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
Index
