STM-Graph: A Python Framework for Spatio-Temporal Mapping and Graph Neural Network Predictions
Urban spatio-temporal data present unique challenges for predictive analytics due to their dynamic and complex nature. We introduce STM-Graph, an open-source Python framework that transforms raw spatio-temporal urban event data into graph representations suitable for Graph Neural Network (GNN) training and prediction. STM-Graph integrates diverse spatial mapping methods, urban features from OpenStreetMap, multiple GNN models, comprehensive visualization tools, and a graphical user interface (GUI) suitable for professional and non-professional users. This modular and extensible framework facilitates rapid experimentation and benchmarking. It allows integration of new mapping methods and custom models, making it a valuable resource for researchers and practitioners in urban computing. The source code of the framework and GUI are available at: https://github.com/Ahghaffari/stm_graph and https://github.com/tuminguyen/stm_graph_gui.
Code (0)
등록된 구현이 없습니다.
Tasks
Graph Neural NetworkSimilar Papers 제목 키워드 기반
Spatiotemporal Air Quality Mapping in Urban Areas Using Sparse Sensor Data, Satellite Imagery, Meteorological Factors, and Spatial Features
Monitoring air pollution is crucial for protecting human health from exposure to harmful substances. Traditional methods of air quality monitoring, such as ground-based sensors and satellite-based remote sensing, face li…
SuperMap: A Spatio-Temporal SLAM System for Visual-Language Navigation
Robotic navigation in human environments requires a spatio-temporal semantic representation that can rec- oncile open-vocabulary perception with long-term environmental changes. While foundation models provide strong zer…
Unifying Watermarking via Dimension-Aware Mapping
Deep watermarking methods often share similar encoder-decoder architectures, yet differ substantially in their functional behaviors. We propose DiM, a new multi-dimensional watermarking framework that formulates watermar…
Spatio-temporal-spectral-angular observation model that integrates observations from UAV and mobile mapping vehicle for better urban mapping
In a complex urban scene, observation from a single sensor unavoidably leads to voids in observations, failing to describe urban objects in a comprehensive manner. In this paper, we propose a spatio-temporal-spectral-ang…
TSFEDL: A Python Library for Time Series Spatio-Temporal Feature Extraction and Prediction using Deep Learning (with Appendices on Detailed Network Architectures and Experimental Cases of Study)
The combination of convolutional and recurrent neural networks is a promising framework that allows the extraction of high-quality spatio-temporal features together with its temporal dependencies, which is key for time s…
Anomaly DetectionTime SeriesTime Series AnalysisTime Series Prediction