Predicting Air Temperature from Volumetric Urban Morphology with Machine Learning

• URL: http://arxiv.org/abs/2501.09469v1
• Date: Thu, 16 Jan 2025 11:10:38 GMT
• Title: Predicting Air Temperature from Volumetric Urban Morphology with Machine Learning
• Authors: Berk Kıvılcım, Patrick Erik Bradley,
• Abstract summary: We introduce a method that converts CityGML data into voxels which works efficiently and fast in high resolution for large scale datasets such as cities. Those voxelized 3D city data from multiple cities and corresponding air temperature data are used to develop a machine learning model.
• Score: 0.0
• License:
• Abstract: In this study, we firstly introduce a method that converts CityGML data into voxels which works efficiently and fast in high resolution for large scale datasets such as cities but by sacrificing some building details to overcome the limitations of previous voxelization methodologies that have been computationally intensive and inefficient at transforming large-scale urban areas into voxel representations for high resolution. Those voxelized 3D city data from multiple cities and corresponding air temperature data are used to develop a machine learning model. Before the model training, Gaussian blurring is implemented on input data to consider spatial relationships, as a result the correlation rate between air temperature and volumetric building morphology is also increased after the Gaussian blurring. After the model training, the prediction results are not just evaluated with Mean Square Error (MSE) but some image similarity metrics such as Structural Similarity Index Measure (SSIM) and Learned Perceptual Image Patch Similarity (LPIPS) that are able to detect and consider spatial relations during the evaluation process. This trained model is capable of predicting the spatial distribution of air temperature by using building volume information of corresponding pixel as input. By doing so, this research aims to assist urban planners in incorporating environmental parameters into their planning strategies, thereby facilitating more sustainable and inhabitable urban environments.

Related papers

• Geospatial Data Fusion: Combining Lidar, SAR, and Optical Imagery with AI for Enhanced Urban Mapping [0.0]
  This study explores the integration of Lidar, Synthetic Aperture Radar (SAR), and optical imagery through advanced artificial intelligence techniques for enhanced urban mapping. The research employs Fully Convolutional Networks (FCNs) as the primary deep learning model for urban feature extraction. Key findings indicate that the FCN-PSO model achieved a pixel accuracy of 92.3% and a mean Intersection over Union (IoU) of 87.6%, surpassing traditional single-sensor approaches.
  arXiv  Detail & Related papers  (2024-12-25T22:17:31Z)
• OPUS: Occupancy Prediction Using a Sparse Set [64.60854562502523]
  We present a framework to simultaneously predict occupied locations and classes using a set of learnable queries. OPUS incorporates a suite of non-trivial strategies to enhance model performance. Our lightest model achieves superior RayIoU on the Occ3D-nuScenes dataset at near 2x FPS, while our heaviest model surpasses previous best results by 6.1 RayIoU.
  arXiv  Detail & Related papers  (2024-09-14T07:44:22Z)
• Machine Learning Framework for High-Resolution Air Temperature Downscaling Using LiDAR-Derived Urban Morphological Features [0.0]
  This study presents a data-driven framework for downscaling air temperature using datasets generated by UrbClim. To extract urban morphological features, first a three-dimensional building model was created using LiDAR data and deep learning models. The framework successfully estimated air temperatures at higher resolutions, enabling the identification of local air temperature patterns at street level.
  arXiv  Detail & Related papers  (2024-08-31T12:59:21Z)
• Deep Generative Data Assimilation in Multimodal Setting [0.1052166918701117]
  In this work, we propose SLAMS: Score-based Latent Assimilation in Multimodal Setting. We assimilate in-situ weather station data and ex-situ satellite imagery to calibrate the vertical temperature profiles, globally. Our work is the first to apply deep generative framework for multimodal data assimilation using real-world datasets.
  arXiv  Detail & Related papers  (2024-04-10T00:25:09Z)
• Observation-Guided Meteorological Field Downscaling at Station Scale: A Benchmark and a New Method [66.80344502790231]
  We extend meteorological downscaling to arbitrary scattered station scales and establish a new benchmark and dataset. Inspired by data assimilation techniques, we integrate observational data into the downscaling process, providing multi-scale observational priors. Our proposed method outperforms other specially designed baseline models on multiple surface variables.
  arXiv  Detail & Related papers  (2024-01-22T14:02:56Z)
• Over-the-Air Federated Learning and Optimization [52.5188988624998]
  We focus on Federated learning (FL) via edge-the-air computation (AirComp) We describe the convergence of AirComp-based FedAvg (AirFedAvg) algorithms under both convex and non- convex settings. For different types of local updates that can be transmitted by edge devices (i.e., model, gradient, model difference), we reveal that transmitting in AirFedAvg may cause an aggregation error. In addition, we consider more practical signal processing schemes to improve the communication efficiency and extend the convergence analysis to different forms of model aggregation error caused by these signal processing schemes.
  arXiv  Detail & Related papers  (2023-10-16T05:49:28Z)
• Semi-supervised Learning from Street-View Images and OpenStreetMap for Automatic Building Height Estimation [59.6553058160943]
  We propose a semi-supervised learning (SSL) method of automatically estimating building height from Mapillary SVI and OpenStreetMap data. The proposed method leads to a clear performance boosting in estimating building heights with a Mean Absolute Error (MAE) around 2.1 meters. The preliminary result is promising and motivates our future work in scaling up the proposed method based on low-cost VGI data.
  arXiv  Detail & Related papers  (2023-07-05T18:16:30Z)
• ClimaX: A foundation model for weather and climate [51.208269971019504]
  ClimaX is a deep learning model for weather and climate science. It can be pre-trained with a self-supervised learning objective on climate datasets. It can be fine-tuned to address a breadth of climate and weather tasks.
  arXiv  Detail & Related papers  (2023-01-24T23:19:01Z)
• Spatio-Temporal Graph Few-Shot Learning with Cross-City Knowledge Transfer [58.6106391721944]
  Cross-city knowledge has shown its promise, where the model learned from data-sufficient cities is leveraged to benefit the learning process of data-scarce cities. We propose a model-agnostic few-shot learning framework for S-temporal graph called ST-GFSL. We conduct comprehensive experiments on four traffic speed prediction benchmarks and the results demonstrate the effectiveness of ST-GFSL compared with state-of-the-art methods.
  arXiv  Detail & Related papers  (2022-05-27T12:46:52Z)
• Learning Similarity Metrics for Volumetric Simulations with Multiscale CNNs [25.253880881581956]
  We propose a similarity model based on entropy, which allows for the creation of physically meaningful ground truth distances. We create collections of fields from numerical PDE solvers and existing simulation data repositories. A multiscale CNN architecture that computes a volumetric similarity metric (VolSiM) is proposed.
  arXiv  Detail & Related papers  (2022-02-08T19:19:08Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.