Explain It to Me – Facing Remote Sensing Challenges in the Bio- And Geosciences With Explainable Machine Learning

Roscher, Ribana; Bohn, Bastian; Duarte, Marco F.; Garcke, Jochen

doi:10.5194/isprs-annals-v-3-2020-817-2020

Cited by 24 publications

(9 citation statements)

References 56 publications

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“…Specifically, such methods have been investigated in the fields of healthcare (e.g., [13,[22][23][24][25]), finance (e.g., [26][27][28]), and law (e.g., [26,29,30]). Explainable ML has seen less application and investigation in geospatial science and geoscience [31][32][33]. For geohazard mapping and modeling specifically, we argue that there is value in interpretable results.…”

Section: Background 21 Explainable Machine Learningmentioning

confidence: 99%

Explainable Boosting Machines for Slope Failure Spatial Predictive Modeling

Maxwell

Sharma²,

Donaldson³

2021

Remote Sensing

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Machine learning (ML) methods, such as artificial neural networks (ANN), k-nearest neighbors (kNN), random forests (RF), support vector machines (SVM), and boosted decision trees (DTs), may offer stronger predictive performance than more traditional, parametric methods, such as linear regression, multiple linear regression, and logistic regression (LR), for specific mapping and modeling tasks. However, this increased performance is often accompanied by increased model complexity and decreased interpretability, resulting in critiques of their “black box” nature, which highlights the need for algorithms that can offer both strong predictive performance and interpretability. This is especially true when the global model and predictions for specific data points need to be explainable in order for the model to be of use. Explainable boosting machines (EBM), an augmentation and refinement of generalize additive models (GAMs), has been proposed as an empirical modeling method that offers both interpretable results and strong predictive performance. The trained model can be graphically summarized as a set of functions relating each predictor variable to the dependent variable along with heat maps representing interactions between selected pairs of predictor variables. In this study, we assess EBMs for predicting the likelihood or probability of slope failure occurrence based on digital terrain characteristics in four separate Major Land Resource Areas (MLRAs) in the state of West Virginia, USA and compare the results to those obtained with LR, kNN, RF, and SVM. EBM provided predictive accuracies comparable to RF and SVM and better than LR and kNN. The generated functions and visualizations for each predictor variable and included interactions between pairs of predictor variables, estimation of variable importance based on average mean absolute scores, and provided scores for each predictor variable for new predictions add interpretability, but additional work is needed to quantify how these outputs may be impacted by variable correlation, inclusion of interaction terms, and large feature spaces. Further exploration of EBM is merited for geohazard mapping and modeling in particular and spatial predictive mapping and modeling in general, especially when the value or use of the resulting predictions would be greatly enhanced by improved interpretability globally and availability of prediction explanations at each cell or aggregating unit within the mapped or modeled extent.

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Section: Background 21 Explainable Machine Learningmentioning

confidence: 99%

Explainable Boosting Machines for Slope Failure Spatial Predictive Modeling

Maxwell

Sharma²,

Donaldson³

2021

Remote Sensing

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“…This is an advantage over many classical approaches, which often estimate only individual parameters. Second, images are directly assessible and increase the reliability of the results because they can be visually interpreted by farmers, which is in line with the goals of explainable machine learning [13]. A general advantage of GANs for temporal prediction is that they are trained using time-series that can be acquired by regular measurements with satellites, UAVs, or ground robots and do not require time-consuming annotations of images.…”

Section: Introductionmentioning

confidence: 76%

Temporal Prediction and Evaluation of Brassica Growth in the Field using Conditional Generative Adversarial Networks

Drees,

Junker-Frohn,

Kierdorf

et al. 2021

Preprint

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Farmers frequently assess plant growth and performance as basis for making decisions when to take action in the field, such as fertilization, weed control, or harvesting. The prediction of plant growth is a major challenge, as it is affected by numerous and highly variable environmental factors. This paper proposes a novel monitoring approach that comprises high-throughput imaging sensor measurements and their automatic analysis to predict future plant growth. Our approach's core is a novel machine learning-based growth model based on conditional generative adversarial networks, which is able to predict the future appearance of individual plants. In experiments with RGB time-series images of laboratory-grown Arabidopsis thaliana and field-grown cauliflower plants, we show that our approach produces realistic, reliable, and reasonable images of future growth stages. The automatic interpretation of the generated images through neural network-based instance segmentation allows the derivation of various phenotypic traits that describe plant growth.

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“…These three properties are found in different approaches with different levels. An overview in the context of remote sensing can be found, for example, in (Roscher et al, 2020b). Although a model may have been learned using various ML techniques, the goals of explainable ML are mostly mentioned in the context of deep neural networks, whose decision-making is often difficult for humans to comprehend due to their complexity.…”

Section: Related Workmentioning

confidence: 99%

“…Little work has been done so far in remote sensing, particularly with satellite images, to understand better what has been learned. (Roscher et al, 2020b) discuss first works in this direction and show that explainability is often used to align the models with existing knowledge, for example, to improve models and to correct obvious flaws in case of wrong decisions. To this point, explainable ML has been used less to uncover previously unknown patterns and to derive novel scientific insights.…”

Section: Introductionmentioning

confidence: 99%

Jungle-Net: Using Explainable Machine Learning to Gain New Insights Into the Appearance of Wilderness in Satellite Imagery

Stomberg

Weber

Schmitt

et al. 2021

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

Self Cite

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Abstract. Explainable machine learning has recently gained attention due to its contribution to understanding how a model works and why certain decisions are made. A so far less targeted goal, especially in remote sensing, is the derivation of new knowledge and scientific insights from observational data. In our paper, we propose an explainable machine learning approach to address the challenge that certain land cover classes such as wilderness are not well-defined in satellite imagery and can only be used with vague labels for mapping. Our approach consists of a combined U-Net and ResNet-18 that can perform scene classification while providing at the same time interpretable information with which we can derive new insights about classes. We show that our methodology allows us to deepen our understanding of what makes nature wild by automatically identifying simple concepts such as wasteland that semantically describes wilderness. It further quantifies a class’s sensitivity with respect to a concept and uses it as an indicator for how well a concept describes the class.

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Explain It to Me – Facing Remote Sensing Challenges in the Bio- And Geosciences With Explainable Machine Learning

Cited by 24 publications

References 56 publications

Explainable Boosting Machines for Slope Failure Spatial Predictive Modeling

Explainable Boosting Machines for Slope Failure Spatial Predictive Modeling

Temporal Prediction and Evaluation of Brassica Growth in the Field using Conditional Generative Adversarial Networks

Jungle-Net: Using Explainable Machine Learning to Gain New Insights Into the Appearance of Wilderness in Satellite Imagery

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