Automated methods for building function classification are essential due to restricted access to official building use data. Existing approaches utilize traditional Natural Language Processing (NLP) techniques to analyze textual data representing human activities, but they struggle with the ambiguity of semantic contexts. In contrast, Large Language Models (LLMs) excel at capturing the broader context of language. This study presents a method that uses LLMs to interpret OpenStreetMap (OSM) tags, combining them with physical and spatial metrics to classify urban building functions. We employed an XGBoost model trained on 32 features from six city datasets to classify urban building functions, demonstrating varying F1 scores from 67.80% in Madrid to 91.59% in Liberec. Integrating LLM embeddings enhanced the model's performance by an average of 12.5% across all cities compared to models using only physical and spatial metrics. Moreover, integrating LLM embeddings improved the model's performance by 6.2% over models that incorporate OSM tags as one-hot encodings, and when predicting based solely on OSM tags, the LLM approach outperforms traditional NLP methods in 5 out of 6 cities. These results suggest that deep contextual understanding, as captured by LLM embeddings more effectively than traditional NLP approaches, is beneficial for classification. Finally, a Pearson correlation coefficient of approximately -0.858 between population density and F1-scores suggests that denser areas present greater classification challenges. Moving forward, we recommend investigation into discrepancies in model performance across and within cities, aiming to identify generalized models.

Multi-representation databases (MRDBs) are used in several geographical information system applications for different purposes. MRDBs are mainly obtained through model and cartographic generalizations. Simplification is the essential operator of cartographic generalization, and streams and lakes are essential features in hydrography. In this study, a new algorithm was developed for the simplification of streams and lakes. In this algorithm, deviation angles and error bands are used to determine the characteristic vertices and the planimetric accuracy of the features, respectively. The algorithm was tested using a high-resolution national hydrography dataset of Pomme de Terre, a sub-basin in the USA. To assess the performance of the new algorithm, the Bend Simplify and Douglas-Peucker algorithms, the medium-resolution hydrography dataset of the sub-basin, and Töpfer’s radical law were used. For quantitative analysis, the vertex numbers, the lengths, and the sinuosity values were computed. Consequently, it was shown that the new algorithm was able to meet the main requirements (i.e., accuracy, legibility and aesthetics, and storage).

Eski dönemlere ait birçok arkeolojik alanı ve kültürel miras yapılarını içinde barındıran Şanlıurfa, Şuayip Peygamber’in yaşadığı yer olduğuna olan inançtan dolayı Şuayip Şehri olarak bilinen arkeolojik alanı da bünyesinde barındırmaktadır. Arkeolojik alanlar ve bu alanlarda gün yüzüne çıkarılan yapılar hem kültür turizmi açısından hem de bilimsel anlamda önem taşımaktadır. Bu çalışmada yersel fotogrametri ve İnsansız Hava Aracı (İHA) fotogrametrisi kullanılarak Şuayip Şehri’nde bulunan bir yapıya ait üç boyutlu model üretilmiştir. İki farklı veri setinin de kendilerine has dezavantajlarından dolayı birleşik veri setiyle yoğun nokta bulutu ve üç boyutlu model üretimi gerçekleşmiştir. İHA’dan elde edilen fotoğrafların yapının üst kısmını, yerden çekilen fotoğrafların ise yapının cephelerini tam olarak içermesinden dolayı birleşik model münferit kaynaklardan elde edilen modellere göre daha anlamlı oluşturulmuştur. 216 adet yerden ve 26 adet İHA’dan elde edilen fotoğraf ile nokta bulutu üretimi ve modelleme işlemi yapılmıştır. İki farklı veri setindeki koordinat sistemleri farklı olduğu için İteratif En Yakın Nokta (İEYN) yöntemiyle noktalar arasında dönüşüm yapılmıştır. Çalışma sonucunda yerden çekilen fotoğraflardan yaklaşık 7.6 milyon, İHA’dan çekilen fotoğraflardan ise yaklaşık 2.2 milyon nokta üretilmiştir.

Cartographic design is fundamental to effective mapmaking, requiring adherence to principles such as visual hierarchy, symbolization, and color theory to convey spatial information accurately and intuitively, while Artificial Intelligence (AI) and Large Language Models (LLMs) have transformed various fields, their application in cartographic design remains underexplored. This study assesses the capabilities of a multimodal advanced LLM, GPT-4o, in understanding and suggesting cartographic design elements, focusing on adherence to established cartographic principles. Two assessments were conducted: a text-to-text evaluation and an image-to-text evaluation. In the text-to-text assessment, GPT-4o was presented with 15 queries derived from key concepts in cartography, covering classification, symbolization, visual hierarchy, color theory, and typography. Each query was posed multiple times under different temperature settings to evaluate consistency and variability. In the image-to-text evaluation, GPT-4o analyzed maps containing deliberate cartographic errors to assess its ability to identify issues and suggest improvements. The results indicate that GPT-4o demonstrates general reliability in text-based tasks, with variability influenced by temperature settings. The model showed proficiency in classification and symbolization tasks but occasionally deviated from theoretical expectations. In visual hierarchy and layout, the model performed consistently, suggesting appropriate design choices. In the image-to-text assessment, GPT-4o effectively identified critical design flaws such as inappropriate color schemes, poor contrast and misuse of shape and size variables, offering actionable suggestions for improvement. However, limitations include dependency on input quality and challenges in interpreting nuanced spatial relationships. The study concludes that LLMs like GPT-4o have significant potential in cartographic design, particularly for tasks involving creative exploration and routine design support. Their ability to critique and generate cartographic elements positions them as valuable tools for enhancing human expertise. Further research is recommended to enhance their spatial reasoning capabilities and expand their use of visual variables beyond color, thereby improving their applicability in professional cartographic workflows.