Three-dimensional reconstruction is a key technology employed to represent virtual reality in the real world, which is valuable in computer vision. Large-scale 3D models have broad application prospects in the fields of smart cities, navigation, virtual tourism, disaster warning, and search-and-rescue missions. Unfortunately, most image-based studies currently prioritize the speed and accuracy of 3D reconstruction in indoor scenes. While there are some studies that address large-scale scenes, there has been a lack of systematic comprehensive efforts to bring together the advancements made in the field of 3D reconstruction in large-scale scenes. Hence, this paper presents a comprehensive overview of a 3D reconstruction technique that utilizes multi-view imagery from large-scale scenes. In this article, a comprehensive summary and analysis of vision-based 3D reconstruction technology for large-scale scenes are presented. The 3D reconstruction algorithms are extensively categorized into traditional and learning-based methods. Furthermore, these methods can be categorized based on whether the sensor actively illuminates objects with light sources, resulting in two categories: active and passive methods. Two active methods, namely, structured light and laser scanning, are briefly introduced. The focus then shifts to structure from motion (SfM), stereo matching, and multi-view stereo (MVS), encompassing both traditional and learning-based approaches. Additionally, a novel approach of neural-radiance-field-based 3D reconstruction is introduced. The workflow and improvements in large-scale scenes are elaborated upon. Subsequently, some well-known datasets and evaluation metrics for various 3D reconstruction tasks are introduced. Lastly, a summary of the challenges encountered in the application of 3D reconstruction technology in large-scale outdoor scenes is provided, along with predictions for future trends in development.

In the field of multi-view satellite photogrammetry, the neural radiance field (NeRF) method has received widespread attention due to its ability to provide continuous scene representation and realistic rendering effects. However, the satellite radiance field methods based on the NeRF are limited by the slow training speed of the original NeRF, and the scene reconstruction efficiency is low. Training for a single scene usually takes 8–10 h or even longer, which severely constrains the utilization and exploration of the NeRF approach within the domain of satellite photogrammetry. In response to the above problems, we propose an efficient neural radiance field method called SatelliteRF, which aims to quickly and efficiently reconstruct the earth’s surface through multi-view satellite images. By introducing innovative multi-resolution hash coding, SatelliteRF enables the model to greatly increase the training speed while maintaining high reconstruction quality. This approach allows for smaller multi-layer perceptron (MLP) networks, reduces the computational cost of neural rendering, and accelerates the training process. Furthermore, to overcome the challenges of illumination changes and transient objects encountered when processing multi-date satellite images, we adopt an improved irradiance model and learn transient embeddings for each image. This not only increases the adaptability of the model to illumination variations but also improves its ability to handle changing objects. We also introduce a loss function based on stochastic structural similarity (SSIM) to provide structural information of the scene for model training, which further improves the quality and detailed performance of the reconstructed scene. Through extensive experiments on the DFC 2019 dataset, we demonstrate that SatelliteRF is not only able to significantly reduce the training time for the same region from the original 8–10 h to only 5–10 min but also achieves better performance in terms of rendering and the reconstruction quality.

The prediction of vessel trajectories plays a crucial role in ensuring maritime safety and reducing maritime accidents. Substantial progress has been made in trajectory prediction tasks by adopting sequence modeling methods, containing recurrent neural networks (RNNs) and sequence-to-sequence networks (Seq2Seq). However, (1) most of these studies focus on the application of trajectory information, such as the longitude, latitude, course, and speed, while neglecting the impact of differing vessel features and behavioral preferences on the trajectories. (2) Challenges remain in acquiring these features and preferences, as well as enabling the model to sensibly integrate and efficiently express them. To address the issue, we introduce a novel deep framework VEPO-S2S, consisting of a Multi-level Vessel Trajectory Representation Module (Multi-Rep) and a Feature Fusion and Decoding Module (FFDM). Apart from the trajectory information, we first defined the Multi-level Vessel Characteristics in Multi-Rep, encompassing Shallow-level Attributes (vessel length, width, draft, etc.) and Deep-level Features (Sailing Location Preference, Voyage Time Preference, etc.). Subsequently, Multi-Rep was designed to obtain trajectory information and Multi-level Vessel Characteristics, applying distinct encoders for encoding. Next, the FFDM selected and integrated the above features from Multi-Rep for prediction by employing both a priori and a posteriori mechanisms, a Feature Fusion Component, and an enhanced decoder. This allows the model to efficiently leverage them and enhance overall performance. Finally, we conducted comparative experiments with several baseline models. The experimental results demonstrate that VEPO-S2S is both quantitatively and qualitatively superior to the models.

The 3D scene editing method based on neural implicit field has gained wide attention. It has achieved excellent results in 3D editing tasks. However, existing methods often blend the interaction between objects and scene environment. The change of scene appearance like shadows is failed to be displayed in the rendering view. In this paper, we propose an Object and Scene environment Interaction aware (OSI-aware) system, which is a novel two-stream neural rendering system considering object and scene environment interaction. To obtain illuminating conditions from the mixture soup, the system successfully separates the interaction between objects and scene environment by intrinsic decomposition method. To study the corresponding changes to the scene appearance from object-level editing tasks, we introduce a depth map guided scene inpainting method and shadow rendering method by point matching strategy. Extensive experiments demonstrate that our novel pipeline produce reasonable appearance changes in scene editing tasks. It also achieve competitive performance for the rendering quality in novel-view synthesis tasks.

The convergence of Digital Earth technologies and the field of marine environmental support represent a significant trend in global scientific and technological advancement. In response to the characteristics of marine environmental data such as sparsity, dynamic complexity, and multi-scale interdependence, and by integrating the evolution of international Digital Earth technology with China’s independent innovation practices, this study constructs three core methodological paradigms. The first is the element-integrated ontology, which proposes a hybrid representation paradigm of “spatiotemporal embedded field + ontology” to achieve integrated and dynamically evolving modeling of marine physical environments alongside geological, biological, chemical, and other multidimensional elements. The second is the cyclically nested spatiotemporal principle, introducing a spatiotemporal framework of “multi-period temporal decomposition and cross-scale structural transformation” to provide a physically interpretable and uncertainty-quantifiable predictive framework for complex marine phenomena. The third is the information transition and value-adding method, which establishes an information transition mechanism of “structure-semantics-utility” to facilitate the transformation and enhancement of massive raw data into high-value decision-making knowledge. Building on the above research, targeted policy recommendations are proposed across three dimensions: national-level infrastructure planning, data fusion development models, and value-adding channels for industrial applications. These recommendations aim to support the development and industrial implementation of an autonomous and controllable marine Digital Earth technology system in China.

Recently, few-shot object detection (FSOD) in remote sensing images (RSIs) has been attracting increasing research attention. However, mainstream FSOD methods for RSIs suffer from severe base-class performance degradation when adapting to novel classes. Meanwhile, most existing methods introduce additional learnable parameters, which limit their practical deployment in remote sensing applications. In this paper, we focus on lightweight high-performance FSOD in RSIs and propose HALD-FSOD, an enhanced two-stage fine-tuning approach (TFA) based detector that achieves remarkable novel-class performance improvement without forgetting pre-trained base-class knowledge. Through a comprehensive analysis of the TFA framework’s limitations in remote sensing scenarios, a hierarchical adaptive learning framework is proposed, which achieves an optimal balance between base-class knowledge preservation and novel-class feature adaptation. Considering the low inter-class separability of geospatial objects, we design an asymmetric margin-enhanced cosine classifier to learn a more discriminative decision boundary for novel classes. Furthermore, a loss-aware dynamic class weighting mechanism is developed to alleviate the learning imbalance between base and novel classes. Extensive experiments on DIOR and NWPU VHR 10.v2 datasets demonstrate that our proposed HALD-FSOD achieves competitive overall performance, with remarkable improvement in novel-class detection and negligible base-class degradation, without introducing any additional learnable parameters.