# Identification of Land Use Mix Using Point-Based Geospatial Data in Urban Areas
### Related Works
#### 1.1.2 LULC Classification  
There has been a significant amount of research on LULC classification in the literature. These studies have focused on utilizing various data sources such as point-based data and satellite imagery to identify LULC. For instance, the study by [PERSON] et al. [9] combined data from OpenStreetMap road networks, POI data, and satellite imagery to classify urban land use. They used a combination of probabilistic topic models and support vector machines and highlighted the potential for combining remote sensing and social media data for better LULC classification. Social networks have also been explored for the classification of LULC. [PERSON] et al. [10] explored the relationship between spatial technologies and social media in remote sensing, emphasizing the significance of using social media to monitor various aspects of the urban environment. The study highlighted the importance of social media for monitoring natural disasters and land cover. On the other hand, [PERSON] et al. [11] argued that volunteered geographic information (VGI) from sources like Foursquare could enhance real-time LULC classification. However, the accuracy of VGI information depends on the level of participation, which might be low in some regions. [PERSON] and [PERSON] [12] put forth the idea of a \"Visitation by Area attraction and Neighborhood\" competition to explain the reasons behind people's check-ins in specific neighborhoods. This study highlights the potential of incorporating information on people's check-ins in LULC classification models. By understanding this relationship, the authors aim to improve the accuracy of LULC classification. [PERSON] et al. [13] combined social media activity with convolutional neural networks (CNNs) to improve remote sensing for emergency response and rainfall event monitoring. The study demonstrated the effectiveness of using social media and CNNs for remote sensing; however, it is limited by the quality and timeliness of the social media data used. Other studies also utilized deep learning models for LULC classification. [PERSON] et al. [14] proposed a framework that uses POI data and the Word2 Vec model to identify urban land use on the scale of traffic analysis zones, offering a unique approach to incorporating social media data in LULC classification. [PERSON] et al. [15] used geotagged social network images with a Naive Bayes classifier to explore LULC; however, it is noted that the quality of the geotagged images may limit the performance of this method. Furthermore, [PERSON] and [PERSON] [16] used self-organizing maps (SOM) to cluster land uses in urban areas based on patterns of tweet activity, offering a novel approach to utilizing social media data. This study provides a unique perspective in the field of LULC classification by exploring the potential of using social media data in this context. However, it is essential to consider some potential limitations of this approach. For instance, the accuracy of the classification results may be limited by the quality and coverage of the tweet data. Additionally, [PERSON] et al. [17] proposed a deep learning model that utilizes a CNN and a constrained extreme learning machine (CELM), demonstrating promising results in LULC classification. The data used in the study consisted of remote sensing data, including high-resolution satellite images and ground-truth data. The authors tested the proposed model on several land-use types, including residential, commercial, industrial, and open spaces. The authors aimed to improve the accuracy and efficiency of LULC classification by integrating the two models. The study's results showed that the proposed model outperformed the traditional single model-based approaches, with promising results in classification accuracy. Lastly, [PERSON] et al. [18] presented a system based on a combination of Place2 Vec and POI to identify functional urban regions. This study explores the potential of incorporating POI data into deep-learning models for LULC classification.