All Geospatial Training Courses
at Geo-Learn India

Remote sensing is the science of acquiring, processing, and interpreting satellite and aerial imagery to understand the Earth’s surface — and it is one of the most valuable skills in environmental science, agriculture, forestry, hydrology, and disaster management in India today. This course covers the complete remote sensing pipeline: from the physics of electromagnetic radiation and how satellites capture data, to advanced image classification, spectral analysis, change detection, and accuracy assessment using freely available datasets like Landsat and Sentinel.

Students work with real satellite imagery of Indian landscapes — forests in Odisha, agricultural fields in Punjab, urban areas of Hyderabad, and river systems in the Ganga basin — gaining directly applicable experience in Indian remote sensing contexts.

• Geography, Environmental Science, and Earth Science students and researchers
• Forestry, agriculture, and water resources professionals needing satellite data skills
• Government staff at Forest Survey of India, NRSC, state RS centres
• PhD and M.Phil. scholars needing LULC or change detection for their research
• GIS professionals wanting to add satellite image processing to their skill set

1. 01Principles of Remote SensingElectromagnetic spectrum, energy interactions, active vs passive sensors, resolution types
2. 02Satellite Platforms & SensorsLandsat 8/9, Sentinel-2, IRS ResourceSat, MODIS, SPOT — bands, resolutions, orbits
3. 03Image Acquisition & Data DownloadUSGS EarthExplorer, Copernicus Open Access Hub, Bhuvan — free satellite data download
4. 04Image Display & Band CombinationsTrue color, false color, NIR composites — loading, stretching, and visualizing satellite data
5. 05Image Enhancement & FilteringContrast stretching, histogram equalization, spatial filters — improving image interpretability
6. 06Spectral IndicesNDVI, EVI, SAVI (vegetation) — NDWI, MNDWI (water) — NDBI, NBI (built-up) — NBR (burn)
7. 07Unsupervised ClassificationK-means and ISODATA clustering — setting parameters, labeling classes, refining results
8. 08Supervised ClassificationTraining sample collection, Maximum Likelihood, Random Forest — applying to Indian landscapes
9. 09LULC MappingLand Use / Land Cover map production — class hierarchy, legend standardization, area statistics
10. 10Accuracy AssessmentConfusion matrix, overall accuracy, Kappa coefficient — interpreting and reporting classification quality
11. 11Change Detection AnalysisMulti-date image differencing, post-classification comparison — detecting deforestation, urban growth
12. 12Thermal & SAR Remote Sensing BasicsLand Surface Temperature from Landsat thermal bands — Sentinel-1 SAR for flood mapping
13. 13Capstone ProjectComplete LULC change detection study for a chosen Indian region — classification, accuracy, report

• Vegetation Health MonitoringCalculate and map NDVI for an agricultural district across two seasons to assess crop health and drought stress.
• Urban Expansion AnalysisClassify and compare land cover for an Indian city (2010 vs 2024) to quantify urban sprawl using multi-temporal Landsat data.
• Flood Inundation MappingUse Sentinel-1 SAR and optical imagery to delineate flood extents and calculate affected areas.

QGIS (Quantum GIS) is the world’s most popular free and open-source GIS software — and an essential tool for students, government organizations, NGOs, researchers, and professionals who need professional GIS capability without expensive licensing costs. QGIS is fully capable of performing everything that paid GIS software can do, and its plugin ecosystem extends its functionality even further.

This course gives you complete mastery of QGIS — from installation and basic map production to advanced spatial analysis, database integration, Python scripting via PyQGIS, and plugin usage. Being open source, QGIS is especially popular in government departments, universities, and international development organizations across India.

• Students who cannot afford ArcGIS licenses and want full professional GIS skills
• Government and NGO staff working with open-source GIS tools
• Researchers in academic institutions using QGIS for field data analysis
• Professionals transitioning from ArcGIS to open-source GIS workflows
• Anyone who wants a powerful, free alternative to expensive GIS software

1. 01QGIS Installation & InterfaceDownload, install, configure — panels, toolbars, map canvas, browser — QGIS project files
2. 02Loading Data — All FormatsShapefile, GeoJSON, GeoPackage, CSV, KML, WMS/WFS web services, OSM tiles
3. 03Coordinate Reference Systems in QGISCRS assignment, on-the-fly reprojection, transformations — managing projections for Indian data
4. 04Digitization & EditingCreating new layers, digitizing features, topology checks, advanced editing tools
5. 05Attribute Table & Field CalculatorFiltering, sorting, joining tables, QGIS expressions — calculating new fields and statistics
6. 06Spatial Analysis & GeoprocessingBuffer, clip, intersect, union, dissolve, spatial join — QGIS Processing Toolbox
7. 07Raster Analysis in QGISRaster calculator, terrain analysis (slope, aspect, hillshade, TWI), raster clipping and masking
8. 08Symbology, Labels & Map StylingRule-based rendering, graduated colors, heatmaps, data-driven labels — advanced styling
9. 09Print Layout & AtlasCreating map layouts — legend, scale bar, north arrow — Atlas generation for multi-sheet maps
10. 10QGIS Plugins & EcosystemQuickMapServices, SRTM Downloader, Semi-Automatic Classification Plugin, TimeManager, OpenLayers
11. 11PostGIS Spatial Database IntegrationConnecting QGIS to PostgreSQL/PostGIS — loading, querying, and managing spatial databases
12. 12PyQGIS — Python Automation BasicsQGIS Python console — running scripts, automating geoprocessing, batch operations

Python has become the dominant programming language for geospatial data science, GIS automation, remote sensing analysis, and spatial machine learning. The ability to write Python scripts to read, process, analyze, and visualize spatial data is increasingly expected in GIS job roles, research positions, and geospatial startup environments in India and globally.

This course starts from Python basics (no prior programming required) and progresses through the full ecosystem of geospatial Python libraries — GeoPandas, Shapely, Rasterio, Fiona, Folium, Matplotlib, contextily, PyProj, and more. You will automate GIS workflows that would take hours manually, process satellite imagery programmatically, build interactive web maps, and integrate spatial analysis into data science pipelines — skills that set you apart in the geospatial job market.

• GIS professionals wanting to automate workflows and reduce manual processing time
• Data scientists and developers adding spatial analysis to their Python skill set
• Environmental and remote sensing researchers needing scripted analysis pipelines
• Students targeting geospatial data science or spatial AI roles in India’s tech sector
• Anyone who wants to move beyond GUI-based GIS into programmatic spatial analysis

1. 01Python Foundations for Geospatial WorkVariables, data types, loops, functions, file I/O — Anaconda, Jupyter Notebook, VS Code setup
2. 02NumPy & Pandas for Spatial DataArrays, DataFrames, CSV and Excel data handling — cleaning and preparing tabular geospatial data
3. 03Shapely — Geometric OperationsCreating and manipulating Point, Line, Polygon geometries — spatial relationships, buffers, intersections
4. 04Fiona — Reading & Writing Spatial FilesShapefiles, GeoJSON, GeoPackage — reading, filtering, writing vector formats with Python
5. 05PyProj — Coordinate TransformationsCRS definitions, coordinate reprojection, datum transformations — working with Indian projections
6. 06GeoPandas — Spatial DataFramesGeoDataFrames, spatial joins, dissolve, groupby, overlay — the core of Python GIS analysis
7. 07Spatial Visualization with Matplotlib & contextilyPlotting GeoDataFrames, choropleth maps, adding basemap tiles — publication-quality spatial maps
8. 08Folium — Interactive Web MapsBuilding interactive Leaflet.js maps with Python — popups, markers, choropleth layers, HTML export
9. 09Rasterio — Raster Data ProcessingReading, writing, clipping, reprojecting rasters — raster math, masking, band operations
10. 10Satellite Image Processing with PythonLoading Landsat/Sentinel bands, computing NDVI/NDWI, band compositing, histogram analysis
11. 11Automating GIS WorkflowsBatch processing scripts — automating clip, reproject, calculate, export for 100s of files at once
12. 12Spatial Statistics & Data SciencePoint pattern analysis, kernel density estimation, spatial autocorrelation (Moran’s I) with PySAL
13. 13GeoPandas + Rasterio IntegrationZonal statistics — extracting raster values to vector features using rasterstats
14. 14Capstone ProjectFull Python geospatial analysis pipeline — data processing, analysis, visualization, and web map output