Road detection via a dual-task network based on cross-layer graph fusion modules

Road detection based on remote sensing images is of great significance to intelligent traffic management. The performances of the mainstream road detection methods are mainly determined by their extracted features, whose richness and robustness can be enhanced by fusing features of different types and cross-layer connections. However, the features in the existing mainstream model frameworks are often similar in the same layer by the single-task training, and the traditional cross-layer fusion ways are too simple to obtain an efficient effect, so more complex fusion ways besides concatenation and addition deserve to be explored. Aiming at the above defects, we propose a dual-task network (DTnet) for road detection and cross-layer graph fusion module (CGM): the DTnet consists of two parallel branches for road area and edge detection, respectively, while enhancing the feature diversity by fusing features between two branches through our designed feature bridge modules (FBM). The CGM improves the cross-layer fusion effect by a complex feature stream graph, and four graph patterns are evaluated. Experimental results on three public datasets demonstrate that our method effectively improves the final detection result

Geomatics engineering ecosystem, what more to be done

Shared Prosperity Vision 2030 (WKB2030) is one of the Malaysian government’s initiatives to achieve the goals of a developed country by the year 2030. It is one of the motivational pillars that ought to underpin state-level policymaking and development strategies with the participation of all relevant professional professions. The geomatics engineering profession is one of the country’s core prospects and has contributed to the geomatics field in Malaysia, also responding to the call of WKB2030. Unfortunately, the digitalization process is moving too fast and adaptation to current technologies is crucial to stay relevant in the industry. As a result, this study highlighted the geomatics engineering direction through previous study trend identification and the compilation of the blueprint for WKB 2030 to ensure the geomatics engineering profession strives hard in adapting and contributing to national development. To empower the geomatics engineering ecosystem, various initiatives have been emphasized with the involvement of the geomatics engineering community in implementing new measures to enhance the field’s established services by leveraging innovations developed in the context of the Fourth Industrial Revolution (IR 4.0). Lastly, this manuscript will examine, from a geomatics engineering stance, how the geomatics field has adapted to recent technological breakthroughs in order to realize the WKB2030 objectives

Building-road Collaborative Extraction from Remotely Sensed Images via Cross-Interaction

Buildings are the basic carrier of social production and human life; roads are the links that interconnect social networks. Building and road information has important application value in the frontier fields of regional coordinated development, disaster prevention, auto-driving, etc. Mapping buildings and roads from very high-resolution (VHR) remote sensing images have become a hot research topic. However, the existing methods often ignore the strong spatial correlation between roads and buildings and extract them in isolation. To fully utilize the complementary advantages between buildings and roads, we propose a building-road collaborative extraction method based on multi-task and cross-scale feature interaction to improve the accuracy of both tasks in a complementary way. A multi-task interaction module is proposed to interact information across tasks and preserve the unique information of each task, which tackle the seesaw phenomenon in multitask learning. By considering the variation in appearance and structure between buildings and roads, a cross-scale interaction module is designed to automatically learn the optimal reception field for different tasks. Compared with many existing methods that train each task individually, the proposed collaborative extraction method can utilize the complementary advantages between buildings and roads by the proposed inter-task and inter-scale feature interactions, and automatically select the optimal reception field for different tasks. Experiments on a wide range of urban and rural scenarios show that the proposed algorithm can achieve building-road extraction with outstanding performance and efficiency.Comment: 34 pages,9 figures, submitted to ISPRS Journal of Photogrammetry and Remote Sensin