Grid Classification

Introduction

Land use and land cover (LULC) classification plays a vital role in environmental monitoring, agricultural planning, urban expansion analysis, and sustainable development. Accurate classification of satellite and aerial imagery enables decision-makers and researchers to monitor changes, predict trends, and plan effectively. However, traditional methods for classifying LULC are time-consuming, labor-intensive, and often require significant domain expertise.

The primary aim of this project is to develop a generalized, scalable, and easy-to-use system for Land Use and Land Cover (LULC) classification using both supervised and unsupervised machine learning algorithms. The project is designed to address the increasing demand for automated geospatial analysis tools that can efficiently process and classify high-resolution satellite images. By targeting a range of grid sizes — including 3km x 3km, 1km x 1km, and 500m x 500m — the system provides the flexibility needed for various environmental and planning applications.

To achieve this, the proposed solution integrates a suite of machine learning models. Supervised algorithms such as Random Forest, Support Vector Machines (SVM), and XGBoost are employed for high-accuracy classification when labeled training data is available. For scenarios lacking labeled data, the system supports unsupervised learning using clustering techniques like KMeans and KMeans++. This dual-mode approach allows the system to adapt to diverse datasets and classification challenges, making it broadly applicable across different use cases in environmental monitoring, urban planning, and agricultural management.