M. Martínez: “Coarse-to-fine adaptive
people detection for video sequences by maximizing mutual information”. Sensors,
Volume 19, Issue 4, ISSN 1424-8220, January 2019.

4. Alejandro López-Cifuentes, Marcos Escudero-Viñolo, Jesús Bescós and Álvaro GarcíaMartín: “Semantic-Aware Scene Recognition”. Pattern Recognition. Accepted February
2020.

5. Paula Moral, Álvaro García-Martín, Marcos Escudero Viñolo, Jose M. Martinez, Jesus
Bescós, Jesus Peñuela, Juan Carlos Martinez, Gonzalo Alvis: “Towards automatic waste
containers management in cities via computer vision: containers localization and geopositioning in city maps”. Waste Management, June 2022.

6. Javier Montalvo, Álvaro García-Martín, Jesus Bescós: “Exploiting Semantic Segmentation
to Boost Reinforcement Learning in Video Game Environments”. Multimedia Tools and
Applications. September 2022.

7. Paula Moral, Álvaro García-Martín, Jose M. Martinez, Jesus Bescós: “Enhancing Vehicle
Re-Identification Via Synthetic Training Datasets and Re-ranking Based on Video-Clips
Information”. Multimedia Tools and Applications. February 2023.

8. Roberto Alcover-Couso, Juan C. SanMiguel, Marcos Escudero-Viñolo and Alvaro GarciaMartin: “On exploring weakly supervised domain adaptation strategies for semantic
segmentation using synthetic data”. Multimedia Tools and Applications. February 2023.

9. Juan Ignacio Bravo Pérez-Villar, Álvaro García-Martín, Jesús Bescós, Marcos EscuderoViñolo: “Spacecraft Pose Estimation: Robust 2D and 3D-Structural Losses and
Unsupervised Domain Adaptation by Inter-Model Consensus”. IEEE Transactions on
Aerospace and Electronic Systems. August 2023.

10. Javier Montalvo, Álvaro García-Martín, José M. Martinez. “An Image-Processing Toolkit
for Remote Photoplethysmography”, Multimedia Tools and Applications. July 2024.

11. Juan Ignacio Bravo Pérez-Villar, Álvaro García-Martín, Jesús Bescós, Juan C. SanMiguel:
“Test-Time Adaptation for Keypoint-Based Spacecraft Pose Estimation Based on
Predicted-View Synthesis”. IEEE Transactions on Aerospace and Electronic Systems.
May 2024.

12. Kirill Sirotkin, Marcos Escudero-Viñolo, Pablo Carballeira, Álvaro García-Martín:
“Improved Transferability of Self-Supervised Learning Models Through Batch
Normalization Finetuning”. Applied Intelligence. Aug 2024.

13. Javier Galán, Miguel González, Paula Moral, Álvaro García-Martín, Jose M. Martinez:
“Transforming Urban Waste Collection Inventory: AI-Based Container Classification and
Re-Identification”. Waste Management, Feb 2025.