Machine Learning Applications for Earth Observation

Lary, David J.; Zewdie, Gebreab K.; Liu, Xun; Wu, Daji; Levetin, Estelle; Allee, Rebecca J.; Malakar, N. K.; Walker, Annette L.; Mussa, Hamse Y.; Mannino, Antonio; Aurin, Dirk

doi:10.1007/978-3-319-65633-5_8

Cited by 60 publications

(32 citation statements)

References 72 publications

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“…Machine learning provides an objective set of tools for automating discovery. It is therefore not surprising that machine learning is currently revolutionizing many areas of science, technology, business, and medicine [25,26].…”

Section: Machine Learningmentioning

confidence: 99%

“…Some of the commonly used regression algorithms include neural networks [57][58][59][60][61][62], support vector machines [63][64][65][66][67], decision trees [68], and ensembles of trees such as random forests [69][70][71]. Previously we used a similar approach to cross-calibrate satellite instruments [19,[25][26][27][28]. Recently other studies also used machine learning to calibrate low cost sensors [72,73].…”

Section: Machine Learningmentioning

confidence: 99%

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Using Machine Learning for the Calibration of Airborne Particulate Sensors

Wijeratne

Kiv

Aker

et al. 2019

Sensors

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Airborne particulates are of particular significance for their human health impacts and their roles in both atmospheric radiative transfer and atmospheric chemistry. Observations of airborne particulates are typically made by environmental agencies using rather expensive instruments. Due to the expense of the instruments usually used by environment agencies, the number of sensors that can be deployed is limited. In this study we show that machine learning can be used to effectively calibrate lower cost optical particle counters. For this calibration it is critical that measurements of the atmospheric pressure, humidity, and temperature are also made.

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Section: Machine Learningmentioning

confidence: 99%

Section: Machine Learningmentioning

confidence: 99%

Using Machine Learning for the Calibration of Airborne Particulate Sensors

Wijeratne

Kiv

Aker

et al. 2019

Sensors

Self Cite

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“…Similarly, there is a large and dynamic network of observation technologies, and institutions and agencies that also benefit from, and drive, advances and disruptions. Examples of disruptive technological advances in the field of ocean observation and more widely include NOAA's Big Data Project 2 ; EO Data Cubes and Analysis Ready Datasets (Giuliani et al, 2017;Nativi et al, 2017), crowd-sourcing and citizen science (Mazumdar et al, 2017;Brovelli et al, 2018;O'Sullivan et al, 2018) and deep learning and artificial intelligence (Lary et al, 2018). Related to this is the way that Big Data has changed the paradigm when it comes to the availability and exploitation of ocean observing data.…”

Section: The Need For Innovation and Usable Informationmentioning

confidence: 99%

The Role of Stakeholders in Creating Societal Value From Coastal and Ocean Observations

et al. 2019

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The importance of stakeholder engagement in ocean observation and in particular the realization of economic and societal benefits is discussed, introducing a number of overarching principles such as the convergence on common goals, effective communication, co-production of information and knowledge and the need for innovation. A series of case studies examine the role of coordinating frameworks such as the United States' Interagency Ocean Observing System (IOOS R), and the European Ocean Observing System (EOOS), public-private partnerships such as Project Azul and the Coastal Data Information Program (CDIP) and finally the role of the "third" or voluntary sector. The paper explores the value that stakeholder engagement can bring as well as making recommendations for the future.

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“…These breakthroughs are facilitated by several technological advances, particularly the increasing availability of moderate (5-30 m), high-resolution (1-5m, HR), and very high resolution (<1 m, VHR) imagery, as well as new machine-learning (ML) algorithms that frequently require large, high quality training datasets [19][20][21][22][23][24]. Large training datasets have been necessary for decades in the production of continental and global maps [1,2,25,26].…”

Section: Introductionmentioning

confidence: 99%

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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Machine Learning Applications for Earth Observation

Cited by 60 publications

References 72 publications

Using Machine Learning for the Calibration of Airborne Particulate Sensors

Using Machine Learning for the Calibration of Airborne Particulate Sensors

The Role of Stakeholders in Creating Societal Value From Coastal and Ocean Observations

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

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