NDWI, Transition, Hillshade, Aspect
Hillshading, slope mapping, aspect, net gain and net loss of urban area, extraction of waterbody from satellite imagery using Normalized Difference Vegetation index
About Me
I am a highly experienced GIS and Remote Sensing Specialist with over a decade of professional expertise in geospatial data analysis, satellite imagery processing, and map creation. My work spans a wide range of applications including environmental monitoring, land use and land cover analysis, urban planning, agriculture, disaster management, and climate change impact assessment. Throughout my career, I have developed and implemented advanced spatial analysis workflows, combining the power of remote sensing technologies with geographic information systems to support data-driven decision-making. I am particularly skilled in leveraging Python programming within Jupyter Lab to automate geospatial processes, perform spatial statistics, classify satellite imagery, and generate high-resolution maps. My work often involves integrating raster and vector data, developing custom scripts for data cleaning and transformation, and visualizing spatial patterns with clarity and precision. My technical toolkit includes proficiency in libraries such as GDAL, Rasterio, Geopandas, Shapely, and Matplotlib, along with experience in working with tools like QGIS, Arcgis pro, global mapper, Google Earth engine and ArcGIS for more complex geoprocessing and cartographic tasks. I am adept at handling large-scale datasets, conducting change detection studies, building predictive models using machine learning, and producing publication-quality maps that communicate spatial narratives effectively. With a strong understanding of geospatial standards, coordinate systems, and remote sensing methodologies, I consistently deliver accurate, reliable, and insightful geospatial outputs tailored to the unique needs of each project. My passion lies in turning complex spatial data into actionable intelligence that drives sustainable development and informed planning.
