Dr. Arif UR Rehman | Remote Sensing Awards | Best Researcher Award
Dr. Arif UR Rehman, Aerospace Information Research Institute, CAS, Pakistan
Suitability for the Best Researcher Award
Based on the provided information, Arif UR Rehman has a strong academic and research background in Remote Sensing, GIS, and Machine Learning Applications in Forestry and Agriculture. His qualifications and achievements make him a potential candidate for the Best Researcher Award, but there are some aspects to consider:
🎓 Education
📍 Master in Forestry (Professional Degree) (2019 – 2021)
- Institution: Beijing Forestry University, China 🇨🇳
- Field: Forest Management
- Final Grade: A+
- Thesis: Feasibility of combining Landsat-8 data with ancillary variables for forest types and land cover classification in mountainous terrains of northern Pakistan
📍 Master of Philosophy (MPhil) in Remote Sensing and GIS (2017 – 2019)
- Institution: PUCIT, University of the Punjab, Pakistan 🇵🇰
- Field: Remote Sensing & GIS
- Final Grade: 72.8%
- Thesis: Remote Sensing & GIS application for monitoring and evaluating afforestation impact – A case study of the Billion Tree Tsunami Project in Peshawar, Pakistan 🌳
📍 Master of Science (MSc) in Geographic Information System (GIS) (2015 – 2017)
- Institution: PUCIT, University of the Punjab, Pakistan 🇵🇰
- Field: GIS
- Final Grade: 73.2%
- Thesis: Analyzing the impacts of deforestation on Land Surface Temperature in Northern Pakistan 🌍🌡️
📍 Post Graduate Diploma (PGD) in Remote Sensing & GIS (2014 – 2015)
- Institution: NCE in Geology, University of Peshawar, Pakistan 🇵🇰
- Field: GIS & Remote Sensing
- Final Grade: 73%
- Thesis: Spatial-Temporal assessment of Land-Use and Land-Cover changes in Lahore 🏙️
📍 Master of Science (MSc) in Electronics (2013 – 2015)
- Institution: University of Peshawar, Pakistan 🇵🇰
- Field: Electronics
- Final Grade: 64%
- Thesis: Developing Hardware & Android software for Outdoor Advertisement Display 📱💡
🏢 Work Experience
🔹 Research Assistant (Feb 2023 – Aug 2024)
- Institution: Aerospace Information Research Institute, Chinese Academy of Sciences 🇨🇳
- Location: Beijing, China
- Key Responsibilities:
- Spatio-Temporal Remote Sensing data acquisition & processing 🛰️
- Developing Machine Learning-based tools for vegetation mapping 🌿🤖
- Scientific Publications 📚
🔹 Remote Sensing Analyst (Jun 2022 – Dec 2022)
- Organization: CABI International; ADB Project: Strengthening Food Security 🇵🇰
- Location: National Agricultural Research Centre (NARC), Islamabad
- Key Responsibilities:
- Capacity development of the Crop Reporting Service Department 🌾
- Google Earth Engine for Crop Classification 🛰️
- Seasonal Crop Classification Maps 📊
🏆 Achievements & Contributions
✔ Published multiple scientific papers in Remote Sensing, GIS, and Forestry journals 📄🔬
✔ Expertise in Google Earth Engine, Machine Learning, GIS & Remote Sensing, and Crop Mapping 🌍🌾
✔ Significant contributions to afforestation projects (Billion Tree Tsunami) 🌳✅
✔ Developed ML-based tools for vegetation mapping and land cover classification 🤖🌎
🎖 Awards & Honors
🏅 A+ Grade in Master’s at Beijing Forestry University (Highest distinction)
🏅 Recognized for contributions to afforestation monitoring in Pakistan 🌲
🏅 Key researcher in major GIS & Remote Sensing projects 📊
Publication Top Notes:
Removal of environmental influences for estimating soil texture fractions based on ZY1 satellite hyperspectral images
Multi-Temporal Sentinel-1 and Sentinel-2 Data for Orchards Discrimination in Khairpur District, Pakistan Using Spectral Separability Analysis and Machine Learning Classification
Estimation of above-ground biomass in dry temperate forests using Sentinel-2 data and random forest: a case study of the Swat area of Pakistan
The role of random forest and Markov chain models in understanding metropolitan urban growth trajectory
Large Scale Fish Images Classification and Localization using Transfer Learning and Localization Aware CNN Architecture
Combining Landsat-8 spectral bands with ancillary variables for land cover classification in mountainous terrains of northern Pakistan
Comparing different space-borne sensors and methods for the retrieval of land surface temperature