![]() For instance, taking SpaceNet road dataset as the source domain, compared with the no adaptation method, the IOU performance of GOAL framework is increased by 14.36%, 5.49%, 4.51%, 5.63% and 15.14% on DeepGlobe, Boston, Birmingham, Shanghai, and Wuhan images, respectively, which demonstrates its strong generalization capability. The experimental results show that the proposed GOAL framework can clearly improve the cross-domain road detection performance, without any annotation of the target domain images. Extensive experiments were conducted on different road datasets, including two public competition road datasets-SpaceNet and DeepGlobe-and our own large-scale annotated images from four cities: Boston, Birmingham, Shanghai, and Wuhan. Therefore, a local alignment operation is introduced, which adaptively adjusts the weight of the adversarial loss according to the road recognition difficulty. However, the traditional global adversarial learning approach cannot guarantee local semantic consistency. On the other hand, the complex background of VHR remote sensing images, such as the occlusions and shadows of trees and buildings, makes some roads easy to recognize, while others are much more difficult. On the one hand, considering the spatial information similarities between the source and target domains, feature space driven adversarial learning is applied to explore the shared features across domains. In this paper, to address this problem, a global-local adversarial learning (GOAL) framework is proposed for cross-domain road detection. The manual pixel-level labeling process is also extremely time-consuming, and the performance of CNNs degrades significantly when there is a domain gap between the training and test images. However, this approach relies on massive annotated samples, and the problem of limited generalization for unseen images still remains. If you have any questions, please reach out through the Topcoder Forum ( ).Road detection based on convolutional neural networks (CNNs) has achieved remarkable performances for very high resolution (VHR) remote sensing images. ![]() ![]() To further aid competitors, the SpaceNet 5 baseline is fully open source, and yields a score of 54. The first 20 competitors to reach a score of 50 (out of a possible 100) received a credit for 10 hours on a p3.2xlarge for training and improving their models. For the first time in SpaceNet history, the final submissions were tested on a mystery city dataset that was revealed and open sourced at the end of the Challenge. SpaceNet open sourced new data sets for the following cities: Moscow, Russia Mumbai, India and San Juan, Puerto Rico. You can find a detailed description of CosmiQ Works’ algorithmic baseline on their blog at The DownLinQ. The task of this challenge was to output a detailed graph structure with edges corresponding to roadways and nodes corresponding to intersections and end points, with estimates for route travel times on all detected edges. The SpaceNet 5 challenge sought to build upon the advances from SpaceNet 3 and test challenge participants to automatically extract road networks and routing information from satellite imagery, along with travel time estimates along all roadways, thereby permitting true optimal routing. Satellite or aerial imagery often provides the first large-scale data in such scenarios, rendering such imagery attractive. This statement is as true today as it was two years ago when the SpaceNet Partners announced the SpaceNet Challenge 3 focused on road network detection and routing. In a disaster response scenario, for example, pre-existing foundational maps are often rendered useless due to debris, flooding, or other obstructions. Determining optimal routing paths in near real-time is at the heart of many humanitarian, civil, military, and commercial challenges.
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