Automated road network extraction from remote sensing imagery remains a significant challenge despite its importance in a broad array of applications. To this end, we explore road network extraction at scale with inference of semantic features of the graph, identifying speed limits and route travel times for each roadway. We call this approach City-Scale Road Extraction from Satellite Imagery v2 (CRESIv2). Including estimates for travel time permits true optimal routing, not just the shortest geographic distance. We compare SpaceNet labels to OpenStreetMap (OSM) labels, and find that models both trained and tested on SpaceNet labels outperform OSM labels by 60 or greater. For a diverse test set of SpaceNet data and a traditional edge weight of geometric distance, we find an aggregate of 5 improvement over existing methods. We also test our algorithm on Google satellite imagery with OpenStreetMap labels, and find a 23 improvement over previous work. Metric scores decrease by only 4 on large graphs when using travel time rather than geometric distance for edge weights, indicating that optimizing routing for travel time is feasible with this approach.
Extracting road networks directly has also been attempted by a number of studies. @cite_22 attempted road extraction via a Gibbs point process, while @cite_14 showed some success with road network extraction with a conditional random field model. @cite_10 used junction-point processes to recover line networks in both roads and retinal images @cite_32 extracted road networks by representing image data as a graph of potential paths. @cite_29 extracted road centerlines and widths via OSM and a Markov random field process. @cite_31 used a topology-aware loss function to extract road networks from aerial features as well as cell membranes in microscopy.
@cite_14 @cite_22 @cite_29 @cite_32 @cite_31 @cite_10
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In this paper, we propose a learning-based road network extraction scheme from high resolution satellite. First, the convolutional neural network(CNN), which is able to capture large context of local structures, are applied to predict the probability of a pixel belonging to road regions, and assign labels to each pixel to describe whether it is road. Then, a line integral convolution based algorithm is developed to smooth the rough map to connect small gaps. Finally, by combining with some common image processing operators, road centerlines are able to be acquired. Attribute to the learning capacity of CNN, and the line integral convolution based connection scheme, the proposed road extraction method is able to provide high quality results comparing to current state-of-art road extraction methods. Road information is fundamental not only in the military field but also common daily living. Automatic road extraction from a remote sensing images can provide references for city planning as well as transportation database and map updating. However, owing to the spectral similarity between roads and impervious structures, the current methods solely using spectral characteristics are often ineffective. By contrast, the detailed information discernible from the high-resolution aerial images enables road extraction with spatial texture features. In this study, a knowledge-based method is established and proposed; this method incorporates the spatial texture feature into urban road extraction. The spatial texture feature is initially extracted by the local Moran’s I, and the derived texture is added to the spectral bands of image for image segmentation. Subsequently, features like brightness, standard deviation, rectangularity, aspect ratio, and area are selected to form the hypothesis and verification model based on road knowledge. Finally, roads are extracted by applying the hypothesis and verification model and are post-processed based on the mathematical morphology. The newly proposed method is evaluated by conducting two experiments. Results show that the completeness, correctness, and quality of the results could reach approximately 94 , 90 and 86 respectively, indicating that the proposed method is effective for urban road extraction. In this paper we propose a new method for the extraction of roads from remotely sensed images. Under the assumption that roads form a thin network in the image, we approximate such a network by connected line segments. To perform this task, we construct a point process able to simulate and detect thin networks. The segments have to be connected, in order to form a line-network. Aligned segments are favored whereas superposition is penalized. These constraints are enforced by the interaction model (called the Candy model). The specific properties of the road network in the image are described by the data term. This term is based on statistical hypothesis tests. The proposed probabilistic model can be written within a Gibbs point process framework. The estimate for the network is found by minimizing an energy function. In order to avoid local minima, we use a simulated annealing algorithm, based on a Monte Carlo dynamics (RJMCMC) for finite point processes. Results are shown on SPOT, ERS and aerial images. Abstract In this paper, we present work on automatic road extraction from high-resolution aerial imagery taken over urban areas. In order to deal with the high complexity of this type of scenes, we integrate detailed knowledge about roads and their context using explicitly formulated scale-dependent models. The knowledge about how and when certain parts of the road and context model are optimally exploited is expressed by an extraction strategy. The key feature of the presented approach is the integral treatment of three essential issues of object extraction in complex scenes. (1) Specific parts of the road model and extraction strategy are automatically adapted to the respective contextual situation. (2) The extraction incorporates components for self-diagnosis that internally evaluate hypotheses indicating their relevance for further processing. (3) Multiple views on the scene are utilized in different ways. Redundancies in the extraction are exploited, occlusions are predicted and obviated, and a 3D object description is generated. The results achieved with our approach show that a stringent realization of these issues enables the extraction of roads even if their appearance is heavily affected by other objects. Based on an external evaluation of the results, we discuss advantages but also remaining deficiencies of this approach.