Semantic enrichment of building functions through geospatial data integration and ontological inference

Memduhoglu, Abdulkadir; Basaraner, Melih

doi:10.1177/23998083231206165

Cited by 3 publications

References 38 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

Enriching building function classification using Large Language Model embeddings of OpenStreetMap Tags

Memduhoğlu,

Fulman,

Zipf

2024

Earth Sci Inform

View full text Add to dashboard Cite

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.

show abstract

Enriching building function classification using Large Language Model embeddings of OpenStreetMap Tags

Memduhoğlu,

Fulman,

Zipf

2024

Earth Sci Inform

View full text Add to dashboard Cite

show abstract

Enriching building function classification using Large Language Model embeddings of OpenStreetMap Tags

Memduhoğlu,

Fulman,

Zipf

2024

Preprint

View full text Add to dashboard Cite

Automated methods for building function classification are becoming necessary due to restricted access to accurate building use data. Traditional on-site surveys conducted by government agencies are costly and can be influenced by subjective judgment, highlighting the need for more objective and cost-effective approaches. Existing approaches utilize Natural Language Processing (NLP) techniques such as text similarity and topic modeling, which typically struggle with the ambiguity of semantic contexts in textual data representing human activities. This study introduces a method for classifying urban building functions by integrating physical and spatial metrics with contextual embeddings from OpenStreetMap (OSM) tags, employing Large Language Models (LLMs) to improve the precision and relevance of function classifications in urban settings. 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, and by 6.2% over models that incorporate direct tags from OSM. This suggests that deep contextual understanding is beneficial for classification. Moving forward, we suggest investigating the discrepancies in classification accuracy across different urban contexts, which is a common occurrence in existing research.

show abstract

Semi-Automatic Data Enrichment for Open Street Map (Osm) Using Deep Learning Algorithms

Şenol

2024

Mugla Journal of Science and Technology

View full text Add to dashboard Cite

It is crucial to obtain continuous data on unplanned urbanization regions in order to develop precise plans for future studies in these regions. An unplanned urbanization area was selected for analysis, and road extraction was performed using very high-resolution unmanned aerial vehicle (UAV) images. In this regard, the Sat2Graph deep learning model was employed, utilizing the object detection tool integrated within the deep learning package published by ArcGIS Pro software, for the purpose of road extraction from a very high-resolution UAV image. The high-resolution UAV images were subjected to analysis using the photogrammetry method, with the results obtained through the application of the Sat2Graph deep learning model. The resulting road extraction was employed for the purpose of data enhancement on OpenStreetMap (OSM). This will facilitate the expeditious and precise implementation of data updates conducted by volunteers. It should be noted that the recall, F1 score, precision ratio/uncertainty accuracy, average producer accuracy, and intersection over union of products were automatically extracted with the algorithm and determined to be 0.816, 0.827, 0.838, 0.792, and 0.597, respectively.

show abstract

Semantic enrichment of building functions through geospatial data integration and ontological inference

Cited by 3 publications

References 38 publications

Enriching building function classification using Large Language Model embeddings of OpenStreetMap Tags

Enriching building function classification using Large Language Model embeddings of OpenStreetMap Tags

Enriching building function classification using Large Language Model embeddings of OpenStreetMap Tags

Semi-Automatic Data Enrichment for Open Street Map (Osm) Using Deep Learning Algorithms

Contact Info

Product

Resources

About