## 1 Introduction
The representative approaches are through the use of TF-IDF, which is based on statistical features [19; 20], TextRank, based on word graph models [21; 22], and Probabilistic Latent Semantic Analysis (PLSA) and Latent Dirichlet Allocation (LDA), based on topic models [23]. PLSA and LDA are the most widely used probabilistic techniques in topic modeling [24]. PLSA is a latent variable model based on co-occurrence data item-document matrices, also known as the ASPECT model [25]. The superiority of PLSA is demonstrated by its comparison with k-means and LSA [26]. As a variant or extension of PLSA, LDA uses Bayesian methods for parameter estimation to compensate for the incompleteness of PLSA, in terms of topic probability distribution. However, it is difficult to explain LDA without prior knowledge of the underlying topics and hyperparameters. All these approaches mentioned above ignore the most important semantic features of words and the semantic associations between words. Although pre-trained word embeddings are widely used in classification tasks, its application in topic modeling mainly focuses on probability techniques, such as in LDA [27; 28; 29; 30; 31], and there is also preliminary work using these embeddings to evaluate the consistency of topic models [32; 33]. The probability-based statistical topic modeling methods aforementioned are unable to capture the whole context of a document, as they usually consider only a single graph representation of a word [1]. Alternatively, the n-gram representation that considers multiple words simultaneously can be used, but the efficiency of the model rapidly decreases due to the dimension disaster [34]. Therefore, [PERSON] quantifies words as a vector, which takes into account the context and locates similar words in a similar space to address the limitations of this representation. Although this representation, based on pre-trained models, is widely used and its performance has been validated in recent text analysis, few attempts have yet been made to develop new topic models that are based on Bert. At present, only a few studies have adopted semantic embedding in topic analysis. A recent study uses Bert to generate text semantics as the input for topic classification [35]. While literature abstracts are a core corpus that reveal the distribution of research topics [36; 37], these classic scientific journals can extract topic terms to analyze the trends in the research fields [38; 39]. In conclusion, both the existing types of studies on geospatial data trend analysis have their limitations. Studies based on screening reviews require a lot of time and energy to screen and summarize all the literature. Methods based on bibliometric analysis are not suitable for discovering potential patterns in fields related to geospatial data. In addition, topic models, which are often used for trend analysis in other fields, are usually based on single-word vector representations that are non-contextual and sparse. In order to overcome these problems, this paper proposes a new topic modeling approach, which applies a new word embedding method in the field of computer linguistics to topic models and can help extract textual topics, namely the Bert-based Latent Semantic Analysis (Bert-LSA) topic modeling approach. It utilizes the Bert contextual word embedding algorithm and spherical k-means clustering to combine context embedding and clustering in a coordinated way, and finally assigns topics to documents. The proposed method is used to conduct a specific trend analysis of the technologies related to geospatial data, which can serve as an advanced and useful alternative method to extract meaningful topics involved in the current trend of geospatial data. The structure of this paper is as follows. In Section 2, the textual data sources and data pre-processing are introduced. A new topic modeling approach is proposed to compensate for the limitations of existing technologies, which will be discussed in detail in Section 3. Section 4 presents the results of the trend analysis.