Military conflicts have a significant impact on national security and the ecological environment. Effective detection methods for ground-moving objects in complex scenarios can be further utilized to assess damage and provide recommendations for security and environmental restoration. Current ground-moving object detection in optical remote sensing imagery struggles with balancing detection accuracy and real-time performance, hindering timely threat assessment. To address this, the study proposes ground-moving object detector (GMODet), a real-time detection method incorporating region awareness and semantic-spatial interaction to enhance the detection of partially occluded and fine-grained objects in complex environments. The framework includes three modules: the region awareness module (RAW), cross-scale context-aware feature aggregator (CCFA), and semantic-spatial progressive interaction module (SPIM), focusing on extracting discriminative features for contextual, multiscale, and semantic-spatial information. A new dataset, ground-based moving object dataset (GMOD), is constructed with four object types and high scene complexity, alongside experiments on the publicly available military vehicle remote sensing dataset (MVRSD). GMODet achieves the state-of-the-art performance, with mAP50, mAP75, and mAP scores of 65.5%, 48.5%, and 42.3% on the GMOD, outperforming the second-best results by 1.9%, 5.1%, and 1.5%, respectively. On the MVRSD, it achieves mAP50, mAP75, and mAP scores of 88.2%, 75.2%, and 61.7%, respectively. Notably, with an inference time of just 25 s on large-scale images ($9152\times 9152$pixels), GMODet showcases outstanding accuracy, speed, robustness, and generalization in ground-moving object detection.

Accurate and regularly updated global road maps have many applications in intelligent navigation and urban planning. Deep learning algorithms have recently shown promising road extraction results using various Earth observation data. However, limitations in sample annotation and domain discrepancies between different regions and sensors pose challenges to the applicability of existing road extraction methods for large-scale tasks. In this paper, we propose a novel GlobalRoadMapper scheme to extract global-scale roads at the submeter level from optical remote sensing images that only require limited labeled samples. The proposed GlobalRoadMapper is designed as a two-stage method integrating Supervised Domain Incremental and Unsupervised Domain Adaptation. The supervised domain incremental stage learns road features from the source domain. To deal with catastrophic forgetting when deep models learn new knowledge from new domains, a strategy that couples pre-domain replay and mean-teacher is proposed. The unsupervised domain adaptation stage expands knowledge learned from the source domain to unseen scenes. Multi-level domain adaptation is proposed at the image, feature, and prediction levels in the second stage to address the issue of weak cross-domain generalization. The effectiveness of GlobalRoadMapper was tested at 43 sites worldwide. Qualitative and quantitative results demonstrate that GlobalRoadMapper outperforms the existing methods for global-scale road extraction tasks. Furthermore, city-scale sub-meter road mapping was conducted in six cities from different regions worldwide. GlobalRoadMapper achieved visual results comparable to the manually annotated OpenStreetMap road networks. Overall, GlobalRoadMapper holds great potential for large-scale road extraction and can be adapted to create easily updatable road maps globally.

Urban function mapping serves as a vital role in urban management and planning tasks. To generate fine-grained recognition at spatial and semantic scale, a contrastive manner integrating comprehensive representation from multi-modal descriptions of urban functional zones (UFZs)is proposed. Abstract physical features from VHR images are obtained from the founder deep convolutional model. Spatial pattern and semantic features are extracted from geographical entities including urban buildings and POIs, respectively. The proposed model is validated in the downtown Wuhan, China, where resources and sensing data citywide are concentrated. Rich information is provided but also the challenges due to the high complexity are posed for urban functions recognition. The superior performances demonstrate that the multidimensional, especially the integration with spatial pattern of primary urban materials, enhances the exact and robust recognition on sophisticated functions of urban land.

Satellite videos have recently served as a new data source for a wide range of applications in traffic management and military surveillance. Due to its wider coverage, satellite videos show more advantages in large-scale monitoring than ground surveillance videos. However, pseudomotion background and low-resolution targets pose new challenges to moving vehicle detection in satellite videos, resulting in poor performance of conventional target detection methods when applied to satellite videos. To overcome this difficulty, we propose a novel moving vehicle detection approach using adaptive motion separation and difference accumulated trajectory. Specifically, a new indicator is designed to assist adaptive separation of moving targets and background, considering the scale invariance of vehicles in satellite videos. Meanwhile, we offer a vehicle discrimination algorithm based on a differential accumulated trajectory to distinguish the moving vehicles from the pseudomotion background. Experimental results on two satellite video data sets demonstrate that the proposed approach achieves better detection performance over the state-of-the-art moving vehicle detection methods.

Timely updated road networks are the basis for many real-world applications such as intelligent navigation and traffic management. Existing road updating methods based on remote sensing images learn from historical road databases to update roads. Road extraction models learned from historical images however, are not easily applied to a current image due to spectral differences; and only changed roads need updating. In this paper, an Unchanged-Guided Road Updating (UGRoadUpd) framework is proposed to improve the quality of updated road networks by limiting the road updating range and learning from historical unchanged roads. The UGRoadUpd framework identifies road changes using a novel dual-task dominant-transformer-based neural network for road change detection (DT-RoadCDNet). DT-RoadCDNet executes road segmentation and change detection simultaneously, from bi-temporal remote sensing images. The Dominant-Transformer based Global Context Modeling module in DT-RoadCDNet globally models the contextual spatial structure for improved integrity in roads and road changes. Based on the discovery of road changes, an unchanged-guided road update strategy updates the roads in changed areas by learning from the prior information provided by unchanged roads in a historical road database. Experiments on two newly annotated road change detection and update datasets confirms the effectiveness of our UGRoadUpd framework.

Vehicle re-identification supports cross-camera tracking and the location of specific vehicles in a smart city. The gallery images of vehicles are ranked based on the similarities in the appearance of objects to a vehicle query image. Previous work on vehicle re-identification has mainly focused on global or local analyses of predefined regions of vehicles to classify the vehicle images with a softmax loss function. On the one hand, separate global or predefined local regions of vehicles are often sensitive to perspective and occlusions. On the other hand, the embedding space supervised by the softmax loss function is not sufficiently compact for the object class. To solve these problems, we propose an end-to-end distance-based global and partial multi-regional deep network (DGPM) that combines multi-regional features to identify global and local differences. We exploit a three-branch architecture to learn the global and partial features from coarsely partitioned regions. A global similarity module is introduced to reduce the background information interference in the local branches. Unlike general classification, we design a distance-based classification layer that maintains consistency among criteria for similarity evaluation. Furthermore, we use spatiotemporal vehicle information to improve the vehicle re-identification results when the camera and shooting time are available. Systematic comparative evaluations performed on the large-scale VeRi and VehicleID datasets showed that our approach robustly achieved state-of-the-art performance. For instance, for the VeRi dataset, we achieve (79.39 + 2.78)% mAP and (96.19 + 2.26)% Rank-1 accuracy.

To meet the demands in the rapid update of roads in basic geographic information, a novel Dominant Feature Driven Road Extraction Method (DFDREM) is proposed to extract urban roads from high spatial resolution satellite images. In the proposed DFDREM, road scenes are classified into edge-feature-dominant (EFD) roads and region-feature-dominant (RFD) roads based on directional line density at first. Then EFD roads are extracted with statistical lines structure line grouping and RFD roads are extracted with deep U-Net. Experiments on large scenes show that the proposed framework can complete the urban road network extraction task with the capability to 1) deal with interruptions caused by shadows and occlusions, 2) handle roads under construction with incomplete spectral and geometric characteristic.