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.
@cite_14: The aim of this work is to extract the road network from aerial images. What makes the problem challenging is the complex structure of the prior: roads form a connected network of smooth, thin segments which meet at junctions and crossings. This type of a-priori knowledge is more difficult to turn into a tractable model than standard smoothness or co-occurrence assumptions. We develop a novel CRF formulation for road labeling, in which the prior is represented by higher-order cliques that connect sets of super pixels along straight line segments. These long-range cliques have asymmetric PN-potentials, which express a preference to assign all rather than just some of their constituent super pixels to the road class. Thus, the road likelihood is amplified for thin chains of super pixels, while the CRF is still amenable to optimization with graph cuts. Since the number of such cliques of arbitrary length is huge, we furthermore propose a sampling scheme which concentrates on those cliques which are most relevant for the optimization. In experiments on two different databases the model significantly improves both the per-pixel accuracy and the topological correctness of the extracted roads, and outperforms both a simple smoothness prior and heuristic rule-based road completion.
@cite_22: 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.
@cite_29: In recent years, contextual models that exploit maps have been shown to be very effective for many recognition and localization tasks. In this paper we propose to exploit aerial images in order to enhance freely available world maps. Towards this goal, we make use of OpenStreetMap and formulate the problem as the one of inference in a Markov random field parameterized in terms of the location of the road-segment centerlines as well as their width. This parameterization enables very efficient inference and returns only topologically correct roads. In particular, we can segment all OSM roads in the whole world in a single day using a small cluster of 10 computers. Importantly, our approach generalizes very well, it can be trained using only 1.5 km2 aerial imagery and produce very accurate results in any location across the globe. We demonstrate the effectiveness of our approach outperforming the state-of-the-art in two new benchmarks that we collect. We then show how our enhanced maps are beneficial for semantic segmentation of ground images.
@cite_32: We propose a novel approach to automated delineation of linear structures that form complex and potentially loopy networks. This is in contrast to earlier approaches that usually assume a tree topology for the networks. At the heart of our method is an Integer Programming formulation that allows us to find the global optimum of an objective function designed to allow cycles but penalize spurious junctions and early terminations. We demonstrate that it outperforms state-of-the-art techniques on a wide range of datasets.
@cite_31: Delineation of curvilinear structures is an important problem in Computer Vision with multiple practical applications. With the advent of Deep Learning, many current approaches on automatic delineation have focused on finding more powerful deep architectures, but have continued using the habitual pixel-wise losses such as binary cross-entropy. In this paper we claim that pixel-wise losses alone are unsuitable for this problem because of their inability to reflect the topological impact of mistakes in the final prediction. We propose a new loss term that is aware of the higher-order topological features of linear structures. We also introduce a refinement pipeline that iteratively applies the same model over the previous delineation to refine the predictions at each step while keeping the number of parameters and the complexity of the model constant. When combined with the standard pixel-wise loss, both our new loss term and our iterative refinement boost the quality of the predicted delineations, in some cases almost doubling the accuracy as compared to the same classifier trained with the binary cross-entropy alone. We show that our approach outperforms state-of-the-art methods on a wide range of data, from microscopy to aerial images.
@cite_10: 
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.