GIS & RS Specialist
SAWIE Ecosystems
Total years of experience :3 years, 10 Months
• Automate geospatial analysis processes to enhance efficiency and accuracy. Manage and analyze large datasets, employing R, Python, and Google Earth Engine for data analysis.
• Utilize advanced techniques for Remote Sensing (RS) and Geographic Information Systems (GIS).
• Solve complex geospatial problems by applying a combination of RS and GIS Methodologies.
• Apply advanced machine-learning techniques to extract valuable insights from data.
• Conduct in-depth Geographic Information Systems (GIS) analysis to extract valuable insights from geographic data.
• Specialize in Crop Scan Mapping, leveraging remote sensing techniques for accurate crop health monitoring using NDVI (Normalized Difference Vegetation Index).
• Expertise in cloud removal from imagery to ensure high-quality and cloud-free geospatial data.
• Utilize GIS software and Remote Sensing tools to provide accurate and timely data for decision-making in agriculture-related solutions for a variety of projects.
• Field Mapping for Organic Cotton Mapping.
Play a key role in Cadastral Mapping to maintain accurate land parcel information.
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Model parcel fabric for efficient land record management.
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Contribute to the automation of land records to enhance data accessibility.
Worked on the NSER project for Clusters 2&3, focusing on Linear Feature Digitization.
Digitized linear geographic features to support project goals and data accuracy.
Responsible for creating and managing Enumeration Areas for various projects.
Contributed to the digitization of geographic features to improve data accessibility.
Participated in the creation and management of
Enumeration Areas to support project objectives.
Contributed to the digitization of geographic features for data enhancement.
Weeds Identification Using Image Processing and Deep Learning Techniques Master Thesis of RS & GIS Agriculture Drought Monitoring and Hazard Assessment Using Google Earth Engine for Sindh Site-Specific Soil Management Zoning
Quantification of Green Houses Gases at Urban Level Using Sentinel 5P, A Case Study of Lahore Final Year Project of BS Geo-Informatics Flood Mapping and Damage Assessment Using Sentinel 1 SAR Data in Google Earth Engine for District Layyah Object Base Image Classification for Islamabad Supervise Classification Using Cart Classifier and Google Earth Engine, A Case Study of Islamabad Cotton Yield Forecasting Of District Sahiwal Crime Mapping Using ArcGIS, A Case Study of Boston Forest Gain and Loss Calculation Using Google Earth Engine, A Case Study of Ostergotland Soil and Water Quality Assessment Mapping for District Faisalabad
