Hi, I'm Abdullah-Al-Faisal, a GIS, Remote Sensing and Environmental Sciences Enthusiastic
I, Abdullah-Al- Faisal, am a young Research Enthusiastic with a focus on geographic information systems (GIS), artificial Intelligence, computer science, machine learning, atmospheric research, climate change, hazard vulnerability, earth and planetary science and remote sensing (RS).
I hold a Master of Science (MSc) in Applied Geographical Information Systems and Remote Sensing from the University of Southampton, where I was awarded the Commonwealth Shared Scholarship for the 2021/22 academic year. I graduated with a grade of 74% with Distinction, positioning myself within the top 3 of my cohort. Additionally, I hold a bachelor’s degree in Urban and Regional Planning from the Rajshahi University of Engineering and Technology in Bangladesh.
My work experience is diverse and includes positions such as Teaching and Research Assistant at McGill University, where I assisted two courses titled Natural Disasters and Environmental Geology (human-Earth System). I am also working on the Surface Earth System Analysis and Modeling Environment (SESAME) project to generate complex human-Earth system datasets using jurisdictional data, remote sensing, and machine learning algorithms. In my role as GIS Specialist at the Operational Centre Amsterdam (OCA) at Médecins Sans Frontières (MSF)/Doctors Without Borders in Bangladesh, I carried out a water and sanitation (WatSan) project based on the Rohingya Refugee Community, using GIS and remote sensing support. I led a GIS Data Collection Team of more than 20 refugees and managed water network planning and the O&A for rapid service planning and action in a humanitarian context.
Furthermore, I served as a Research Consultant at the Center for Environmental and Geographic Information and Services (CEGIS) in the Climate Change and Disaster Management Division. In this role, I managed all GIS and RS related divisional activities by engaging in several national and international climate change (CC) and water management-related projects. The CC Forecasting & Participatory Scenario Development by analyzing and forecasting flash floods using a variety of climatic variables, water logging in a city due to heavy rainfall, sea level rise in coastal region due to climate change, CC risk assessment in roads and low-lying flood potential region, preparation of storm water management plan for Thimphu, Bhutan were among the most sophisticated and exclusive projects I worked on in this short-time period.
I possess excellent technical skills in the use of ArcGIS Enterprise, ArcGIS Pro, ArcGIS Online, ArcGIS Collector, Survey123, Kobo Toolbox, and Python programming language for developing algorithms.
Founder of Dynamic Institute of Geospatial Observation Network - DIGON
A Role Model For Next Generation Information Services
DIGON, an expert oriented dynamic institute, provides high-quality research solutions and information services. Several services such as research paper writing, publication in high impact factor journals (Elsevier, Springer), conferences (both national and international) and book chapters (Taylor and Francis groups), proofreading, reviewing research papers, article writing, case studies, thesis paper writing, proposal writing and statistical supports are providing through DIGON with wide ranging satisfaction.
Skills
GIS & Remote sensing
Disaster risk management and mitigation
Geo-Spatial and Geo-Statistical Analysis
Air pollution concentration analysis
Climate Change analysis
Monitoring, Evaluation and Learning (MEL)
Project Monitoring & Documentation
Field visiting and data collection
Strategic planning and implementation
Strong programming skill in Python – mostly geospatial related libraries.
Google Earth Engine (JavaScript), SQL, Model Builder, ENVI IDL, R programming.
Geo-Spatial and Geo-Statistical Analysis
Remote Sensing; GIS; Web GIS; Climate Change; Disaster Risk Reduction (DRR); Land, Air, Forest and Hydrological Analysis; Deep learning, Photogrammetry, LiDAR, Laser Scanning, Topographic Data Analysis, Environmental analysis, Human-Earth Systems.
Proficiency in ArcGIS, ArcGIS Enterprise, ArcGIS Pro, QGIS, ERDAS Imagine, ENVI, AutoCAD, SPSS, AMOS, Survey Tools (ArcGIS Survey123, Collector, Kobo Toolbox).
Additional software such as AutoCAD, SketchUP, Lumion, After effect, Camtesia Studio, Illustrator, MS Office Package
Developed Algorithms
A deep neural network (DNN) model to estimate Solar Induced Fluorescence (SIF) using other climate variables.
A model to re-grid raster data into a desired resolution (e.g., 1 degree) by considering the weighted sum and mean values of small grid cells, particularly if the x and y cell size are not equal.
An automated conversion of landcover classes to fractions of each class, stored in a 3-dimensional netCDF file.
A distribution of country-wise jurisdictional tabular data to a worldwide gridded and harmo-nized dataset, which can be used for spatial analysis with other climate models, along with several Python functions (i.e., get_netcdf_info, raster_2_ds, merge_ds, and coun-try_selection_based_on_jurisdictional_year).
