Harnessing AI and Machine Learning for Geospatial Analysis

Explore numeric, factor, and character data types in R. Create vectors with c, convert to factor, build frames with data.frame, and add columns with dollar and cbind; view and summarize.

Import data in R using csv, sav, and xls formats with functions like read.table and read.csv, then read online files such as Columbus.csv and view the data as a table.

Export and save data in R using Write.csv for csv or semicolon separated formats, Write.table for text, and save, load, saveRDS, readRDS, and save.image to manage multiple objects and workspaces.

Master data manipulation in R with tidyverse and dplyr by importing csv data, renaming columns, piping, grouping, summarizing, selecting, filtering, mutating, and reshaping between wide and long forms, plus joins.

Explore how to use R for research by understanding R packages and library, installing and loading packages with install.packages and library, removing and updating packages with remove.packages and update.packages.

Learn to work with R's working directory and paths, using get pwd, set WD, absolute and relative paths, list files, create and delete files, copy files, and check existence.

Apply multiple linear regression in R using lm to relate lai to lst, temperature, precipitation, and humidity, and interpret r squared, f statistic, and model diagnostics.

Discover why Python powers AI and ML with a rich library ecosystem, from NumPy and Pandas to TensorFlow and Keras. Embrace its readable syntax for rapid prototyping across domains.

Download and install Miniconda to set up Python with conda and a few packages including pip, and register Miniconda as the default Python 3.9.

Create and manage Python environments with Anaconda or Miniconda, activate environments before installing packages like numpy and seaborn with conda or pip, and use conda env list to view environments.

Learn to install and run jupyter notebook to write and execute python code, manage conda environments, add extensions, and launch and close notebooks.

Explore Google Colab, a free cloud-based platform for Python in collaborative notebooks. Share notebooks, use GPUs/TPUs, and run libraries like TensorFlow and PyTorch for data analysis and machine learning.

Explore python workflows to compute ndvi, ndb, and nbr from Landsat 8 imagery in Kurdistan, using red, nir, and swir bands; export nbr as geotiff and visualize the burn map.

Compute zonal statistics in python for geospatial analysis using the calculate z s function from the pi lst package to summarize raster data by shapefile boundaries and export to csv.

Master plotting in Python with matplotlib and seaborn, from time series and seasonal plots to multi-variable data, creating line, bar, scatter, box, histogram, animated plots, and cat plots in Jupyter.

Visualize geospatial data with heat maps, swarm, bar, stacked bar, pair, scatter, and 3d scatter plots using seaborn and matplotlib, while cleaning borough data for happiness, diabetes, anxiety, and employment.

Visualize geospatial data with Python by creating pie charts, box plots, histograms, and animated plots, and explore Seaborn cat plots such as violin, swarm, and bar.

Analyze crop data with Python using DM, DTM, multispectral imagery, and NDVI, loading data from Drone Mapper, and compute zonal statistics with visualization of canopy mean height and thermal data.

Explore geospatial analysis with machine learning and data processing through hands-on code using geopandas, folium, k-means, random forest, cnn with Keras, arima for time series forecasting, and isolation forest.

Learn geospatial data processing with Rasterio: normalize elevation, extract features, apply k-means clustering, and build random forest and CNN models for Landsat and CIFAR-10 data.

Analyze temporal and geospatial data by applying an ARIMA time series model to annual precipitation, detect anomalies with isolation forest, and visualize patterns via Geopandas, K-means clustering, and spatial joins.

Apply kriging interpolation to CSV data with coordinates and rainfall, visualize original points and the interpolated surface, then analyze ndwi time-series and terrain slope from DEM.

Detect edges on satellite images with the Sobel filter using Rasterio and SciPy. Forecast time series with lstm models on synthetic sine data and Erbil Governorate precipitation.

Predict crop health from remote sensing data using a three-layer neural network and ndvi. Compute zonal statistics with rasterio and geopandas, then train and evaluate in Python.

Implement deep learning in R by building a neural network for iris data, using one-hot encoding, train-test split, ReLU and softmax, and evaluate with categorical cross-entropy and Adam optimizer.

Fine-tune a deep learning model in R by stacking dense layers with relu, using four input features and three iris classes via softmax, and evaluate with categorical cross-entropy and Adam.

Train a convolutional neural network for image classification on the Eurosat dataset using PyTorch, leveraging a pre-trained ResNet-50, data transformations, training and evaluation with a confusion matrix and accuracy metrics.

Set up a gpu-enabled environment for AI tasks by installing TensorFlow GPU and CUDA toolkit, verify GPU availability in Jupyter, and compare GPU versus CPU performance on MNIST neural network.

Leverage random forest classification in Google Earth Engine to integrate SAR and optical data (Sentinel-1 and Sentinel-2), classify crops like barley and wheat, and assess accuracy improvements.

Analyze India air quality data with Python and machine learning. Apply pandas, Matplotlib, Seaborn, NumPy, scikit-learn, and statsmodels to load, describe SO2, NO2, PM, SPM, and assess missing values.

The lecture demonstrates removing outliers with the IQR method, computing state-wise means for pollutants like SO2, NO2, and SPM, imputing missing values, and deriving AQI and pollutant indices in Python.

Perform exploratory data analysis on air quality data using visualizations such as heatmaps and rake plots, then apply backward elimination and linear regression with cross-validation to predict AQI.

Analyze multicollinearity in air quality modeling using VIF, cross-validation, and ridge regression to evaluate AQI in India. Explore stepwise regression and interaction effects to improve model generalization and avoid overfitting.

Compare neural network, decision tree, random forest, and nearest neighbors on a synthetic Python dataset, with neural network at 81%, decision tree 80%, random forest 79%, and nearest neighbors 76%.

Detect and count plants in geospatial data using Python and computer vision, building a plant model with Geopandas, Rasterio, and OpenCV, masking rasters and exporting coordinates to CSV.