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Remote Sensing & GIS · founded & taught by Nitesh

Every watershed starts at a ridge.
Every GIS skillset starts here.

MapCraft GIS trains hydrologists, foresters and geographers to turn satellite imagery and terrain models into answers that hold up in the field — SAR flood maps, groundwater models, watershed delineations, built the way they're actually built on real projects.

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About

MapCraft GIS is run by Nitesh, a Remote Sensing and GIS practitioner who works hands-on with the same tools he teaches — SAR flood inundation mapping, groundwater modeling, watershed delineation, and hydraulic simulation across the field areas he trains on.

Courses grow out of real project work rather than slide decks: a lecture is built only after the workflow has been run end-to-end on an actual dataset, from a HEC-RAS dam-break study on the Beas River to Sentinel-1 flood mapping over Punjab. That "learn it, build it, then teach it" discipline is what shapes every MapCraft syllabus.

Batch-based training Bilingual — Hindi + English Project capstones Certificate on completion
PGD Remote Sensing & GIS
IIRS-ISRO, Water Resources specialization
M.Sc. Environmental Sciences
Central University of Himachal Pradesh
Registration
UDYAM-registered MSME, India
Base
Himachal Pradesh, India

Courses

Flagship program · 4 levels + bonus

RS & GIS Masterclass

A complete run from theory foundation to advanced GIS, microwave/SAR remote sensing, and a cartography + AI bonus track — closing with a final project. Past cohorts have delivered SAR flood mapping and NDWI water-extraction work.

₹7,999
₹3,999
Limited-time offer
Scratch → AdvancedNo prior GIS experience needed
Bilingual batchesTaught in Hindi + English
QGIS + GEE + PythonFully hands-on tooling
Final projectEvaluated & certified
Class Timing
Evening batch, 7:30 PM – 9:30 PM
Mode of Teaching
Bilingual — Hindi + English
Course Duration
7–8 weeks
Platform
Google Meet
Recorded Lectures
Shared as a Drive link — lifetime access, streaming only (download not available)
Module 1 — Geography & Map Basics
  • Earth & Map Fundamentals — Geoid vs. ellipsoid, Earth's coordinate framework
  • Geographic Coordinate System — Lat/long, Prime Meridian, DD & DMS
  • Projected Coordinate System — PCS, UTM, projection zones
  • Map Projections & Datum — Distortion types, WGS84, Everest, datum transformation
  • Map Scale & Cartographic Elements — RF, scale bar, legend, north arrow, insets
  • Map Reading & Best Practices — Topographic maps, coordinate extraction, common errors
Module 2 — Remote Sensing Fundamentals
  • Introduction to RS — Components and applications
  • Electromagnetic Radiation — Spectrum, wavelength & frequency
  • Atmosphere & Surface Interaction — Atmospheric windows, scattering
  • Spectral Analysis — Vegetation, water, soil, built-up signatures
  • RS Sensors — Spatial/spectral/temporal/radiometric resolution
  • Satellite Image Interpretation — TCC, FCC, band combinations
Module 3 — GIS Fundamentals
  • Introduction to GIS — Components, workflow, applications
  • Spatial Data Models — Raster vs. vector, advantages & limitations
  • GIS Data Management — Attribute tables, field types, metadata
  • Spatial Analysis — Spatial/attribute joins, topology, queries
  • P1 · GIS Introduction & Portals — Popular GIS data portals
  • P2 · Satellite Data & GEE — USGS, Copernicus, Earth Engine intro
  • P3 · QGIS Interface & Vector Editing — Layer management, OSM plugin
  • P4 · Digitization — Point, line & polygon digitizing from Google Earth Pro
  • P5 · Georeferencing — Toposheets, coordinate systems, measurement
  • P6 · CSV Data & Interpolation — XY plotting, IDW interpolation
  • P7 · Raster Analysis — Clip, extract by mask, FCC
  • P8 · Raster Visualization — Batch processing, spectral signature (SCP)
  • P9 · Raster & Vector Modelling — Continuous vs. discrete data
  • P10 · Raster to Vector — Polygonize, feature extraction, data cleaning
  • P11 · Spectral Indices — NDVI, NDWI, NDBI band math
  • P12 · NDVI, LAI & Rainfall Interception Analysis — Vegetation index modeling & canopy interception
  • P13 · Land Surface Temperature & UHI Analysis — LST retrieval, urban heat island mapping
  • P14 · LULC Classification — Supervised/unsupervised, accuracy assessment
  • P15 · Image Enhancement — Contrast stretching, histogram equalization
  • P16 · DEM & Terrain Analysis — Hillshade, slope, aspect, contours
  • P17 · MCDM & AHP Analysis — Pairwise comparison, weighted overlay
  • P18 · Risk Zonation Mapping — Flood, landslide & groundwater potential
  • P19 · Model Builder & Automation — Automated GIS workflows, batch processing
  • P20 · Spatial Data Integration — Spatial/attribute joins, field calculator
  • P21 · Hydrology & Watershed Analysis — Flow accumulation, watershed delineation
  • L21 · Intro to Microwave RS — Optical vs. microwave, EM spectrum
  • L22 · Active & Passive Microwave — Radar fundamentals, backscatter factors
  • L23 · SAR Concepts — Radar vs. SAR, range & azimuth resolution
  • L24 · SAR Image Characteristics — Speckle, polarization, foreshortening & shadow
  • L25 · SAR Satellite Missions — Sentinel-1, ALOS PALSAR, TerraSAR-X
  • L26 · SAR Applications — Flood mapping, soil moisture, InSAR basics
  • P27 · Glass Effect
  • P28 · 3D Polygon Paper-Cut Effect
  • P29 · Magnifier Lens Effect
  • P30 · Depression Effect
  • P31 · Smooth Multidirectional Hillshade
  • P32 · Glowing Stream Order
  • P33 · Blender + QGIS 3D Mapping
🎓 Participants who complete all modules and the final project receive a Certificate of Completion in Remote Sensing & GIS, issued by MapCraft GIS — UDYAM-registered MSME.
Coming Soon Groundwater Modeling Intensive

Groundwater Modeling Intensive

A structured lecture series across ModelMuse and Visual MODFLOW — from conceptual model to a working, calibrated groundwater simulation.

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Coming Soon Flood & Hydraulic Modeling using HEC-RAS

Flood & Hydraulic Modeling

HEC-RAS and HEC-HMS workflows for hydraulic and hydrologic modeling, including 2D mesh setup and dam-break / breach analysis.

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Coming Soon Python, Machine Learning and AI for Remote Sensing and GIS

Python, Machine Learning & AI for Remote Sensing & GIS

A full path from Python foundations to deep learning for satellite imagery — NumPy, GeoPandas and rasterio, Random Forest / SVM classification with scikit-learn, and CNN-based image classification and change detection, positioned as the premium capstone after the RS & GIS Masterclass.

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Demo Lectures

Coming Soon
Geography Fundamentals

GCS vs. PCS & UTM Projection

Geographic vs. projected coordinate systems, and choosing the right UTM zone.

Coming Soon
Remote Sensing

Reading the Spectral Signature Curve

How vegetation, water and soil trace distinct signature curves across bands.

Coming Soon
GIS Practicals

QGIS Plugins & Road Network Extraction

Using QGIS plugins to extract road networks from satellite imagery.

Coming Soon
SAR & Microwave RS

SAR-Based Flood Mapping

Sentinel-1 backscatter, speckle filtering, and building a flood extent map.

Expertise

SAR Flood Mapping NDWI Water Extraction MODFLOW / Visual MODFLOW HEC-RAS Dam-Break Analysis Watershed Delineation AHP–MCDM Risk Zonation Google Earth Engine + Python QGIS Field Workflows DEM & Geodesy Fundamentals Machine Learning for RS/GIS

Mapping Services Available

Beyond training — MapCraft also takes on mapping and geospatial analysis work directly.

SAR Flood Mapping & Inundation Assessment
Sentinel-1 based flood extent mapping for a river reach or district
Enquire →
Watershed & Drainage Analysis
DEM-based delineation, stream ordering, and drainage network extraction
Enquire →
Terrain & DEM Processing
DEM correction, hillshade, slope/aspect, and 3D terrain visualization
Enquire →
Risk & Suitability Zonation
AHP-MCDM based flood, fire, or site-suitability zonation maps
Enquire →
Custom GEE / Python Automation
Scripted Earth Engine or Python pipelines for recurring analysis needs
Enquire →

Projects

Uttarakhand
Understanding springshed recharge mechanisms and delineating recharge zones using RS-GIS and Electrical Resistivity Tomography (ERT) for field validation. View live 3D dashboard →
Himachal Pradesh
RUSLE-based soil erosion estimation paired with machine learning driving-force analysis — Random Forest, GBRT and permutation importance.
Beas River, HP
Dam-break analysis using HEC-RAS for Pong Dam — breach parameters via Froehlich equations, storage-area curve construction, Courant-violation troubleshooting.

Contact

Whether you're planning a batch enrollment, asking about the groundwater intensive, or need a mapping job done — write in and we'll figure out the right starting point.