GlobalBuildingMap -- Unveiling the Mystery of Global Buildings

• URL: http://arxiv.org/abs/2404.13911v2
• Date: Wed, 22 May 2024 13:01:52 GMT
• Title: GlobalBuildingMap -- Unveiling the Mystery of Global Buildings
• Authors: Xiao Xiang Zhu, Qingyu Li, Yilei Shi, Yuanyuan Wang, Adam Stewart, Jonathan Prexl,
• Abstract summary: We created the highest resolution and highest accuracy building map ever created: the GlobalBuildingMap (GBM) A joint analysis of building maps and solar potentials indicates that rooftop solar energy can supply the global energy consumption need at a reasonable cost. We also identified a clear geospatial correlation between building areas and key socioeconomic variables.
• Score: 16.01396565010977
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Understanding how buildings are distributed globally is crucial to revealing the human footprint on our home planet. This built environment affects local climate, land surface albedo, resource distribution, and many other key factors that influence well-being and human health. Despite this, quantitative and comprehensive data on the distribution and properties of buildings worldwide is lacking. To this end, by using a big data analytics approach and nearly 800,000 satellite images, we generated the highest resolution and highest accuracy building map ever created: the GlobalBuildingMap (GBM). A joint analysis of building maps and solar potentials indicates that rooftop solar energy can supply the global energy consumption need at a reasonable cost. Specifically, if solar panels were placed on the roofs of all buildings, they could supply 1.1-3.3 times -- depending on the efficiency of the solar device -- the global energy consumption in 2020, which is the year with the highest consumption on record. We also identified a clear geospatial correlation between building areas and key socioeconomic variables, which indicates our global building map can serve as an important input to modeling global socioeconomic needs and drivers.

Related papers

• GloSoFarID: Global multispectral dataset for Solar Farm IDentification in satellite imagery [1.2021565114959365]
  This study develops the first comprehensive global dataset of multispectral satellite imagery of solar panel farms. It is intended to form the basis for training robust machine learning models, which can accurately map and analyze the expansion and distribution of solar panel farms globally.
  arXiv  Detail & Related papers  (2024-04-08T04:10:50Z)
• Points for Energy Renovation (PointER): A LiDAR-Derived Point Cloud Dataset of One Million English Buildings Linked to Energy Characteristics [0.705947228027401]
  This paper presents a building point cloud dataset that promotes a data-driven, large-scale understanding of the 3D representation of buildings and their energy characteristics. We generate building point clouds by intersecting building footprints with geo-referenced LiDAR data and link them with attributes from UK's energy performance database. To achieve a representative sample, we select one million buildings from a range of rural and urban regions across England, of which half a million are linked to energy characteristics.
  arXiv  Detail & Related papers  (2023-06-28T08:48:22Z)
• Improving day-ahead Solar Irradiance Time Series Forecasting by Leveraging Spatio-Temporal Context [46.72071291175356]
  Solar power harbors immense potential in mitigating climate change by substantially reducing CO$_2$ emissions. However, the inherent variability of solar irradiance poses a significant challenge for seamlessly integrating solar power into the electrical grid. In this paper, we put forth a deep learning architecture designed to harnesstemporal context using satellite data.
  arXiv  Detail & Related papers  (2023-06-01T19:54:39Z)
• 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)
• A General Purpose Neural Architecture for Geospatial Systems [142.43454584836812]
  We present a roadmap towards the construction of a general-purpose neural architecture (GPNA) with a geospatial inductive bias. We envision how such a model may facilitate cooperation between members of the community.
  arXiv  Detail & Related papers  (2022-11-04T09:58:57Z)
• Low Emission Building Control with Zero-Shot Reinforcement Learning [70.70479436076238]
  Control via Reinforcement Learning (RL) has been shown to significantly improve building energy efficiency. We show it is possible to obtain emission-reducing policies without a priori--a paradigm we call zero-shot building control.
  arXiv  Detail & Related papers  (2022-08-12T17:13:25Z)
• An Artificial Intelligence Dataset for Solar Energy Locations in India [6.454602468926006]
  India has set ambitious goals to install 300 gigawatts of solar energy capacity by 2030. Land use planners will need access to up-to-date and accurate geo-spatial information of PV infrastructure. We develop a spatially explicit machine learning model to map utility-scale solar projects across India.
  arXiv  Detail & Related papers  (2022-01-31T23:53:19Z)
• Jalisco's multiclass land cover analysis and classification using a novel lightweight convnet with real-world multispectral and relief data [51.715517570634994]
  We present our novel lightweight (only 89k parameters) Convolution Neural Network (ConvNet) to make LC classification and analysis. In this work, we combine three real-world open data sources to obtain 13 channels. Our embedded analysis anticipates the limited performance in some classes and gives us the opportunity to group the most similar.
  arXiv  Detail & Related papers  (2022-01-26T14:58:51Z)
• HyperionSolarNet: Solar Panel Detection from Aerial Images [0.7157957528875099]
  We use deep learning methods for automated detection of solar panel locations and their surface area using aerial imagery. Our work provides an efficient and scalable method for detecting solar panels, achieving an accuracy of 0.96 for classification and an IoU score of 0.82 for segmentation performance.
  arXiv  Detail & Related papers  (2022-01-06T15:43:13Z)
• Open government geospatial data on buildings for planning sustainable and resilient cities [0.0]
  We conduct a global study of 2D geospatial data on buildings that are released by governments for free access. We benchmark more than 140 releases from 28 countries containing above 100 million buildings, based on five dimensions: accessibility, richness, data quality, harmonisation, and relationships with other actors. We find that much building data released by governments is valuable for spatial analyses, but there are large disparities among them and not all instances are of high quality, harmonised, and rich in descriptive information.
  arXiv  Detail & Related papers  (2021-06-28T17:13:04Z)
• Deep Learning Guided Building Reconstruction from Satellite Imagery-derived Point Clouds [39.36437891978871]
  We present a reliable and effective approach for building model reconstruction from the point clouds generated from satellite images. Specifically, a deep-learning approach is adopted to distinguish the shape of building roofs in complex and yet noisy scenes. As the first effort to address the public need of large scale city model generation, the development is deployed as open source software.
  arXiv  Detail & Related papers  (2020-05-19T05:38:06Z)

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.