Key issues in rigorous accuracy assessment of land cover products

Stehman, Stephen V.; Foody, Giles M.

doi:10.1016/j.rse.2019.05.018

Cited by 395 publications

(368 citation statements)

References 183 publications

(252 reference statements)

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“…Training reference data are expert-defined exemplar observations used to assess TD errors during or after data creation. Map reference data are independent observations used to assess final map accuracy; while these may be collected using many of the same procedures as the other three datasets [57], they have more stringent design protocols and can only be used to assess the final map product, rather than used iteratively in model or map improvement [57]. Map reference data are often referred to as the test set in ML literature [77], but we use the former term to align with the terminology commonly used by the EO community.…”

Section: Characterizing Training Data Errormentioning

confidence: 99%

“…Map accuracy assessment practices and standards are well-established in the EO literature [39,40,45,57,78]. We briefly review these procedures here because they are essential for quantifying how TD error impacts map accuracy.…”

Section: Map Accuracy Assessment Proceduresmentioning

confidence: 99%

“…Previous research shows that the impact of TD error can be substantial but varied, suggesting that a more comprehensive approach to this issue is warranted. Furthermore, while TD and map reference data are often collected using the same approaches [55][56][57] and often subject to the same errors, the existing procedures to minimize and account for map reference errors [34,38,46,47] are not necessarily relevant for quantifying the impacts of TD error. The problems associated with TD error can be summarized as follows:1.The "big data" era vastly increases the demand for TD.…”

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confidence: 99%

“…TD errors may propagate to downstream products in surprising and potentially harmful ways (e.g., leading to bad decisions) and can occur without the map producer and/or map user's knowledge. This problem is particularly relevant in the common case where TD and reference data are collected using the same methods, and/or in cases where map reference data error is not known or accounted for, which is still common [57].…”

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confidence: 99%

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Accounting for Training Data Error in Machine Learning Applied to Earth Observations

et al. 2020

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Remote sensing, or Earth Observation (EO), is increasingly used to understand Earth system dynamics and create continuous and categorical maps of biophysical properties and land cover, especially based on recent advances in machine learning (ML). ML models typically require large, spatially explicit training datasets to make accurate predictions. Training data (TD) are typically generated by digitizing polygons on high spatial-resolution imagery, by collecting in situ data, or by using pre-existing datasets. TD are often assumed to accurately represent the truth, but in practice almost always have error, stemming from (1) sample design, and (2) sample collection errors. The latter is particularly relevant for image-interpreted TD, an increasingly commonly used method due to its practicality and the increasing training sample size requirements of modern ML algorithms. TD errors can cause substantial errors in the maps created using ML algorithms, which may impact map use and interpretation. Despite these potential errors and their real-world consequences for map-based decisions, TD error is often not accounted for or reported in EO research. Here we review the current practices for collecting and handling TD. We identify the sources of TD error, and illustrate their impacts using several case studies representing different EO applications (infrastructure mapping, global surface flux estimates, and agricultural monitoring), and provide guidelines for minimizing and accounting for TD errors. To harmonize terminology, we distinguish TD from three other classes of data that should be used to create and assess ML models: training reference data, used to assess the quality of TD during data generation; validation data, used to iteratively improve models; and map reference data, used only for final accuracy assessment. We focus primarily on TD, but our advice is generally applicable to all four classes, and we ground our review in established best practices for map accuracy assessment literature. EO researchers should start by determining the tolerable levels of map error and appropriate error metrics. Next, TD error should be minimized during sample design by choosing a representative spatio-temporal collection strategy, by using spatially and temporally relevant imagery and ancillary data sources during TD creation, and by selecting a set of legend definitions supported by the data. Furthermore, TD error can be minimized during the collection of individual samples by using consensus-based collection strategies, by directly comparing interpreted training observations against expert-generated training reference data to derive TD error metrics, and by providing image interpreters with thorough application-specific training. We strongly advise that TD error is incorporated in model outputs, either directly in bias and variance estimates or, at a minimum, by documenting the sources and implications of error. TD should be fully documented and made available via an open TD repository, allowing others to replicate and assess its use. To guide researchers in this process, we propose three tiers of TD error accounting standards. Finally, we advise researchers to clearly communicate the magnitude and impacts of TD error on map outputs, with specific consideration given to the likely map audience.

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Section: Characterizing Training Data Errormentioning

confidence: 99%

Section: Map Accuracy Assessment Proceduresmentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

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Accounting for Training Data Error in Machine Learning Applied to Earth Observations

et al. 2020

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“…In fact, the process of determining the so-called "ground truth" is seldom discussed in the literature and is often considered a straightforward -yet costly-task. Nonetheless, generating authoritative reference data sets remains a major challenge in accuracy assessment and it merits greater consideration in accuracy assessment (Stehman and Foody, 2019). Errors can indeed alter the process of generating reference data and even a small amount of errors can propagate and significantly impact the accuracy assessment (Woodcock and Gopal, 2000;Foody, 2009Foody, , 2013.…”

Section: Introductionmentioning

confidence: 99%

How Response Designs and Class Proportions Affect the Accuracy of Validation Data

2020

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Reference data collected to validate land cover maps are generally considered free of errors. In practice, however, they contain errors despite all efforts to minimise them. These errors then propagate up to the accuracy assessment stage and impact the validation results. For photo-interpreted reference data, the three most widely studied sources of error are systematic incorrect labelling, vigilance drops, and demographic factors. How internal estimation errors, i.e., errors intrinsic to the response design, affect the accuracy of reference data is far less understood. In this paper, we analysed the impact of estimation errors for two types of legends (binary and multi-class) as well as for two common response designs (point-based and partition-based) with a range of sub-sample sizes. We showed that the accuracy of response designs depends on the class proportions within the sampling units, with complex landscapes being more prone to errors. As a result, response designs where the number of sub-samples are fixed are inefficient, and the labels of reference data sets have inconsistent confidence levels. In order to control estimation errors, to guarantee high accuracy standards of validation data, and to minimise data collection efforts, we proposed to rely on confidence intervals of the photo-interpreted data to define how many sub-samples should be labelled. In practice, sub-samples are iteratively selected and labelled until the estimated class proportions reach the desired level of confidence. As a result, less effort is spent on labelling obvious cases and the spared effort can be allocated to more complex cases, thus leading to an increased reliability of reference data and in turn better accuracy assessment. Across our study site, we demonstrated that such an approach could reduce the labelling effort by 50% to 75%, with greater gains in homogeneous landscapes. We contend that adopting this optimisation approach will not only increase the efficiency of reference data collection but will also help deliver reliable accuracy estimates to the user community.

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Exploiting Multitemporal Multispectral High‐resolution Satellite Data toward Annual Land Cover and Crop Type Mapping

KARAKIZI,

KARANTZALOS,

KANDYLAKIS

2024

Multitemporal Earth Observation Image Analysis

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Key issues in rigorous accuracy assessment of land cover products

Cited by 395 publications

References 183 publications

Accounting for Training Data Error in Machine Learning Applied to Earth Observations

Accounting for Training Data Error in Machine Learning Applied to Earth Observations

How Response Designs and Class Proportions Affect the Accuracy of Validation Data

Exploiting Multitemporal Multispectral High‐resolution Satellite Data toward Annual Land Cover and Crop Type Mapping

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