LULC Change & Future Prediction Using ANN–CA–Markov Modelling in QGIS
Historical LULC & Change Matrix
Markov Chain Probabilities
ANN Potential & Drivers
CA Simulation & Validation
Model spatial transition potential using Artificial Neural Networks driven by slope DEMs, road networks, and proximity raster datasets.
Process multi-temporal satellite data from USGS, Bhuvan, and ESA WorldCover to derive historical land transformation matrices.
Calculate transition probability matrices and establish baseline land transfer dynamics between historical baseline dates.
Execute Cellular Automata spatial allocations in QGIS MOLUSCE and validate accuracy using Kappa indices and Figure of Merit.
Six Comprehensive Practical Modules
Gain practical experience with open-source QGIS tools, MOLUSCE integration, and standardized environmental dataset preparation.
1. LULC Concepts & Data Prep
2. Change Detection Analysis
3. ANN Potential Modelling
4. Cellular AutomataSimulation
Multi-temporal data acquisition from Bhuvan, USGS, and ESA WorldCover with spatial resampling and projection standards.
Quantifying spatial gains, losses, net persistence, and generating standard land-use transition matrices.
Calibrating Neural Network transition potentials incorporating spatial drivers like elevation slope and road distances.
Coupling Markov probabilities with CA spatial rules within QGIS MOLUSCE to simulate future land scenarios.
Who Should Join This Workshop
Publishable Skills & Deliverables
Designed for PhD scholars, postgraduate students, faculty members, GIS & Remote Sensing professionals, urban planners, environmental consultants, and climate scientists looking to advance their research tools.
Participants will master building ANN transition potentials, running Markov probability chains, executing CA simulations, and validating future land scenarios for environmental impact assessments.
Prerequisites: Basic GIS and raster data familiarity is recommended. Prior programming or coding knowledge is not required.
Receive hands-on guidance from Dr. Shaik Vazeed Pasha, NIAS, Indian Institute of Science (IISC) Campus, Bengaluru an accomplished expert in remote sensing applications, GIS analytics, and spatial predictive modelling.
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Reserve Your Seat for 4 Live Hands-On Sessions
Limited seats available – Register before 23 Oct 2026