# Review on Active and Passive Remote Sensing Techniques for Road Extraction
## 3 Road Extraction Based on Different Data Sources
### Road Extraction Based on High-Spatial Resolution Images
#### 3.1.1 Main Methods  
Numerous road extraction algorithms based on high-resolution images have been developed over the past few decades, making it difficult to classify them. Traditional methods include automatic and semiautomatic extraction. Some methods based on deep learning have emerged and attracted considerable attention, owing to their high precision, in recent years. In this paper, we summarised the heuristic and data-driven road extraction methods based on two state-of-the-art reviews [34; 71]. A comparison between the different data-driven methods applied on the Massachusetts road dataset is shown in Table 4. It can be seen from Table 4 that most data-driven methods can obtain a high precision (better than 0.8). Meanwhile, different methods have advantages and disadvantages. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Special** & **WaveLength** & **Horizontal and Elevation Accuracy** & **Altitude** & **Pulse** & **Point Density** \\ \hline Leica & & & & & & **Repetition** & **Point Density** \\ Hyperion2+ [161], & Multiple pulses in the air measured & 1064 nm & \textless{}13 cm, & 300–5500 m & \textless{}2000 kHz & 2 pts/m\({}^{2}\)/4000 m, 40 pts/m\({}^{2}\)/600 m \\
2021 & & & & & & 6 million points \\ Leica SPL [162], 2021  
The heuristic extraction methods can be subdivided into automatic and semiautomatic methods according to the degree of automation facilitated by the method. The semiautomatic extraction algorithms require initial seeds, and the user needs to check the results frequently. In these methods, the seeds and directions should be provided manually, and the algorithms can recognise and roll back the previous results. Several classic semiautomatic methods exist, such as the active contour model, dynamic programming, geodesic path and template matching. The active contour model, proposed by [PERSON] et al. [171] and also known as the balloon snakes or ribbon snakes model, can extract road information through contour deformations from the labelled lines or points. The geodesic path method can produce a road-probability map through the extracted road edges. The dynamic programming method requires road parameters and mainly focuses on solving optimisation problems [172]. The template matching method can extract the road features through constructed windows and then matching the extracted points. All these semiautomatic methods include automatic processes. Automatic road extraction uses information such as road topology and context features to extract and recognise roads through methods such as pattern recognition, computer vision, artificial intelligence, and image understanding [173; 174; 175; 176]. Even though there are no fully automatic algorithms for all types of high-resolution images, several methods [71] such as segmentation-based, edge analysis, map-based, swarm intelligence-based, object-based and multispectral segmentation methods are much more efficient than the semiautomatic methods. Methods based on segmentation can identify regions through numerous algorithms [71] such as support vector machines (SVMs), artificial neural networks, Bayesian classifiers, mean shifts, watershed algorithms, super pixel segmentation, Gaussian mixture models, graph-based segmentation and conditional random field (CRF) models, which are usually used in combination to improve the classification accuracy. The edge analysis method is realised via edge detection, which is suitable for extracting the main roads. The map-based methods, especially OpenStreetMap, focus on urban roads. Swarm intelligence-based methods such as ant colony optimisation, artificial bee colony and firefly algorithms use discretisation and networking to simulate real biological behaviours [178]. Object-based methods are used to classify objects through object segmentation and feature characterisation. Multispectral segmentation methods usually require the support of multispectral or hyperspectral images [2].