Road extraction from remote sensing images in very high resolution is important for autonomous driving and road planning. Compared with large-scale objects, roads are smaller, winding, and likely to be covered by buildings’ shadows, causing deep convolutional neural networks (DCNNs) to be difficult to identify roads. The paper proposes a semantics-geometry framework (SGNet) with a two-branch backbone, i.e., semantics-dominant branch and geometry-dominant branch. The semantics-dominant branch inputs images to predict dense semantic features, and the geometry-dominant branch takes images to generate sparse boundary features. Then, dense semantic features and boundary details generated by two branches are adaptively fused. Further, by utilizing affinity between neighborhood pixels, a feature refinement module is proposed to refine textures and road details. We evaluate the SGNet on the Ottawa road dataset. Experiments show that the SGNet outperforms other competitors on the road extraction task. Codes is available at https://github.com/qiuluyi/SGNet.

Recently, deep learning has been widely used in the segmentation tasks of remote sensing images. However, the existing deep learning method most focus on local contextual information and has limited field of perception, which makes it difficult to capture the long-range contextual feature of objects at large scales form very-high-resolution (VHR) images. In this paper, we present a novel Local–global Framework consisting of the dual-source fusion network and local–global transformer modules, which efficiently utilize features extracted from multiple sources and fully capture features of local and global regions. The dual-source fusion network is an encoder designed to extract features from multiple sources such as spectra, synthetic aperture radar, and elevations, which selective fuse features from multiple sources and reduce the interference of redundant features. The local–global transformer module is proposed to capture fine-grained local features and coarse-grained global features, which enables the framework to focus on recognizing multiple-scale objects from the local and global regions. Moreover, we propose a pixelwise contrastive loss, which could encourage that the prediction is pulled closer to the ground truth. The Local–global Framework achieves state-of-the-art performance with 90.45% mean f1 score on the ISPRS Vaihingen dataset and 93.20% mean f1 score on the ISPRS Potsdam dataset.

It is of great significance for disaster prevention and mitigation to carry out disaster simulations for dam failure accidents in advance, but at present, there are few professional systems for disaster simulations of tailings dams. In this paper, we focused on the construction of a virtual geographic environment (VGE) system that provides an effective tool for visualizing the dam-break process of a tailings pond. The dam-break numerical model of the tailings dam based on computational fluid dynamics (CFD) was integrated into the VGE system. The infrastructure of the VGE was supported by a 3-D geographic information system (GIS) with a user-friendly interface for the initiation, visualization, and analysis of the dynamic process of tailings dam failure. Key technologies, including the integration of numerical models, rendering of large-scale scenes, and optimizations of disaster simulation and visualization, were discussed in detail. In the prototype system, information on the run-out path, travel distance, etc. can be obtained to visually describe the flow motion released by two dam failure cases. The simulation results showed that the VGE can be used for the multidimensional, dynamic and interactive visualization of dam-break disasters, and can also be useful for assessing the risk associated with tailings dams.

With the advancement of computer vision, artificial intelligence, and remote sensing technologies, deep learning algorithms are increasingly used in terrestrial, airborne, and spaceborne-based fire detection systems. The performance and generalization of these data-driven fire detection algorithms, however, are restricted by the limited number and source of fire detection datasets. A large-scale fire detection benchmark dataset covering complex and varied fire scenarios is urgently needed. This work constructs a 100,000-level Flame and Smoke Detection Dataset (FASDD) based on multi-source heterogeneous flame and smoke images. It holds rich variations in image size, resolution, illumination (day and night), scenario (indoor and outdoor), image range (far and near), viewing angle (top view and side view), platform (surveillance cameras, drones, and satellites), and data source (Internet, social media, and open-access fire datasets). To the best of our knowledge, FASDD is currently the most versatile and comprehensive dataset for fire detection. It provides a challenging benchmark to drive the continuous evolution of fire detection models. Additionally, we formulate a unified workflow for preprocessing, annotation, and quality control of fire samples. Out-of-the-box annotations are published in four different formats for training deep learning models. Extensive performance evaluations based on classical methods show that most of the object detection models trained on FASDD can achieve satisfactory fire detection results, and especially YOLOv5x achieves nearly 80 % mAP@0.5 accuracy on heterogeneous images spanning two domains of computer vision and remote sensing. And the application in wildfire location demonstrates that deep learning models trained on our dataset can be used in recognizing and monitoring forest fires. Deep learning models trained with FASDD can be simultaneously deployed on satellites, drones, and ground sensors, thus realizing collaborative fire observation and detection in a space-air-ground integrated network environment. The dataset is available from the Science Data Bank website at https://doi.org/10.57760/sciencedb.j00104.00103 (Wang et al., 2022a).

Most deep-learning methods that achieve high segmentation accuracy require deep network architectures that are too heavy and complex to run on embedded devices with limited storage and memory space. To address this issue, this letter proposes an efficient generative adversarial transformer (GATrans) for achieving high-precision semantic segmentation while maintaining an extremely efficient size. The framework utilizes a global transformer network (GTNet) as the generator, efficiently extracting multilevel features through residual connections. GTNet employs global transformer blocks with progressively linear computational complexity to reassign global features based on a learnable similarity function. To focus on object- and pixel-level information, the GATrans optimizes the objective function by combining structural similarity losses. We validate the effectiveness of our approach through extensive experiments on the Vaihingen dataset, achieving an average $F1$ score of 90.17% and an overall accuracy (OA) of 91.92%. Codes are available at https://github.com/qiuluyi/GATrans .