GLObal Building heights for Urban Studies (UT-GLOBUS) for city- and street- scale urban simulations: Development and first applications

• URL: http://arxiv.org/abs/2205.12224v2
• Date: Thu, 15 Aug 2024 18:14:34 GMT
• Title: GLObal Building heights for Urban Studies (UT-GLOBUS) for city- and street- scale urban simulations: Development and first applications
• Authors: Harsh G. Kamath, Manmeet Singh, Neetiraj Malviya, Alberto Martilli, Liu He, Daniel Aliaga, Cenlin He, Fei Chen, Lori A. Magruder, Zong-Liang Yang, Dev Niyogi,
• Abstract summary: We introduce University of Texas - Global Building heights for Urban Studies (UT-GLOBUS) UT-GLOBUS is a dataset providing building heights and urban canopy parameters (UCPs) for more than 1200 cities or locales worldwide. Validation using LiDAR data from six US cities showed UT-GLOBUS-derived building heights had a root mean squared error (RMSE) of 9.1 meters.
• Score: 3.4244476729483013
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We introduce University of Texas - Global Building heights for Urban Studies (UT-GLOBUS), a dataset providing building heights and urban canopy parameters (UCPs) for more than 1200 cities or locales worldwide. UT-GLOBUS combines open-source spaceborne altimetry (ICESat-2 and GEDI) and coarse-resolution urban canopy elevation data with a machine-learning model to estimate building-level information. Validation using LiDAR data from six US cities showed UT-GLOBUS-derived building heights had a root mean squared error (RMSE) of 9.1 meters. Validation of mean building heights within 1-km^2 grid cells, including data from Hamburg and Sydney, resulted in an RMSE of 7.8 meters. Testing the UCPs in the urban Weather Research and Forecasting (WRF-Urban) model resulted in a significant improvement (55% in RMSE) in intra-urban air temperature representation compared to the existing table-based local climate zone approach in Houston, TX. Additionally, we demonstrated the dataset's utility for simulating heat mitigation strategies and building energy consumption using WRF-Urban, with test cases in Chicago, IL, and Austin, TX. Street-scale mean radiant temperature simulations using the Solar and LongWave Environmental Irradiance Geometry (SOLWEIG) model, incorporating UT-GLOBUS and LiDAR-derived building heights, confirmed the dataset's effectiveness in modeling human thermal comfort in Baltimore, MD (daytime RMSE = 2.85 C). Thus, UT-GLOBUS can be used for modeling urban hazards with significant socioeconomic and biometeorological risks, enabling finer scale urban climate simulations and overcoming previous limitations due to the lack of building information.

Related papers

• MambaDS: Near-Surface Meteorological Field Downscaling with Topography Constrained Selective State Space Modeling [68.69647625472464]
  Downscaling, a crucial task in meteorological forecasting, enables the reconstruction of high-resolution meteorological states for target regions. Previous downscaling methods lacked tailored designs for meteorology and encountered structural limitations. We propose a novel model called MambaDS, which enhances the utilization of multivariable correlations and topography information.
  arXiv  Detail & Related papers  (2024-08-20T13:45:49Z)
• A global product of fine-scale urban building height based on spaceborne lidar [14.651500878252723]
  We provide an up-to-date global product of urban building heights based on a fine grid size of 150 m around 2020. The estimated method of building height samples based on the GEDI data was effective with 0.78 of Pearson's r and 3.67 m of RMSE. This work will boost future urban studies across many fields including climate, environmental, ecological, and social sciences.
  arXiv  Detail & Related papers  (2023-10-22T16:51:15Z)
• Unified Data Management and Comprehensive Performance Evaluation for Urban Spatial-Temporal Prediction [Experiment, Analysis & Benchmark] [78.05103666987655]
  This work addresses challenges in accessing and utilizing diverse urban spatial-temporal datasets. We introduceatomic files, a unified storage format designed for urban spatial-temporal big data, and validate its effectiveness on 40 diverse datasets. We conduct extensive experiments using diverse models and datasets, establishing a performance leaderboard and identifying promising research directions.
  arXiv  Detail & Related papers  (2023-08-24T16:20:00Z)
• 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)
• A CNN regression model to estimate buildings height maps using Sentinel-1 SAR and Sentinel-2 MSI time series [0.0]
  In this study, we propose a supervised Multimodal Building Height Network (MBHR-Net) for estimating building heights at 10m spatial resolution using Sentinel-1 (S1) and Sentinel-2 (S2) time series. Our MBHR-Net aims to extract meaningful features from the S1 and S2 images to learn complex-temporal relationships between image patterns and building heights. The model is trained and tested in 10 cities in the Netherlands Root Mean Squared Error (RMSE), Intersection over Union (IoU), and R-squared (R2) score metrics are used to evaluate the performance of the model.
  arXiv  Detail & Related papers  (2023-07-03T22:16:17Z)
• DeepLCZChange: A Remote Sensing Deep Learning Model Architecture for Urban Climate Resilience [2.8037951156321372]
  We present a novel, data-driven deep learning architecture and pipeline, DeepLCZChange, to correlate airborne LiDAR data statistics with the Landsat 8 satellite's surface temperature product. A proof-of-concept numerical experiment utilizes corresponding remote sensing data for the city of New York to verify the cooling effect of urban forests.
  arXiv  Detail & Related papers  (2023-06-09T21:42:29Z)
• LibCity: A Unified Library Towards Efficient and Comprehensive Urban Spatial-Temporal Prediction [74.08181247675095]
  There are limitations in the existing field, including open-source data being in various formats and difficult to use. We propose LibCity, an open-source library that offers researchers a credible experimental tool and a convenient development framework.
  arXiv  Detail & Related papers  (2023-04-27T17:19:26Z)
• An evaluation of deep learning models for predicting water depth evolution in urban floods [59.31940764426359]
  We compare different deep learning models for prediction of water depth at high spatial resolution. Deep learning models are trained to reproduce the data simulated by the CADDIES cellular-automata flood model. Our results show that the deep learning models present in general lower errors compared to the other methods.
  arXiv  Detail & Related papers  (2023-02-20T16:08:54Z)
• Urban precipitation downscaling using deep learning: a smart city application over Austin, Texas, USA [0.11726720776908518]
  Urban downscaling is a link to transfer the knowledge from coarser climate information to city scale assessments. We show the development of a high-resolution gridded precipitation product (300 m) from a coarse (10 km) satellite-based product (JAXA GsMAP) Our results have implications for developing high-resolution gridded-precipitation urban datasets and the future planning of smart cities.
  arXiv  Detail & Related papers  (2022-08-15T12:42:20Z)
• Robust Self-Tuning Data Association for Geo-Referencing Using Lane Markings [44.4879068879732]
  This paper presents a complete pipeline for resolving ambiguities during the data association. Its core is a robust self-tuning data association that adapts the search area depending on the entropy of the measurements. We evaluate our method on real data from urban and rural scenarios around the city of Karlsruhe in Germany.
  arXiv  Detail & Related papers  (2022-07-28T12:29:39Z)
• Times Series Forecasting for Urban Building Energy Consumption Based on Graph Convolutional Network [20.358180125750046]
  Building industry accounts for more than 40% of energy consumption in the United States. UBEM is the foundation to support the design of energy-efficient communities. Data-driven models integrated engineering or physical knowledge can significantly improve the urban building energy simulation.
  arXiv  Detail & Related papers  (2021-05-27T19:02:04Z)

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.