Category: Deep Learning Projects

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AGRICULTURE LAND CLASSIFICATION USING DEEP LEARNING

AGRICULTURE LAND CLASSIFICATION USING DEEP LEARNING

Abstract

Land cover classification is a fundamental task in remote sensing used for mapping different types of land surfaces such as forests, urban areas, water bodies, and agricultural land. This project leverages deep learning, specifically the U-Net++ architecture with EfficientNet-B3 encoder, to accurately classify satellite images. A Flask-based web application enables users to upload satellite images and view segmented output along with land distribution analysis. The model also computes and visualizes the percentage coverage of each land category in a given image, facilitating better understanding and decision-making for environmental monitoring and land use planning.

Introduction

Monitoring and managing land resources is vital in the context of urbanization, agriculture, deforestation, and environmental sustainability. Satellite imagery, combined with advanced deep learning techniques, provides a scalable solution for automatic land cover classification. Traditional classification methods often require extensive manual efforts or lack precision. This project addresses these limitations by integrating a deep learning segmentation model (U-Net++) into a user-friendly web interface for real-time land classification and analysis.

Problem Statement

Conventional land cover classification techniques struggle with:

  • Inconsistent accuracy across varying landscapes
  • Lack of scalability for large-scale monitoring
  • Minimal interactivity and user engagement in existing tools

There is a need for an automated, accurate, and interactive system that classifies land cover from satellite imagery and provides analytical insights to support planning and monitoring.

Existing System and Its Disadvantages

Existing Systems:

  • Manual classification or semi-automated tools in GIS software
  • Traditional machine learning with handcrafted features
  • Open-source tools lacking interactivity or model flexibility

Disadvantages:

  • Require domain expertise and manual labeling
  • Lack adaptability to new or unseen landscapes
  • Minimal support for live image upload and result visualization
  • Poor performance in noisy or complex regions

Proposed System and Its Advantages

Proposed System: This project proposes a deep learning-based web platform using U-Net++ with EfficientNet-B3 encoder, capable of segmenting satellite images into land cover classes and visually displaying the results.

Advantages:

  • High accuracy through advanced CNN architecture
  • Fully automated segmentation with minimal human input
  • Interactive and easy-to-use web interface
  • Displays land class distribution and charts for visual analytics
  • Supports expansion with more datasets or models

Modules

  1. User Upload Module
    • Upload satellite images and ground truth masks
  2. Pre-processing Module
    • Normalize and resize images using Albumentations
  3. Model Inference Module
    • Predict segmented image using trained U-Net++ model
  4. Postprocessing Module
    • Apply color mapping and calculate land area percentage
  5. Visualization Module
    • Generate bar chart and display results
  6. Web Interface (Flask)
    • Render upload form and prediction results dynamically

Algorithm / Model Used

Model: U-Net++ with EfficientNet-B3 Encoder

  • Encoder: EfficientNet-B3 (pre-trained on ImageNet)
  • Decoder: Nested skip connections (U-Net++)
  • Output: 7-class pixel-wise segmentation
  • Framework: segmentation_models_pytorch

Workflow:

  1. Preprocess input using Albumentations
  2. Pass through U-Net++ for segmentation
  3. Map classes to RGB for visualization
  4. Count pixels for land distribution stats

Software Requirements

  • Python 3.x
  • Flask
  • PyTorch
  • segmentation-models-pytorch
  • OpenCV
  • Albumentations
  • Matplotlib
  • HTML/CSS (Jinja2 templating)

Hardware Requirements

  • GPU (optional but recommended for training)
  • Minimum 8 GB RAM
  • CPU with multi-threading
  • Storage for model and static files

Conclusion

This project successfully demonstrates the power of deep learning for satellite image analysis through land cover classification. The system combines an advanced segmentation model with an intuitive web interface, enabling users to gain insights into land distribution from satellite data. The classification results and area-based analysis empower planners, environmentalists, and researchers to make informed decisions.