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Related Concept Videos

Types of Global Positioning System Surveys01:30

Types of Global Positioning System Surveys

GPS surveying methods vary in application, accuracy, and data collection techniques, catering to diverse surveying and mapping needs. Static GPS, kinematic GPS, and real-time kinematic (RTK) surveying are widely used. Each technique offers distinct advantages.Static GPS involves placing one receiver at a known reference point and another at the target point. It collects exact positional data by observing multiple satellite ranges over an extended period, achieving centimeter-level accuracy for...
Thematic Layering in GIS01:30

Thematic Layering in GIS

In the past, planning projects such as schools or public facilities required extensive manual effort to gather and compile data. Information such as property boundaries, soil characteristics, road networks, zoning regulations, and flood zones had to be sourced individually from courthouses, utility providers, and registry offices. Assembling these datasets into a coherent format often took several months, delaying project timelines.The introduction of Geographic Information Systems (GIS)...
Design Example: Identifying the Locations of Monuments in the Field Using Global Positioning System Device01:30

Design Example: Identifying the Locations of Monuments in the Field Using Global Positioning System Device

Surveyors use Global Positioning System (GPS) technology to measure the precise location and elevation of points on Earth. In a recent survey, GPS receivers were used to determine the coordinates and elevations of two park monuments. The process involved careful mission planning, data collection, and correction to ensure accuracy. The survey began with mission planning to identify optimal satellite visibility and minimize Position Dilution of Precision (PDOP). A geodetic control point served as...
Levels of Use of a GIS01:29

Levels of Use of a GIS

Geographic Information Systems (GIS) operate across three levels of application, each representing an increasing degree of complexity: data management, analysis, and prediction. These levels reflect the expanding functionality and versatility of GIS technology in handling spatial data for diverse purposes.Data ManagementAt its foundational level, GIS serves as a tool for data management, enabling the input, storage, retrieval, and organization of spatial data. This level is often employed in...
Design Example: Sustainability in Concrete Building01:26

Design Example: Sustainability in Concrete Building

As the construction industry moves towards more eco-friendly practices, concrete's adaptability and its ability to incorporate sustainable features make it a key material in the drive towards greener building solutions.
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Selected Data About Geographic Locations01:25

Selected Data About Geographic Locations

Geographic Information Systems (GIS) rely on two core types of data: spatial data and attribute data.Spatial DataSpatial data defines the physical location of features within a coordinate system, typically expressed in terms of latitude and longitude. It provides precise positioning for elements like roads, rivers, or buildings.Attribute DataAttribute data complements spatial data by adding descriptive information about these features. For example, a road's spatial data includes its start and...

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Related Experiment Video

Updated: Jun 6, 2026

Façade-Level Monitoring of CO2 Variability under Urban Heat Island Conditions using Low-Cost Sensor Data Loggers
07:12

Façade-Level Monitoring of CO2 Variability under Urban Heat Island Conditions using Low-Cost Sensor Data Loggers

Published on: December 12, 2025

High Spatial Resolution Building Characteristics for the Global South: Insights from the Google Open Buildings

Rhorom Priyatikanto1, Heather Chamberlain1, Maksym Bondarenko1

  • 1University of Southampton School of Geography and Environmental Science, Southampton, England, SO17 1BJ, UK.

Gates Open Research
|June 5, 2026
PubMed
Summary

This study created a 100-m resolution building characteristics dataset for the Global South from Google Open Buildings Temporal data. The dataset shows strong correlations with population and other building data, but requires smoothing for time-series analysis.

Keywords:
building characteristicsbuilt environmentgeospatial data

Related Experiment Videos

Last Updated: Jun 6, 2026

Façade-Level Monitoring of CO2 Variability under Urban Heat Island Conditions using Low-Cost Sensor Data Loggers
07:12

Façade-Level Monitoring of CO2 Variability under Urban Heat Island Conditions using Low-Cost Sensor Data Loggers

Published on: December 12, 2025

Area of Science:

  • Geospatial analysis
  • Urban remote sensing
  • Environmental modeling

Background:

  • Growing need for detailed built-up area data for population modeling, urban planning, and environmental research.
  • Existing datasets have limitations, especially in the Global South, due to rapid population changes.
  • High spatial resolution building data is crucial for understanding urban dynamics.

Purpose of the Study:

  • To derive and validate a high-resolution, multi-temporal dataset of building characteristics for the Global South.
  • To assess the utility of the Google Open Buildings Temporal dataset for urban analysis.
  • To provide a valuable resource for urban planning and population studies.

Main Methods:

  • Processed the Google Open Buildings Temporal (OBT) dataset to derive six annual 100-m spatial resolution building characteristic datasets (building count, perimeter, area, volume, height variance, mean distance to nearest edges).
  • Utilized arithmetic operations, convolutions, and spatial aggregation for data derivation.
  • Validated the derived dataset against existing large-scale spatial datasets and assessed temporal consistency, exploring polynomial fitting for smoothing.

Main Results:

  • The new dataset strongly correlated with the Google Open Buildings Polygons dataset (building count: r=0.88, building area: r=0.90).
  • Systematic perimeter underestimation was observed in dense areas due to blending effects; weaker correlations with other datasets were attributed to methodological differences.
  • Strong positive correlations (r > 0.8) were found between building count, area, volume, and population; temporal analysis revealed significant fluctuations, with second-order polynomial fitting optimal for smoothing.

Conclusions:

  • A validated 100-m resolution building characteristics dataset for the Global South (2016-2023) was successfully produced from Google OBT.
  • The dataset demonstrates consistency with similar large-scale spatial datasets.
  • Temporal fluctuations highlight the need for further processing for robust time-series applications.