WHAT IF I TOLD YOU...

Master Python for Hyperspectral Remote Sensing and Save hours daily and unlock powerful tools to solve spatial data challenges efficiently—no prior coding experience needed.

You'll Gain:

• Hands-on training using Python and Google Colab for hyperspectral satellite image analysis.

• Exploration of real-world hyperspectral datasets like AVIRIS and Hyperion.

• Building powerful classification models using Machine Learning (ML) and Deep Learning (DL) techniques.

• LULC map generation and accuracy assessment using Python and QGIS.

• Expert insights on real-world applications in agriculture, urban planning, and environmental monitoring.

Do not miss if you are:

• GIS and Remote Sensing Professionals - Gain advanced expertise in hyperspectral data preprocessing, analysis, and classification.

• Environmental Scientists and Planners - Utilize hyperspectral LULC mapping for applications in agriculture, urban planning, and environmental monitoring.

• Academicians and Researchers - Leverage cutting-edge techniques in LULC mapping for innovative research and publications.

• Data Science Enthusiasts - Explore how Machine Learning (ML) and Deep Learning (DL) can be applied to geospatial datasets.

• Students in Geography, Geoinformatics, or Earth Sciences - Build foundational skills for a career in geospatial analysis using open-source tools.

• Government and NGO Personnel - Develop actionable insights for policy-making and environmental management.

Workshop Gallery

Explore past sessions and learn from experts in hyperspectral remote sensing.

Content

Explore past sessions and learn from experts in hyperspectral remote sensing.

What You’ll Learn

• Introduction to Hyperspectral Imaging and LULC Mapping

  • Basics of hyperspectral imaging and its applications in LULC mapping.

  • Accessing and visualizing publicly available hyperspectral datasets.

  • Overview of LULC classification schemes (e.g., USGS Level I-IV).

  Preprocessing Hyperspectral Data

  • Techniques for noise reduction, atmospheric correction, and dimensionality reduction.

  • Spectral signature analysis for identifying unique LULC class features.

  • Hands-on preprocessing using Python.

  Building and Training Classification Models

  • Deep dive into Machine Learning (SVM, Random Forest) and Deep Learning (CNN).

  • Training CNN-based models for hyperspectral LULC classification using TensorFlow/Keras or PyTorch.

  • Hands-on implementation of classification models.

  LULC Map Generation, Evaluation, and Applications

  • Generating LULC maps with QGIS and Python.

  • Assessing classification accuracy (Confusion Matrix, Kappa Coefficient, Overall Accuracy).

  • Applications of LULC mapping in real-world scenarios like agriculture and urban planning.

Like this training upcoming days should be more training for learning enhancement

Ishtiaq Ahmed, Central University of Punjab, India

★★★★★

The session is awesome

Shrinwantu Raha, Bhairab Ganguly College, Kolkata

★★★★★

Well organized workshop - Bindu K B, Department of Geography, Kannur University

★★★★★

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