I will do geospatial mapping using machine learning

Geospatial Data Analysis with Machine Learning

Feel free to send me your offers, inquiries, and project details, and lets discuss the best support I can provide for your geospatial and machine learning needs.

Example Services:

1. Suitability mapping: such as crops, habitats, soil nutrients, or ecosystem services.
2. Risk prediction: such as fires, deforestation, emissions, floods, or subsidence.
3. Climate impact modeling: such as integrate SSP scenarios to assess the potential impacts of climate change.
4. Land Cover/Use classification.

Example Processing:

1. Sample pre-processing: clean, standardize, and reduce spatial autocorrelation in your data.
2. Predictor collection & selection: apply methods like Pearson correlation, VIF, and recursive feature elimination.
3. Hyperparameter tuning: optimize model parameters using techniques like grid search or Bayesian optimization.
4. Model selection & validation: split data, perform spatial cross-validation, and ensure reliable results.
5. Comprehensive reporting: receive detailed reports with scientific analysis and infographics.

Tools: R, Python, Google Earth Engine.

Model: Random Forest, XBoost, MaxEnt, SVM, kNN, and ensemble model.