Deep Learning for Cadastral Boundary Extraction

Iterative Refinement

• Analyze accuracy assessment results to identify discrepancies and areas of improvement
• Refine pre-processing, image enhancement, segmentation, feature extraction, and boundary detection steps to optimize accuracy
• Incorporate feedback from team members, domain experts, and stakeholders to refine the workflow and enhance the cadastral boundary extraction process

Deep Learning Algorithms

• Convolutional Neural Networks (CNNs): Designed for analyzing visual data such as images
• Generative Adversarial Networks (GANs): Involves two neural networks: a generator and a discriminator
• Recurrent Neural Networks (RNNs): Well-suited for sequential data, including images with temporal dependencies

Fourier Transformation

• Represents a one-dimensional function as a superposition of trigonometric sine and cosine terms
• Calculates the frequency, amplitude, and phase of each sine wave needed to make up a signal
• Assumes the signal is continuous with an infinite extent

Principal Component Analysis

• Transforms a large set of variables into a smaller one, retaining most of the information
• Used for multi-image manipulation, transforming coordinate axes of a multispectral dataset to a new set of mutually orthogonal coordinate axes

Control Points

• Height Benchmarks: Used for vertical control, providing benchmark near works, and accurately leveled to National Benchmark
• Classified into: Natural targets, Signalized targets, and Artificial points based on their appearance on imagery
• Classified into: Control points, Check points, and Tie points based on their role in the adjustment

Machine Learning

• Definition: A system capable of autonomous acquisition and integration of knowledge
• Data Science Process: Data Collection, Data Preparation, EDA, Machine Learning, Visualization
• Examples: Spam filtering, Credit card fraud detection, Detecting faces in images, MRI image analysis, Recommendation system

Bayesian Network Learning

• Key Concepts: Nodes (attributes) = random variables, Conditional independence, Conditional probability table, Bayes Theorem
• Settings: Known structure, fully observable (parameter learning), unknown structure, fully observable (structural learning), known structure, hidden variables (EM algorithm), unknown structure, hidden variables

Unsupervised Learning

• Approaches: Clustering (similarity-based) and density estimation (e.g., EM algorithm)
• Performance Tasks: Understanding and visualization, Anomaly detection, Information retrieval, Data compression

Lidar and Lidar-based Algorithms

• Used for generating precise and directly geo-referenced spatial information
• Lasers produce a coherent light source

Canny Edge Detector

• Definition: A image processing technique used for detecting edges in digital images
• Steps: Noise reduction, Gradient calculation, Non-maximum suppression, Double threshold, Edge Tracking by Hysteresis
• Applications: Object Detection and Recognition, Image Segmentation, Quality Control and Inspection, Video processing, Image Retrieval, Robotics and Autonomous Vehicles

Support Vector Machine vs Box/Parallelepiped Classifiers

• SVM: Supervised learning algorithm used for classification and regression tasks
• Key idea: Maximize the margin between classes, and instances on the boundary of this margin are known as support vectors