ReviewNature communications2024
Challenges in data-driven geospatial modeling for environmental research and practice.
Review in Nature communications, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
15 citing papers in PubMed.
- Accuracy of seven GPS-based geolocation techniquesBMJ open · 2026Article
- A Tutorial on Best Practices and Pitfalls in Applying Machine Learning to Environmental Research.ACS environmental Au · 2026Article
- Comparative evaluation of neural networks and ensemble models for vegetation trend prediction in a semiarid mountain ecosystem, Saudi Arabia.Scientific reports · 2026Article
- Altitude-resolved prediction of roadside air pollution using UAV measurements and machine learning.Scientific reports · 2026Article
- Bringing cross-validation into the real world to evaluate transferability of satellite-based vegetation models.Scientific reports · 2026Article
- Developing an Index to Measure Structural Racism: Methodological Process, Challenges, and Considerations.International journal of environmental research and public health · 2026Article
- Preventing spread of the invasive spotted lanternfly via texture-based automated egg detection.Frontiers in insect science · 2026Article
- Global high-resolution ultrafine particle number concentrations through data fusion with machine learning.Scientific data · 2025Article
- Kriging to Kolmogorov-Arnold Network model accelerated discovery of oxygen control strategy in lead-based fast reactors.Nature communications · 2025Article
- Unified artificial intelligence framework for modeling pollution dynamics and sustainable remediation in environmental chemistry.Scientific reports · 2025Article
- [Environmental justice-challenges for spatial analysis and monitoring].Bundesgesundheitsblatt, Gesundheitsforschung, Gesundheitsschutz · 2025Article
- A global database of soil microbial phospholipid fatty acids and enzyme activities.Scientific data · 2025Article
- Identification of flood vulnerability areas using analytical hierarchy process techniques in the Wuseta watershed, Upper Blue Nile Basin, Ethiopia.Scientific reports · 2025Article
- Synergistic use of satellite, legacy, andFrontiers in plant science · 2025Article
- Artificial intelligence in maternal and child health: Current applications, translational gaps, and future research priorities.Women's health (London, England)Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Machine learning-based geospatial applications offer unique opportunities for environmental monitoring due to domains and scales adaptability and computational efficiency. However, the specificity of environmental data introduces biases in straightforward implementations. We identify a streamlined pipeline to enhance model accuracy, addressing issues like imbalanced data, spatial autocorrelation, prediction errors, and the nuances of model generalization and uncertainty estimation. We examine tools and techniques for overcoming these obstacles and provide insights into future geospatial AI developments. A big picture of the field is completed from advances in data processing in general, including the demands of industry-related solutions relevant to outcomes of applied sciences.
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.