# A Transformer-Based Approach for Efficient Geometric Feature Extraction from Vector Shape Data
## 1 Introduction
(2021) proposed a classification framework to address the challenges arising from encoding imprecision, which could otherwise lead to spatial relationship computation errors, thereby increasing the challenges posed by geometric uncertainty and semantic ambiguity [24]. They advocated improvements in spatial operator selection and geographic uncertainty handling to enhance the system's accuracy. [PERSON] et al. (2021) emphasized the need for shape feature encoding in geographic analysis Q&A systems to effectively translate natural language queries into complex spatial analyses, particularly for issues involving distance or spatial coverage [25]. However, existing research on shape feature encoding and representation is often designed for specific tasks or data types and fails to provide a universal solution for formalizing linear feature sets, with many methods requiring manual intervention. This limits their adaptability to different types of linear features. Current feature extraction methods typically rely on human experts to set parameters and features, thereby increasing the workload and limiting their wider application. With the advent of big data and geographic information systems, automated pattern recognition methods have become increasingly vital. Therefore, it is crucial to develop a solution that minimizes human intervention, adapts to various vector shape types, and directly processes vector data. Deep learning has emerged as a powerful paradigm for feature extraction in various domains, including computer vision and natural language processing. In the context of geospatial data analysis, deep learning models have demonstrated remarkable success in tasks such as object detection, image segmentation, and point cloud classification. These models, often based on convolutional neural networks or recurrent neural networks, can automatically learn hierarchical feature representations from raw data, eliminating the need for manual feature engineering. For example, recent studies have shown the effectiveness of deep learning in tasks related to shape extraction and classification, such as rebar counting in reinforced concrete structures [26] and window detection and state classification [27]. These successes highlight the potential of deep learning to address the challenges of geometric feature extraction from vector shape data, motivating our adoption of a transformer-based deep learning approach. Designing a unique feature extraction method for each task is a natural idea, as seen in computer vision and natural language processing, where systems initially depended on manually designed feature extractors such as SIFT [28] and HOG [29] or task-specific deep learning models such as Faster R-CNN [30]. These schemes require extensive expert knowledge and parameter tuning. However, since the introduction of backbone networks such as Res2 Net [31], ImageNet-based pre-training has become the standard for visual tasks [32; 33; 34; 35]. These models learn general features from large-scale image data and are subsequently applied to various downstream tasks [36; 37]. Transfer learning and fine-tuning have become effective strategies for addressing different visual problems [38], unifying the feature extraction process, simplifying model design, and significantly enhancing development and experimentation efficiency while reducing computational resources and training time. Similarly, in the realm of natural language processing, early methods relied heavily on statistical features, such as TF-IDF [39] or Word2 Vec [40] embeddings, often combined with specific structures, such as RNN [41] or LSTM [42], to model specific tasks. However, the natural language processing field has shifted from task-specific designs to general models with the rise of pre-trained language models, such as BERT [43] and GPT [44; 45; 46]. These models, pre-trained on massive text corpora in an unsupervised manner, greatly enhance natural language understanding capabilities and support new application methods such as transfer learning and zero-shot learning.