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Projects

  • 2020
    • Phnom Penh: Urban Growth from 1988 to 2020 by Landsat Satellite Imageries
    • Sentinel-1 SAR: Cambodia Flood in October 2020
    • Cambodia Forest Cover Change 2000-2019
  • 2021
    • Develop paddy area map from MODIS satellite images using machine learning
      • 1. Export MODIS satellite images from Google Earth Engine
      • 2. Create Mosaic of 2011 NDVI Images
      • 3. Create DataFrame of NDVI Timeseries
      • 4. Noise Reduction in NDVI Timeseries
      • 5. Validation Data
      • 6. KMeans Clustering and Results
    • Indonesia project: high-resolution satellite image analysis
      • 1. Extracting vegetation area
      • 2. Extracting water area
      • 3. Extracting building area
  • 2022
    • Kano & Yoshii river: Land cover classification using machine learning
      • 1. Prepare input data of Kano River
      • 2. Prepare input data of Yoshii River
      • 3. Supervised learning model development
      • 4. Predicting the whole Kano and Yoshii River
    • Mapping flood frequency in Cambodia from 1988 to 2020 using Google Earth Engine
    • Land cover classification using supervised learning method in Google Earth Engine

Documentations

  • LiDAR
    • Create DEM and Hillshade from point cloud data in Python
    • Create RGB image from las file in Python
  • Geo-Python
    • Read & visualize raster image using xarray
    • Classify iris dataset with random forest classifier
    • Create a subplot figure
    • Interactive geoplots in dashboard layout with Bokeh
  • Google Earth Engine
    • Download DEM from SRTM90 dataset
    • Calculate monthly mean temperature from ECMWF Climate dataset
    • Calculate monthly mean precipitation from CHIRPS Daily dataset
    • Calculate water turbidity index (WTI) from Sentinel-2
  • Machine Learning
    • Land Use Classification with Random Forests Classifier
  • Deep Learning
    • UNET Building footprint extraction using PyTorch
      • Convert WorldView-3 satellite images from Uint16 to 8byte
      • Convert building polygons to building masks for semantic segmentation model
      • UNet-Building Segmentation Model for WorldView-3 Satellite Images (PyTorch)
      • Split large satellite image into small images for model prediction
      • Building Footprint Prediction on WorldView-3 Satellite Images

Lessons

  • Geospatial data analysis with Python
    • Session 1: Geometric Objects
    • Session 2: Vector Data Analysis and Map Projection
    • Session 3: Geocoding and Nearest Neighbour Analysis
    • Session 4: Geometric operation and Data classification
    • Session 5: Plotting Static and Interactive Map on Leaftlet
    • Session 6: Raster Data Analysis
Theme by the Executable Book Project

Deep LearningΒΆ

Deep learning projects

  • UNET Building footprint extraction using PyTorch
    • Convert WorldView-3 satellite images from Uint16 to 8byte
    • Convert building polygons to building masks for semantic segmentation model
    • UNet-Building Segmentation Model for WorldView-3 Satellite Images (PyTorch)
    • Split large satellite image into small images for model prediction
    • Building Footprint Prediction on WorldView-3 Satellite Images

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Land Use Classification with Random Forests Classifier

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UNET Building footprint extraction using PyTorch

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