Advances in geocomputation and geospatial artificial intelligence (GeoAI) for mapping

Song, Yongze; Kalácska, Margaret; Gašparović, Mateo; Yao, Jing; Najibi, Nasser

doi:10.1016/j.jag.2023.103300

Cited by 15 publications

(7 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Secondly, the present study was limited in that it explored only traditional classification algorithms (e.g., Maximum Likelihood, Minimum Distance). Although these algorithms achieved a high classification accuracy, another approach receiving a lot of attention in the literature is geospatial artificial intelligence (GeoAI; [59][60][61]). GeoAI has been found to achieve high accuracies in a number of studies (e.g., [46,59,62]) and is a powerful tool for exploring large datasets.…”

Section: Choice Of Methodologymentioning

confidence: 99%

“…Although these algorithms achieved a high classification accuracy, another approach receiving a lot of attention in the literature is geospatial artificial intelligence (GeoAI; [59][60][61]). GeoAI has been found to achieve high accuracies in a number of studies (e.g., [46,59,62]) and is a powerful tool for exploring large datasets. This field is rapidly expanding and encompasses both machine-learning (e.g., random forest, support vector machines) and deep-learning algorithms (e.g., convolutional neural networks).…”

Section: Choice Of Methodologymentioning

confidence: 99%

“…This field is rapidly expanding and encompasses both machine-learning (e.g., random forest, support vector machines) and deep-learning algorithms (e.g., convolutional neural networks). That said, in comparison to traditional algorithms, GeoAI is a novel and developing field [59]. AI methods are often less accessible than traditional techniques, particularly for UAV-based studies (although frameworks are starting to be developed, e.g., [63]).…”

Section: Choice Of Methodologymentioning

confidence: 99%

See 2 more Smart Citations

Species-Level Classification of Peatland Vegetation Using Ultra-High-Resolution UAV Imagery

Simpson,

Nichol,

Wade

et al. 2024

Drones

View full text Add to dashboard Cite

Peatland restoration projects are being employed worldwide as a form of climate change mitigation due to their potential for long-term carbon sequestration. Monitoring these environments (e.g., cover of keystone species) is therefore essential to evaluate success. However, existing studies have rarely examined peatland vegetation at fine scales due to its strong spatial heterogeneity and seasonal canopy development. The present study collected centimetre-scale multispectral Uncrewed Aerial Vehicle (UAV) imagery with a Parrot Sequoia camera (2.8 cm resolution; Parrot Drones SAS, Paris, France) in a temperate peatland over a complete growing season. Supervised classification algorithms were used to map the vegetation at the single-species level, and the Maximum Likelihood classifier was found to perform best at the site level (69% overall accuracy). The classification accuracy increased with the spatial resolution of the input data, and a large reduction in accuracy was observed when employing imagery of >11 cm resolution. Finally, the most accurate classifications were produced using imagery collected during the peak (July–August) or early growing season (start of May). These findings suggest that despite the strong heterogeneity of peatlands, these environments can be mapped at the species level using UAVs. Such an approach would benefit studies estimating peatland carbon emissions or using the cover of keystone species to evaluate restoration projects.

show abstract

Section: Choice Of Methodologymentioning

confidence: 99%

Section: Choice Of Methodologymentioning

confidence: 99%

Section: Choice Of Methodologymentioning

confidence: 99%

See 1 more Smart Citation

Species-Level Classification of Peatland Vegetation Using Ultra-High-Resolution UAV Imagery

Simpson,

Nichol,

Wade

et al. 2024

Drones

View full text Add to dashboard Cite

show abstract

“…The role of artificial intelligence technology in geographical design is mainly reflected in two aspects: (1) Using artificial intelligence algorithms and thinking to calculate and analyze the relevant data in geographical design research efficiently and accurately and mining knowledge and rules from it; (2) Aiming at complex and difficult problems in geospatial research, establish a spatial intelligence model to reveal the internal mechanism behind the phenomenon. Artificial intelligence applied to geographic design mainly refers to "weak artificial intelligence": execution ability is generally better than humans and can formulate and apply digital technology to achieve goals [8]. Its core lies in applying artificial intelligence technology to replace the work handled by the human brain in the past and improve the reliability, validity, and accuracy of geographical planning and design.…”

Section: Advances In Artificial Intelligence Technologies and The Int...mentioning

confidence: 99%

Advancing Urban Life: A Systematic Review of Emerging Technologies and Artificial Intelligence in Urban Design and Planning

He,

Chen

2024

Buildings

View full text Add to dashboard Cite

The advancement of cutting-edge technologies significantly transforms urban lifestyles and is indispensable in sustainable urban design and planning. This systematic review focuses on the critical role of innovative technologies and digitalization, particularly artificial intelligence (AI), in urban planning through geo-design, aiming to enhance urban life. It begins with exploring the importance of AI and digital tools in revolutionizing contemporary urban planning practices. Through the methodology based on the Systematic Reviews and Meta-Analyses (PRISMA) protocol, this review sifts through relevant literature over the past two decades by categorizing artificial intelligence technologies based on their functionalities. These technologies are examined for their utility in urban planning, environmental modeling, and infrastructure development, highlighting how they contribute to creating smarter and more livable cities. For instance, machine learning techniques like supervised learning excel in forecasting urban trends, whereas artificial neural networks and deep learning are superior in pattern recognition and vital for environmental modeling. This analysis, which refers to the comprehensive evaluation conducted in this Systematic Review, encompasses studies based on diverse data inputs and domains of application, revealing a trend toward leveraging AI for predictive analytics, decision-making improvements, and the automation of complex geospatial tasks in urban areas. The paper also addresses the challenges encountered, including data privacy, ethical issues, and the demand for cross-disciplinary knowledge. The concluding remarks emphasize the transformative potential of innovative technologies and digitalization in urban planning, advocating for their role in fostering better urban life. It also identifies future research avenues and development opportunities. In light of our review findings, this study concludes that AI technologies indeed hold transformative promise for the field of geo-design and urban planning. They have proven instrumental in advancing predictive analytics, refining decision-making, and streamlining complex geospatial tasks. The AI’s capacity to process expansive datasets and improve urban planning accuracy has facilitated more sustainable urban development and enhanced the resilience of urban environments.

show abstract

“…Today, GIS competency is necessary for public health departments across the nation at local, county, and state levels (31). Its use will continue to evolve, and we look forward to applying it to additional chronic disease issues.…”

mentioning

confidence: 99%

Chronic Disease Mapping, an Important Strategy and Tool for Health Promotion

Hacker,

Kaufmann

2024

Prev. Chronic Dis.

View full text Add to dashboard Cite

Advances in geocomputation and geospatial artificial intelligence (GeoAI) for mapping

Cited by 15 publications

References 20 publications

Species-Level Classification of Peatland Vegetation Using Ultra-High-Resolution UAV Imagery

Species-Level Classification of Peatland Vegetation Using Ultra-High-Resolution UAV Imagery

Advancing Urban Life: A Systematic Review of Emerging Technologies and Artificial Intelligence in Urban Design and Planning

Chronic Disease Mapping, an Important Strategy and Tool for Health Promotion

Contact Info

Product

Resources

About