Active Learning for Monitoring Network Optimization

Tuia, Devis; Pozdnoukhov, Alexei; Foresti, Loris; Kanevski, Mikhaïl

doi:10.1002/9781118441862.ch13

Cited by 8 publications

(12 citation statements)

References 48 publications

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“…A special attention will be paid to methods involving information on both the support of the measures and the measures (e.g. methods based on conditional stochastic simulations in geostatistics and active learning methods using machine learning algorithms [45]). 20…”

Section: Resultsmentioning

confidence: 99%

The multipoint Morisita index for the analysis of spatial patterns

Golay

Kanevski

Orozco

et al. 2014

Physica A: Statistical Mechanics and its Applications

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In many fields, the spatial clustering of sampled data points has significant consequences. Therefore, several indices have been proposed to assess the degree of clustering affecting datasets (e.g. the Morisita index, Ripley's K-function and Rényi's generalized entropy). The classical Morisita index measures how many times it is more likely to select two sampled points from the same quadrats (the data set is covered by a regular grid of changing size) than it would be in the case of a random distribution generated from a Poisson process. The multipoint version takes into account m points with m ≥ 2. The present research deals with a new development of the multipoint Morisita index (m-Morisita) for (1) the characterization of environmental monitoring network clustering and for (2) the detection of structures in monitored phenomena. From a theoretical perspective, a connection between the m-Morisita index and multifractality has also been found and highlighted on a mathematical multifractal set.

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Section: Resultsmentioning

confidence: 99%

The multipoint Morisita index for the analysis of spatial patterns

Golay

Kanevski

Orozco

et al. 2014

Physica A: Statistical Mechanics and its Applications

Self Cite

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“…covariate) space remains important for all ML algorithms since they all link the covariates and the sample values in a non-linear way, but that additional considerations might outweigh or overtake this uniform spread. An example of optimal design is given by the studies of Pozdnoukhov & Kanevski (2006) and Tuia et al (2013)…”

Section: Challenges and Opportunitiesmentioning

confidence: 99%

“…In the first study, the selected sampling units are the most beneficial for the algorithm, avoiding mis-classification between temperature below or above 20Cs (categorical mapping) by becoming support vectors. InTuia et al (2013), a similar methodology is adopted and tested in three case studies to subsample an existing sample for quantitative mapping, to add optimally new sampling units in a continuous map or to define suitable areas for sampling. In all case studies, the authors obtained a design optimal for the purpose…”

mentioning

confidence: 99%

Machine learning for digital soil mapping: applications, challenges and suggested solutions

Wadoux¹,

Minasny²,

McBratney³

2020

Preprint

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The uptake of machine learning (ML) algorithms in digital soil mapping (DSM) is transforming the way soil scientists produce their maps. Machine learning is currently applied to mapping soil properties or classes much in the same way as other unrelated fields of science. Mapping of soil, however, has unique aspects which require adaptations of the ML algorithms. These features are for example, but not limited to, the inclusion of pedological knowledge into the ML algorithm, the accounting of spatial structure present in the soil data, or the desire to increase our scientific understanding of the distribution and genesis of soil from a calibrated ML model. Tackling these challenges is critical for machine learning to gain credibility and scientific consistency in soil science. In this article, we review the current applications of machine learning in digital soil mapping and suggest improvements. We found a growing interest of the use of ML in DSM. Most studies focus on obtaining accurate maps and disregard the characteristics of soil data, such as spatial autocorrelation. Only a few studies account for existing soil knowledge or quantify the uncertainty of the predicted maps. We then discuss the challenges related to the application of ML for soil mapping and offer solutions from existing studies in the natural sciences. The challenges are organized as follows: sampling, resampling, accounting for the spatial information, multivariate mapping, uncertainty analysis, validation, integration of pedological knowledge and, interpretation of the models. We conclude that for future developments, machine learning should incorporate three core elements: plausibility, interpretability, and explainability, which will trigger soil scientists to move beyond model prediction and towards explanation of soil processes.

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“…Hu and Wang (2011) used Particle Swarm Optimisation combined with Monte Carlo simulations to find optimal locations to estimate the global mean. Tuia et al (2013) employed Active Learning, that searches for the samples that are best suited to improve a model, to determine parameters for a kernel model for mapping.…”

Section: Optimisation Algorithms For Spatial Samplingmentioning

confidence: 99%