A global product of fine-scale urban building height based on spaceborne lidar

• URL: http://arxiv.org/abs/2310.14355v1
• Date: Sun, 22 Oct 2023 16:51:15 GMT
• Title: A global product of fine-scale urban building height based on spaceborne lidar
• Authors: Xiao Ma, Guang Zheng, Chi Xu, L. Monika Moskal, Peng Gong, Qinghua Guo, Huabing Huang, Xuecao Li, Yong Pang, Cheng Wang, Huan Xie, Bailang Yu, Bo Zhao, Yuyu Zhou
• Abstract summary: 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.
• Score: 14.651500878252723
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Characterizing urban environments with broad coverages and high precision is more important than ever for achieving the UN's Sustainable Development Goals (SDGs) as half of the world's populations are living in cities. Urban building height as a fundamental 3D urban structural feature has far-reaching applications. However, so far, producing readily available datasets of recent urban building heights with fine spatial resolutions and global coverages remains a challenging task. Here, we provide an up-to-date global product of urban building heights based on a fine grid size of 150 m around 2020 by combining the spaceborne lidar instrument of GEDI and multi-sourced data including remotely sensed images (i.e., Landsat-8, Sentinel-2, and Sentinel-1) and topographic data. Our results revealed that 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 in comparison to the reference data. The mapping product also demonstrated good performance as indicated by its strong correlation with the reference data (i.e., Pearson's r = 0.71, RMSE = 4.60 m). Compared with the currently existing products, our global urban building height map holds the ability to provide a higher spatial resolution (i.e., 150 m) with a great level of inherent details about the spatial heterogeneity and flexibility of updating using the GEDI samples as inputs. This work will boost future urban studies across many fields including climate, environmental, ecological, and social sciences.

Related papers

• 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)
• UrbanBIS: a Large-scale Benchmark for Fine-grained Urban Building Instance Segmentation [50.52615875873055]
  UrbanBIS comprises six real urban scenes, with 2.5 billion points, covering a vast area of 10.78 square kilometers. UrbanBIS provides semantic-level annotations on a rich set of urban objects, including buildings, vehicles, vegetation, roads, and bridges. UrbanBIS is the first 3D dataset that introduces fine-grained building sub-categories.
  arXiv  Detail & Related papers  (2023-05-04T08:01:38Z)
• Building Coverage Estimation with Low-resolution Remote Sensing Imagery [65.95520230761544]
  We propose a method for estimating building coverage using only publicly available low-resolution satellite imagery. Our model achieves a coefficient of determination as high as 0.968 on predicting building coverage in regions of different levels of development around the world.
  arXiv  Detail & Related papers  (2023-01-04T05:19:33Z)
• 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)
• GLObal Building heights for Urban Studies (UT-GLOBUS) for city- and street- scale urban simulations: Development and first applications [3.4244476729483013]
  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.
  arXiv  Detail & Related papers  (2022-05-24T17:34:14Z)
• SensatUrban: Learning Semantics from Urban-Scale Photogrammetric Point Clouds [52.624157840253204]
  We introduce SensatUrban, an urban-scale UAV photogrammetry point cloud dataset consisting of nearly three billion points collected from three UK cities, covering 7.6 km2. Each point in the dataset has been labelled with fine-grained semantic annotations, resulting in a dataset that is three times the size of the previous existing largest photogrammetric point cloud dataset.
  arXiv  Detail & Related papers  (2022-01-12T14:48:11Z)
• CitySurfaces: City-Scale Semantic Segmentation of Sidewalk Materials [6.573006589628846]
  Most cities lack a spatial catalog of their surfaces due to the cost-prohibitive and time-consuming nature of data collection. Recent advancements in computer vision, together with the availability of street-level images, provide new opportunities for cities to extract large-scale built environment data. We propose CitySurfaces, an active learning-based framework that leverages computer vision techniques for classifying sidewalk materials.
  arXiv  Detail & Related papers  (2022-01-06T21:58:37Z)
• GANmapper: geographical content filling [0.0]
  We present a new method to create spatial data using a generative adversarial network (GAN) Our contribution uses coarse and widely available geospatial data to create maps of less available features at the finer scale in the built environment. We employ land use data and road networks as input to generate building footprints, and conduct experiments in 9 cities around the world.
  arXiv  Detail & Related papers  (2021-08-07T05:50:54Z)
• A Semantic Segmentation Network for Urban-Scale Building Footprint Extraction Using RGB Satellite Imagery [1.9400948599830012]
  Urban areas consume over two-thirds of the world's energy and account for more than 70 percent of global CO2 emissions. We propose a modified DeeplabV3+ module with a Dilated ResNet backbone to generate masks of building footprints from only three-channel RGB satellite imagery. We achieve state-of-the-art performance across three standard benchmarks and demonstrate that our method is agnostic to the scale, resolution, and urban density of satellite imagery.
  arXiv  Detail & Related papers  (2021-04-02T22:32:04Z)
• Towards Semantic Segmentation of Urban-Scale 3D Point Clouds: A Dataset, Benchmarks and Challenges [52.624157840253204]
  We present an urban-scale photogrammetric point cloud dataset with nearly three billion richly annotated points. Our dataset consists of large areas from three UK cities, covering about 7.6 km2 of the city landscape. We evaluate the performance of state-of-the-art algorithms on our dataset and provide a comprehensive analysis of the results.
  arXiv  Detail & Related papers  (2020-09-07T14:47:07Z)

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.