Mapping built environments from UAV imagery: a tutorial on mixed methods of deep learning and GIS

Hong, Xin; Sheridan, Scott C.; Liu, Dong

doi:10.1007/s43762-022-00039-w

Cited by 5 publications

(1 citation statement)

References 43 publications

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“…The green space percentage, green space/building area ratio, tree density, shade coverage, and other indices are commonly used [126][127][128]. Hong et al [129] used GIS to extract the green space density and pavement from visible light images using deep learning and image processing techniques. Although visible light remote sensing images are macroscopic and fast, they only capture one perspective, lack elevation spatial information, and are unable to capture urban details at the street level.…”

Section: Remote Sensing Image Measurementmentioning

confidence: 99%

Computer Vision Technology for Monitoring of Indoor and Outdoor Environments and HVAC Equipment: A Review

Yang

Zhu

et al. 2023

Sensors

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Artificial intelligence technologies such as computer vision (CV), machine learning, Internet of Things (IoT), and robotics have advanced rapidly in recent years. The new technologies provide non-contact measurements in three areas: indoor environmental monitoring, outdoor environ-mental monitoring, and equipment monitoring. This paper summarizes the specific applications of non-contact measurement based on infrared images and visible images in the areas of personnel skin temperature, position posture, the urban physical environment, building construction safety, and equipment operation status. At the same time, the challenges and opportunities associated with the application of CV technology are anticipated.

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Section: Remote Sensing Image Measurementmentioning

confidence: 99%

Computer Vision Technology for Monitoring of Indoor and Outdoor Environments and HVAC Equipment: A Review

Yang

Zhu

et al. 2023

Sensors

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An Analysis of the Prevalence and Trends in Drug-Related Lyrics on Twitter (X): Quantitative Approach

Luo,

Jin,

Kenne

et al. 2024

JMIR Form Res

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Background The pervasiveness of drug culture has become evident in popular music and social media. Previous research has examined drug abuse content in both social media and popular music; however, to our knowledge, the intersection of drug abuse content in these 2 domains has not been explored. To address the ongoing drug epidemic, we analyzed drug-related content on Twitter (subsequently rebranded X), with a specific focus on lyrics. Our study provides a novel finding on the prevalence of drug abuse by defining a new subcategory of X content: “tweets that reference established drug lyrics.” Objective We aim to investigate drug trends in popular music on X, identify and classify popular drugs, and analyze related artists’ gender, genre, and popularity. Based on the collected data, our goal is to create a prediction model for future drug trends and gain a deeper understanding of the characteristics of users who cite drug lyrics on X. Methods X data were collected from 2015 to 2017 through the X streaming application programming interface (API). Drug lyrics were obtained from the Genius lyrics database using the Genius API based on drug keywords. The Smith-Waterman text-matching algorithm is used to detect the drug lyrics in posts. We identified famous drugs in lyrics that were posted. Consequently, the analysis was extended to related artists, songs, genres, and popularity on X. The frequency of drug-related lyrics on X was aggregated into a time-series, which was then used to create prediction models using linear regression, Facebook Prophet, and NIXTLA TimeGPT-1. In addition, we analyzed the number of followers of users posting drug-related lyrics to explore user characteristics. Results We analyzed over 1.97 billion publicly available posts from 2015 to 2017, identifying more than 157 million that matched drug-related keywords. Of these, 150,746 posts referenced drug-related lyrics. Cannabinoids, opioids, stimulants, and hallucinogens were the most cited drugs in lyrics on X. Rap and hip-hop dominated, with 91.98% of drug-related lyrics from these genres and 84.21% performed by male artists. Predictions from all 3 models, linear regression, Facebook Prophet, and NIXTLA TimeGPT-1, indicate a slight decline in the prevalence of drug-related lyrics on X over time. Conclusions Our study revealed 2 significant findings. First, we identified a previously unexamined subset of drug-related content on X: drug lyrics, which could play a critical role in models predicting the surge in drug-related incidents. Second, we demonstrated the use of cutting-edge time-series forecasting tools, including Facebook Prophet and NIXTLA TimeGPT-1, in accurately predicting these trends. These insights contribute to our understanding of how social media shapes public behavior and sentiment toward drug use.

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Geospatial Modeling of Deep Neural Visual Features for Predicting Obesity Prevalence in Missouri: Quantitative Study

Dahu,

Khan,

Toubal

et al. 2024

JMIR AI

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Background The global obesity epidemic demands innovative approaches to understand its complex environmental and social determinants. Spatial technologies, such as geographic information systems, remote sensing, and spatial machine learning, offer new insights into this health issue. This study uses deep learning and spatial modeling to predict obesity rates for census tracts in Missouri. Objective This study aims to develop a scalable method for predicting obesity prevalence using deep convolutional neural networks applied to satellite imagery and geospatial analysis, focusing on 1052 census tracts in Missouri. Methods Our analysis followed 3 steps. First, Sentinel-2 satellite images were processed using the Residual Network-50 model to extract environmental features from 63,592 image chips (224×224 pixels). Second, these features were merged with obesity rate data from the Centers for Disease Control and Prevention for Missouri census tracts. Third, a spatial lag model was used to predict obesity rates and analyze the association between deep neural visual features and obesity prevalence. Spatial autocorrelation was used to identify clusters of obesity rates. Results Substantial spatial clustering of obesity rates was found across Missouri, with a Moran I value of 0.68, indicating similar obesity rates among neighboring census tracts. The spatial lag model demonstrated strong predictive performance, with an R2 of 0.93 and a spatial pseudo R2 of 0.92, explaining 93% of the variation in obesity rates. Local indicators from a spatial association analysis revealed regions with distinct high and low clusters of obesity, which were visualized through choropleth maps. Conclusions This study highlights the effectiveness of integrating deep convolutional neural networks and spatial modeling to predict obesity prevalence based on environmental features from satellite imagery. The model’s high accuracy and ability to capture spatial patterns offer valuable insights for public health interventions. Future work should expand the geographical scope and include socioeconomic data to further refine the model for broader applications in obesity research.

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Mapping built environments from UAV imagery: a tutorial on mixed methods of deep learning and GIS

Cited by 5 publications

References 43 publications

Computer Vision Technology for Monitoring of Indoor and Outdoor Environments and HVAC Equipment: A Review

Computer Vision Technology for Monitoring of Indoor and Outdoor Environments and HVAC Equipment: A Review

An Analysis of the Prevalence and Trends in Drug-Related Lyrics on Twitter (X): Quantitative Approach

Geospatial Modeling of Deep Neural Visual Features for Predicting Obesity Prevalence in Missouri: Quantitative Study

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