# Spatiotemporal Contextual 3D Semantic Segmentation for Intelligent Outdoor Mining
## 1 Introduction  
The intelligent construction of mining areas [1] has developed rapidly in recent years, with artificial intelligence technologies such as autonomous driving being integrated into mining operations [2]. These innovations have shown tremendous potential in ensuring safety, boosting production efficiency, and improving operational stability, gradually positioning intelligent mining as a key driver of technological progress in the mining industry [3]. For example, autonomous trucks and excavation robots have significantly reduced human labor costs and minimized safety risks in hazardous environments. In particular,the use of 3D point cloud data [4], which provides comprehensive spatial coordinate information, has become central to the intelligent construction of mining areas. By accurately capturing the geometric features of the terrain and mining scenes [5], point cloud data is vital for autonomous vehicles and robotic systems operating in mining environments, enabling them to navigate, detect obstacles, and plan optimal paths [6]. Three-dimensional point cloud semantic segmentation plays a crucial role in this process by providing pixel-level analysis of the environment. It helps to identify critical information, such as terrain structure and object distribution, which is essential for autonomous systems to make real-time decisions, including path planning, obstacle avoidance, and operational optimization. Therefore, achieving accurate 3D semantic segmentation is pivotal for the successful implementation of intelligent mining systems. The goal of 3D semantic segmentation is to assign a unique category label to each point in a point cloud, providing more accurate and detailed environmental information. As artificial intelligence continues to develop, numerous point cloud semantic segmentation algorithms have been proposed [7; 8]. These methods can be broadly categorized into two types: projection-based and point-based methods. Projection-based approaches [9; 10] project 3D point cloud data onto a 2D plane, then apply traditional algorithms to label the semantic categories of the projected points. In contrast, point-based methods [11; 12] directly extract features from the 3D point cloud, allowing them to better preserve spatial geometric features and achieve superior performance in evaluation metrics. For instance, PointNet [13; 14], a pioneering method in this domain, addressed the challenges of point cloud rotation and unordered data using spatial transformation networks and multi-layer perceptrons (MLPs). More recent advancements, such as SphereFormer [15], leverage optimized attention mechanisms to enhance segmentation accuracy, especially for distant points. Point-based methods, due to their ability to preserve more feature information, are the focus of this paper. However, the inherent sparsity of point cloud data remains a significant challenge for accurate semantic segmentation [16], particularly in outdoor mining environments where the boundary features between ground and material piles are often ambiguous [2]. This sparsity can blur the boundaries of terrain features, making segmentation even more difficult. One promising approach to mitigate this issue is to aggregate temporal data, thereby leveraging long-term spatiotemporal features [17]. Recent advances have shown that integrating spatiotemporal AI architectures with resource-aware learning can significantly enhance recognition tasks under sparse or noisy sensor input conditions, particularly in structural health monitoring and environmental analytics [18; 19]. Several studies [20; 21; 22] have explored the stacking of historical frame data to capture these spatiotemporal features, but these methods often come with substantial increases in memory and computational costs. Other approaches, such as the introduction of new loss functions [23] or knowledge distillation techniques [24], have also been explored, but they tend to focus on specific tasks and fail to fully address the spatiotemporal relationships between historical and current data.