Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Manipulation and Analysis01:21

Manipulation and Analysis

17
GIS manipulation and analysis functions are vital for decision-making and planning. These activities range from data retrieval tasks, such as selecting information based on specific criteria, to advanced analytical techniques that address complex spatial problems.One critical GIS analysis method is overlaying, which combines multiple data layers to examine impacts. For example, overlaying a river-dammed lake boundary with road networks can identify affected infrastructure. Another common...
17
Levels of Use of a GIS01:29

Levels of Use of a GIS

40
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...
40
Selected Data About Geographic Locations01:25

Selected Data About Geographic Locations

23
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...
23
Methods of Obtaining Topography01:25

Methods of Obtaining Topography

43
Topography involves measuring and mapping land elevations, natural features, and artificial structures to create accurate representations of the terrain. Topographic surveying relies on traditional and modern methods, each with distinct advantages and limitations.Traditional Surveying Methods:Transit stadia surveys and plane table surveys were widely used traditional surveying methods. These techniques relied on instruments like theodolites and stadia rods for measuring distances and angles,...
43
Thematic Layering in GIS01:30

Thematic Layering in GIS

28
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)...
28
GIS Software, Hardware, and Sources of GIS Data01:23

GIS Software, Hardware, and Sources of GIS Data

39
A Geographic Information System (GIS) combines specialized software and hardware to effectively manage, analyze, and present spatial and related data. GIS software includes critical functionalities such as a user interface for easy navigation, database management tools for handling spatial and attribute data, and data retrieval features for efficient access. Analytical tools transform raw data into insights, while display functions produce maps and reports in various formats for effective...
39

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Machine Learning Approaches for Geospatial Modeling of Urban Land Surface Temperature: Assessing Geographical Compactness, Interpretability, and Causal Inference.

Sensors (Basel, Switzerland)·2025
Same author

Corroded Bolt Identification Using Mask Region-Based Deep Learning Trained on Synthesized Data.

Sensors (Basel, Switzerland)·2022
Same author

Correction: Pham et al. Bolt-Loosening Monitoring Framework Using an Image-Based Deep Learning and Graphical Model. <i>Sensors</i> 2020, <i>20</i>, 3382.

Sensors (Basel, Switzerland)·2021
Same author

Automatic Impervious Surface Area Detection Using Image Texture Analysis and Neural Computing Models with Advanced Optimizers.

Computational intelligence and neuroscience·2021
Same author

Roles of Chitosan in Green Synthesis of Metal Nanoparticles for Biomedical Applications.

Nanomaterials (Basel, Switzerland)·2021
Same author

Bolt-Loosening Monitoring Framework Using an Image-Based Deep Learning and Graphical Model.

Sensors (Basel, Switzerland)·2020
Same journal

RETRACTED: Zhang et al. A Novel Framework for Reconstruction and Imaging of Target Scattering Centers via Wide-Angle Incidence in Radar Networks. <i>Sensors</i> 2025, <i>25</i>, 6802.

Sensors (Basel, Switzerland)·2026
Same journal

Enhancing Unsupervised Multi-Source Domain Adaptation for Person Re-Identification via Mixture of Experts and Graph-Based Relation.

Sensors (Basel, Switzerland)·2026
Same journal

Development of an Instrumented Glove for Palmar Pressure Assessment in Kayakers.

Sensors (Basel, Switzerland)·2026
Same journal

Development and Experimental Validation of an Autonomous IoT-Based Monitoring System for Real-Time Water Quality Assessment in the Amazon River.

Sensors (Basel, Switzerland)·2026
Same journal

Semi-Supervised Adversarial Learning Framework for Controller Area Network Bus Intrusion Detection.

Sensors (Basel, Switzerland)·2026
Same journal

Smart Optimization Method for Safety Signs in Innovative Manufacturing Environments Integrating Industrial Field IoT Sensors and Knowledge Graphs.

Sensors (Basel, Switzerland)·2026
See all related articles

Related Experiment Video

Updated: May 25, 2025

Use of Principal Components for Scaling Up Topographic Models to Map Soil Redistribution and Soil Organic Carbon
09:44

Use of Principal Components for Scaling Up Topographic Models to Map Soil Redistribution and Soil Organic Carbon

Published on: October 16, 2018

10.1K

From Data to Insights: Modeling Urban Land Surface Temperature Using Geospatial Analysis and Interpretable Machine

Nhat-Duc Hoang1,2, Van-Duc Tran2,3, Thanh-Canh Huynh1,2

  • 1Institute of Research and Development, Duy Tan University, Da Nang 550000, Vietnam.

Sensors (Basel, Switzerland)
|February 26, 2025
PubMed
Summary
This summary is machine-generated.

This study developed a machine learning model to predict land surface temperature (LST) in Da Nang, Vietnam. Urban density and greenspace density were found to be the most significant factors influencing LST.

Keywords:
Shapley additive explanationsbuilt environmentinterpretable machine learningland surface temperatureurban heat

More Related Videos

Exploring the Effects of Atmospheric Forcings on Evaporation: Experimental Integration of the Atmospheric Boundary Layer and Shallow Subsurface
13:27

Exploring the Effects of Atmospheric Forcings on Evaporation: Experimental Integration of the Atmospheric Boundary Layer and Shallow Subsurface

Published on: June 8, 2015

8.7K
Subsurface Defect Localization by Structured Heating Using Laser Projected Photothermal Thermography
11:34

Subsurface Defect Localization by Structured Heating Using Laser Projected Photothermal Thermography

Published on: May 15, 2017

11.1K

Related Experiment Videos

Last Updated: May 25, 2025

Use of Principal Components for Scaling Up Topographic Models to Map Soil Redistribution and Soil Organic Carbon
09:44

Use of Principal Components for Scaling Up Topographic Models to Map Soil Redistribution and Soil Organic Carbon

Published on: October 16, 2018

10.1K
Exploring the Effects of Atmospheric Forcings on Evaporation: Experimental Integration of the Atmospheric Boundary Layer and Shallow Subsurface
13:27

Exploring the Effects of Atmospheric Forcings on Evaporation: Experimental Integration of the Atmospheric Boundary Layer and Shallow Subsurface

Published on: June 8, 2015

8.7K
Subsurface Defect Localization by Structured Heating Using Laser Projected Photothermal Thermography
11:34

Subsurface Defect Localization by Structured Heating Using Laser Projected Photothermal Thermography

Published on: May 15, 2017

11.1K

Area of Science:

  • Environmental Science
  • Geospatial Analysis
  • Urban Planning

Background:

  • Urban heat islands significantly impact city environments.
  • Accurate modeling of land surface temperature (LST) is crucial for understanding urban heat stress.
  • Da Nang, Vietnam, faces increasing urban development and associated thermal challenges.

Purpose of the Study:

  • To develop and evaluate machine learning models for predicting urban land surface temperature (LST) in Da Nang.
  • To identify key factors influencing spatial LST variations.
  • To provide insights for sustainable urban planning and heat stress mitigation.

Main Methods:

  • Employed Light Gradient Boosting Machine (LightGBM), Support Vector Machine, Random Forest, and Deep Neural Network.
  • Utilized remote sensing data from 2014, 2019, and 2024 for model training and validation.
  • Applied Shapley Additive Explanations to interpret model results and identify influential factors.

Main Results:

  • LightGBM demonstrated superior performance compared to other benchmark machine learning methods.
  • Urban density and greenspace density were consistently identified as the most influential factors affecting LST.
  • Achieved high R-squared values (0.85, 0.92, 0.91) for 2014, 2019, and 2024, respectively.

Conclusions:

  • Machine learning, particularly LightGBM, effectively models spatial LST variations in urban areas.
  • Urban form characteristics, specifically density and green space, are critical drivers of LST.
  • Findings support evidence-based urban planning for mitigating heat stress and enhancing urban resilience.